intro-to-scikit-learn.ipynb

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   "source": [
    "# Introduction to Scikit-Learn (sklearn)\n",
    "\n",
    "This notebook demonstrates some of the most useful functions of the beautiful Sckit-learn library.\n",
    "\n",
    "What we're going to cover:\n",
    "\n",
    "0. An end-to-end scikit-learn workflow\n",
    "1. Getting the data ready\n",
    "2. choose the right estimator/algorithm for our program\n",
    "3. fit the model/algorithm and use it to make predictions on our data\n",
    "4. evaluating a model\n",
    "5. improve a model\n",
    "6. save and load a trained model\n",
    "7. put it all together"
   ]
  },
  {
   "cell_type": "code",
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   "id": "0800e2ad-63d6-4af0-ac7d-55e2e65fdaac",
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   "outputs": [],
   "source": [
    "#standard imports\n",
    "import numpy as np\n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc6ef340-2035-4361-aae5-aef942396207",
   "metadata": {},
   "source": [
    "## 0.An end-to-end scikit-learn workflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c02694fc-7b28-4450-8bfa-a22d8cb14893",
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       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "0     63    1   3       145   233    1        0      150      0      2.3   \n",
       "1     37    1   2       130   250    0        1      187      0      3.5   \n",
       "2     41    0   1       130   204    0        0      172      0      1.4   \n",
       "3     56    1   1       120   236    0        1      178      0      0.8   \n",
       "4     57    0   0       120   354    0        1      163      1      0.6   \n",
       "..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
       "298   57    0   0       140   241    0        1      123      1      0.2   \n",
       "299   45    1   3       110   264    0        1      132      0      1.2   \n",
       "300   68    1   0       144   193    1        1      141      0      3.4   \n",
       "301   57    1   0       130   131    0        1      115      1      1.2   \n",
       "302   57    0   1       130   236    0        0      174      0      0.0   \n",
       "\n",
       "     slope  ca  thal  target  \n",
       "0        0   0     1       1  \n",
       "1        0   0     2       1  \n",
       "2        2   0     2       1  \n",
       "3        2   0     2       1  \n",
       "4        2   0     2       1  \n",
       "..     ...  ..   ...     ...  \n",
       "298      1   0     3       0  \n",
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       "300      1   2     3       0  \n",
       "301      1   1     3       0  \n",
       "302      1   1     2       0  \n",
       "\n",
       "[303 rows x 14 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#1. get te data ready\n",
    "import pandas as pd\n",
    "heart_disease=pd.read_csv(\"data/heart-disease.csv\")\n",
    "heart_disease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a8a71a2d-b9e9-4704-97bf-9156d5c01fc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create X (features matrix)\n",
    "X=heart_disease.drop(\"target\",axis=1)\n",
    "\n",
    "#create y (labels)\n",
    "y=heart_disease[\"target\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f09910bf-a9f2-44fa-995f-3e2a36163083",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\") # can change it to default for the warnings to show "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0a41c39d-e300-4768-a35c-ab98dbe6e142",
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    {
     "data": {
      "text/plain": [
       "{'bootstrap': True,\n",
       " 'ccp_alpha': 0.0,\n",
       " 'class_weight': None,\n",
       " 'criterion': 'gini',\n",
       " 'max_depth': None,\n",
       " 'max_features': 'sqrt',\n",
       " 'max_leaf_nodes': None,\n",
       " 'max_samples': None,\n",
       " 'min_impurity_decrease': 0.0,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 2,\n",
       " 'min_weight_fraction_leaf': 0.0,\n",
       " 'n_estimators': 100,\n",
       " 'n_jobs': None,\n",
       " 'oob_score': False,\n",
       " 'random_state': None,\n",
       " 'verbose': 0,\n",
       " 'warm_start': False}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2. choose the right model and hyperparameters\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "clf = RandomForestClassifier()\n",
    "\n",
    "#we'll keep the default hyperparameters\n",
    "clf.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b1687010-2281-473f-9631-5075ebc81f81",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3. fit the model to the data\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1db0094f-a2cc-415b-a958-49c7827c7296",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "System:\n",
      "    python: 3.11.7 | packaged by Anaconda, Inc. | (main, Dec 15 2023, 18:05:47) [MSC v.1916 64 bit (AMD64)]\n",
      "executable: C:\\Users\\farhe\\codesss\\miniconda_projects\\env\\python.exe\n",
      "   machine: Windows-10-10.0.22631-SP0\n",
      "\n",
      "Python dependencies:\n",
      "      sklearn: 1.2.2\n",
      "          pip: 23.3.1\n",
      "   setuptools: 68.2.2\n",
      "        numpy: 1.26.3\n",
      "        scipy: 1.11.4\n",
      "       Cython: None\n",
      "       pandas: 2.1.4\n",
      "   matplotlib: 3.8.0\n",
      "       joblib: 1.2.0\n",
      "threadpoolctl: 2.2.0\n",
      "\n",
      "Built with OpenMP: True\n",
      "\n",
      "threadpoolctl info:\n",
      "       filepath: C:\\Users\\farhe\\codesss\\miniconda_projects\\env\\Library\\bin\\mkl_rt.2.dll\n",
      "         prefix: mkl_rt\n",
      "       user_api: blas\n",
      "   internal_api: mkl\n",
      "        version: 2023.1-Product\n",
      "    num_threads: 10\n",
      "threading_layer: intel\n",
      "\n",
      "       filepath: C:\\Users\\farhe\\codesss\\miniconda_projects\\env\\vcomp140.dll\n",
      "         prefix: vcomp\n",
      "       user_api: openmp\n",
      "   internal_api: openmp\n",
      "        version: None\n",
      "    num_threads: 12\n"
     ]
    }
   ],
   "source": [
    "import sklearn\n",
    "sklearn.show_versions()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a3bd1a26-5dc5-4048-9ee3-b2c52f72dec1",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e8890ebb-1f9c-4ef6-a7fe-8eca7615a161",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "incomplete input (318560893.py, line 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  Cell \u001b[1;32mIn[9], line 2\u001b[1;36m\u001b[0m\n\u001b[1;33m    y_label=clf.predict(np.array([0,2,3,4])\u001b[0m\n\u001b[1;37m                                           ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m incomplete input\n"
     ]
    }
   ],
   "source": [
    "#make prediction\n",
    "y_label=clf.predict(np.array([0,2,3,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8a3b03b6-40d0-438a-a4d0-f788103ee936",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1,\n",
       "       1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0], dtype=int64)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_preds=clf.predict(X_test)\n",
    "y_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f1275cca-f5b3-49e0-8d8f-1457ebee7316",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "93     1\n",
       "88     1\n",
       "69     1\n",
       "23     1\n",
       "226    0\n",
       "      ..\n",
       "44     1\n",
       "57     1\n",
       "283    0\n",
       "177    0\n",
       "209    0\n",
       "Name: target, Length: 61, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b402f078-1852-430f-82e6-d577fafd8fdd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 4. Evaluate the model on the training data and test data\n",
    "clf.score(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c9b77d0b-b490-48f6-bdaa-cd2066b1164a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8524590163934426"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c27d512b-cb57-4624-be79-7d4f86b1d39d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.76      0.80      0.78        20\n",
      "           1       0.90      0.88      0.89        41\n",
      "\n",
      "    accuracy                           0.85        61\n",
      "   macro avg       0.83      0.84      0.83        61\n",
      "weighted avg       0.85      0.85      0.85        61\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
    "\n",
    "print(classification_report(y_test,y_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "58940dc4-ed40-4a77-9ea8-b0bbe2269000",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[16,  4],\n",
       "       [ 5, 36]], dtype=int64)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test,y_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e959e18c-b541-4797-8ac7-0ecd100e0652",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8524590163934426"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test,y_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e3878ccd-9a81-4f31-a1c0-40e4eb7152fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trying model with 10 estimators...\n",
      "Model accuracy on test set: 78.69%\n",
      "\n",
      "Trying model with 20 estimators...\n",
      "Model accuracy on test set: 77.05%\n",
      "\n",
      "Trying model with 30 estimators...\n",
      "Model accuracy on test set: 86.89%\n",
      "\n",
      "Trying model with 40 estimators...\n",
      "Model accuracy on test set: 85.25%\n",
      "\n",
      "Trying model with 50 estimators...\n",
      "Model accuracy on test set: 85.25%\n",
      "\n",
      "Trying model with 60 estimators...\n",
      "Model accuracy on test set: 88.52%\n",
      "\n",
      "Trying model with 70 estimators...\n",
      "Model accuracy on test set: 88.52%\n",
      "\n",
      "Trying model with 80 estimators...\n",
      "Model accuracy on test set: 90.16%\n",
      "\n",
      "Trying model with 90 estimators...\n",
      "Model accuracy on test set: 88.52%\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#5. Improve the model\n",
    "# try different amount of n_estimators\n",
    "np.random.seed(42)\n",
    "for i in range(10,100,10):\n",
    "    print(f\"Trying model with {i} estimators...\")\n",
    "    clf=RandomForestClassifier(n_estimators=i).fit(X_train,y_train)\n",
    "    print(f\"Model accuracy on test set: {clf.score(X_test,y_test)*100:.2f}%\")\n",
    "    print(\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "33c8a0fb-9433-42ae-b047-350c5a59e137",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 6. save a model and load it\n",
    "import pickle \n",
    "pickle.dump(clf,open(\"random_forest_model_1.pk1\",\"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0eaa6dc6-2f93-4458-ba07-c36fefc323d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8852459016393442"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loaded_model=pickle.load(open(\"random_forest_model_1.pk1\",\"rb\"))\n",
    "loaded_model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ef8bbcf-a2a3-4736-8cf7-8a2e224bd726",
   "metadata": {},
   "source": [
    "## 1. getting our data ready to be used with machine learning \n",
    "\n",
    "3 main things we have to do:\n",
    "1. split the data into features and labels (usually 'X' & 'y')\n",
    "2. filling (also called as imputing) or disregarding missing values\n",
    "3. converting non-numerical values to numerical values (also called feature encoding)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "71739019-8500-4cba-bb99-1b2c72a3af5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <th>1</th>\n",
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       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart_disease.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a9ac7d2d-2c9e-43c2-9192-4e1a37bd02a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
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       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
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       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  \n",
       "0   0     1  \n",
       "1   0     2  \n",
       "2   0     2  \n",
       "3   0     2  \n",
       "4   0     2  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=heart_disease.drop(\"target\",axis=1)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "35436978-e910-46a5-bf1b-b42c95e2c709",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    1\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=heart_disease[\"target\"]\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3317cd97-49b0-41b6-85dc-3febfe8e3847",
   "metadata": {},
   "outputs": [],
   "source": [
    "#split the data into training and test sets\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "91145e10-e8dc-4f74-a8ce-1bb533f58d99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((242, 13), (61, 13), (242,), (61,))"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape,X_test.shape,y_train.shape,y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7ac8030e-ca39-4a62-9461-964faff2dacd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(303, 13)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "20be2a83-8f89-4264-a1e9-93bb6b30fc01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "242.4"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape[0]*0.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "20eeb078-3cab-43f2-acf3-f2a1de032262",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "303"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(heart_disease)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ca8b21d-3a39-4ad2-839f-4bceb1727f14",
   "metadata": {},
   "source": [
    "### 1.1 make sure it's all numerical "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b3cebfdf-956c-4658-b6b1-ccb6b56282f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Honda</td>\n",
       "      <td>White</td>\n",
       "      <td>35431</td>\n",
       "      <td>4</td>\n",
       "      <td>15323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Blue</td>\n",
       "      <td>192714</td>\n",
       "      <td>5</td>\n",
       "      <td>19943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Honda</td>\n",
       "      <td>White</td>\n",
       "      <td>84714</td>\n",
       "      <td>4</td>\n",
       "      <td>28343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>154365</td>\n",
       "      <td>4</td>\n",
       "      <td>13434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>Blue</td>\n",
       "      <td>181577</td>\n",
       "      <td>3</td>\n",
       "      <td>14043</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors  Price\n",
       "0   Honda  White          35431      4  15323\n",
       "1     BMW   Blue         192714      5  19943\n",
       "2   Honda  White          84714      4  28343\n",
       "3  Toyota  White         154365      4  13434\n",
       "4  Nissan   Blue         181577      3  14043"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales=pd.read_csv(\"data/car-sales-extended.csv\")\n",
    "car_sales.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ff2e23a0-7f2e-4169-b7cc-d827d483b5d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(car_sales)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "dcbb31c1-9d99-4ddb-a1c5-15006f20d533",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make             object\n",
       "Colour           object\n",
       "Odometer (KM)     int64\n",
       "Doors             int64\n",
       "Price             int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e44239b4-5f4b-43dd-baa0-96548a44831c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Honda</td>\n",
       "      <td>White</td>\n",
       "      <td>35431</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Blue</td>\n",
       "      <td>192714</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Honda</td>\n",
       "      <td>White</td>\n",
       "      <td>84714</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>154365</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>Blue</td>\n",
       "      <td>181577</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors\n",
       "0   Honda  White          35431      4\n",
       "1     BMW   Blue         192714      5\n",
       "2   Honda  White          84714      4\n",
       "3  Toyota  White         154365      4\n",
       "4  Nissan   Blue         181577      3"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# split into X/y\n",
    "X=car_sales.drop(['Price'],axis=1)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8f9bf465-0892-49f2-97a3-401a1fc56481",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    15323\n",
       "1    19943\n",
       "2    28343\n",
       "3    13434\n",
       "4    14043\n",
       "Name: Price, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=car_sales[\"Price\"]\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "bfa8b4b4-0889-4e87-a071-36e48805bc26",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Splitting into training and test set \n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f71e923e-c957-4b7e-9e9d-c62b2eea652c",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: 'Toyota'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_27364\\2269202656.py\u001b[0m in \u001b[0;36m?\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mensemble\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mRandomForestRegressor\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mRandomForestRegressor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\codesss\\miniconda_projects\\env\\Lib\\site-packages\\sklearn\\ensemble\\_forest.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m    341\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    342\u001b[0m         \u001b[1;31m# Validate or convert input data\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    343\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0missparse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    344\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"sparse multilabel-indicator for y is not supported.\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 345\u001b[1;33m         X, y = self._validate_data(\n\u001b[0m\u001b[0;32m    346\u001b[0m             \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmulti_output\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"csc\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mDTYPE\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    347\u001b[0m         )\n\u001b[0;32m    348\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0msample_weight\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\codesss\\miniconda_projects\\env\\Lib\\site-packages\\sklearn\\base.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[0;32m    580\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[1;34m\"estimator\"\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcheck_y_params\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    581\u001b[0m                     \u001b[0mcheck_y_params\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mdefault_check_params\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mcheck_y_params\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    582\u001b[0m                 \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput_name\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"y\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mcheck_y_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    583\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 584\u001b[1;33m                 \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_X_y\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mcheck_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    585\u001b[0m             \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    586\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    587\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mno_val_X\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mcheck_params\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"ensure_2d\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\codesss\\miniconda_projects\\env\\Lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)\u001b[0m\n\u001b[0;32m   1102\u001b[0m         raise ValueError(\n\u001b[0;32m   1103\u001b[0m             \u001b[1;34mf\"{estimator_name} requires y to be passed, but the target y is None\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1104\u001b[0m         )\n\u001b[0;32m   1105\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1106\u001b[1;33m     X = check_array(\n\u001b[0m\u001b[0;32m   1107\u001b[0m         \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1108\u001b[0m         \u001b[0maccept_sparse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maccept_sparse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1109\u001b[0m         \u001b[0maccept_large_sparse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maccept_large_sparse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\codesss\\miniconda_projects\\env\\Lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[0;32m    876\u001b[0m                         )\n\u001b[0;32m    877\u001b[0m                     \u001b[0marray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mxp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    878\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    879\u001b[0m                     \u001b[0marray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_asarray_with_order\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mxp\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mxp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 880\u001b[1;33m             \u001b[1;32mexcept\u001b[0m \u001b[0mComplexWarning\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mcomplex_warning\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    881\u001b[0m                 raise ValueError(\n\u001b[0;32m    882\u001b[0m                     \u001b[1;34m\"Complex data not supported\\n{}\\n\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    883\u001b[0m                 ) from complex_warning\n",
      "\u001b[1;32m~\\codesss\\miniconda_projects\\env\\Lib\\site-packages\\sklearn\\utils\\_array_api.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(array, dtype, order, copy, xp)\u001b[0m\n\u001b[0;32m    181\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mxp\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    182\u001b[0m         \u001b[0mxp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_namespace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    183\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mxp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;34m\"numpy\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"numpy.array_api\"\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    184\u001b[0m         \u001b[1;31m# Use NumPy API to support order\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 185\u001b[1;33m         \u001b[0marray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    186\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mxp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    187\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    188\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mxp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\codesss\\miniconda_projects\\env\\Lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(self, dtype)\u001b[0m\n\u001b[0;32m   2082\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__array__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mnpt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDTypeLike\u001b[0m \u001b[1;33m|\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2083\u001b[0m         \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2084\u001b[1;33m         \u001b[0marr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2085\u001b[0m         if (\n\u001b[0;32m   2086\u001b[0m             \u001b[0mastype_is_view\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2087\u001b[0m             \u001b[1;32mand\u001b[0m \u001b[0musing_copy_on_write\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: could not convert string to float: 'Toyota'"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "model=RandomForestRegressor()\n",
    "model.fit(X_train,y_train)\n",
    "model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "4602e609-8878-487a-a41f-3f87d7c79bbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.00000e+00, 1.00000e+00, 0.00000e+00, ..., 1.00000e+00,\n",
       "        0.00000e+00, 3.54310e+04],\n",
       "       [1.00000e+00, 0.00000e+00, 0.00000e+00, ..., 0.00000e+00,\n",
       "        1.00000e+00, 1.92714e+05],\n",
       "       [0.00000e+00, 1.00000e+00, 0.00000e+00, ..., 1.00000e+00,\n",
       "        0.00000e+00, 8.47140e+04],\n",
       "       ...,\n",
       "       [0.00000e+00, 0.00000e+00, 1.00000e+00, ..., 1.00000e+00,\n",
       "        0.00000e+00, 6.66040e+04],\n",
       "       [0.00000e+00, 1.00000e+00, 0.00000e+00, ..., 1.00000e+00,\n",
       "        0.00000e+00, 2.15883e+05],\n",
       "       [0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 1.00000e+00,\n",
       "        0.00000e+00, 2.48360e+05]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# turn the categories into numbers\n",
    "from sklearn.preprocessing import OneHotEncoder #Encode categorical features as a one-hot numeric array.\n",
    "from sklearn.compose import ColumnTransformer # transform an entire column\n",
    "\n",
    "categorical_features=[\"Make\",\"Colour\",\"Doors\"]\n",
    "one_hot=OneHotEncoder()\n",
    "transformer=ColumnTransformer([(\"one_hot\",\n",
    "                                one_hot,\n",
    "                                categorical_features)],\n",
    "                                remainder=\"passthrough\")\n",
    "transformed_X=transformer.fit_transform(X) #fit_transform is a convenience method that combines the fitting and transformation steps into one call.\n",
    "transformed_X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "725a56ad-5094-4531-b515-fe86e470dc6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Honda</td>\n",
       "      <td>White</td>\n",
       "      <td>35431</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Blue</td>\n",
       "      <td>192714</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Honda</td>\n",
       "      <td>White</td>\n",
       "      <td>84714</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>154365</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>Blue</td>\n",
       "      <td>181577</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <th>995</th>\n",
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       "      <td>Black</td>\n",
       "      <td>35820</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>155144</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>Blue</td>\n",
       "      <td>66604</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>Honda</td>\n",
       "      <td>White</td>\n",
       "      <td>215883</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>248360</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Make Colour  Odometer (KM)  Doors\n",
       "0     Honda  White          35431      4\n",
       "1       BMW   Blue         192714      5\n",
       "2     Honda  White          84714      4\n",
       "3    Toyota  White         154365      4\n",
       "4    Nissan   Blue         181577      3\n",
       "..      ...    ...            ...    ...\n",
       "995  Toyota  Black          35820      4\n",
       "996  Nissan  White         155144      3\n",
       "997  Nissan   Blue          66604      4\n",
       "998   Honda  White         215883      4\n",
       "999  Toyota   Blue         248360      4\n",
       "\n",
       "[1000 rows x 4 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
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  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "be0ef107-aab0-41ca-96f3-966a8af583b5",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "      0    1    2    3    4    5    6    7    8    9    10   11        12\n",
       "0    0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  1.0  0.0   35431.0\n",
       "1    1.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  1.0  192714.0\n",
       "2    0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  1.0  0.0   84714.0\n",
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       "..   ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...       ...\n",
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       "\n",
       "[1000 rows x 13 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(transformed_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "1a701e7a-fcc8-4f44-b38d-029aa95ad9a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</table>\n",
       "<p>1000 rows × 10 columns</p>\n",
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      ],
      "text/plain": [
       "     Doors  Make_BMW  Make_Honda  Make_Nissan  Make_Toyota  Colour_Black  \\\n",
       "0        4         0           1            0            0             0   \n",
       "1        5         1           0            0            0             0   \n",
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       "3        4         0           0            0            1             0   \n",
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       "999      4         0           0            0            1             0   \n",
       "\n",
       "     Colour_Blue  Colour_Green  Colour_Red  Colour_White  \n",
       "0              0             0           0             1  \n",
       "1              1             0           0             0  \n",
       "2              0             0           0             1  \n",
       "3              0             0           0             1  \n",
       "4              1             0           0             0  \n",
       "..           ...           ...         ...           ...  \n",
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       "997            1             0           0             0  \n",
       "998            0             0           0             1  \n",
       "999            1             0           0             0  \n",
       "\n",
       "[1000 rows x 10 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# converting the numeric data into dataframe using pandas\n",
    "dummies=pd.get_dummies(car_sales[[\"Make\",\"Colour\",\"Doors\"]])\n",
    "dummies.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "1c209cbe-1cf9-4b05-85a3-4084556d1e8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's refit the moedl \n",
    "np.random.seed(42)\n",
    "X_train,X_test,y_train,y_test=train_test_split(transformed_X,\n",
    "                                               y,\n",
    "                                               test_size=0.2)\n",
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "bef3d307-c00d-43d9-ac1d-503c6a36c993",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Honda</td>\n",
       "      <td>White</td>\n",
       "      <td>35431</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Blue</td>\n",
       "      <td>192714</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Honda</td>\n",
       "      <td>White</td>\n",
       "      <td>84714</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>154365</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>Blue</td>\n",
       "      <td>181577</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors\n",
       "0   Honda  White          35431      4\n",
       "1     BMW   Blue         192714      5\n",
       "2   Honda  White          84714      4\n",
       "3  Toyota  White         154365      4\n",
       "4  Nissan   Blue         181577      3"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c819a4bb-46a0-4c11-90db-537fa364c073",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3235867221569877"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "243ca510-3cd6-4351-b180-33e908e40f4d",
   "metadata": {},
   "source": [
    "### 1.2 what if there were missing values?\n",
    "\n",
    "1. fill them with some value (also known as imputation).\n",
    "2. Remove the samples with missing data altogether."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "d0f4cd52-29f2-406c-bf56-7d57d115f87b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Honda</td>\n",
       "      <td>White</td>\n",
       "      <td>35431.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15323.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Blue</td>\n",
       "      <td>192714.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>19943.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Honda</td>\n",
       "      <td>White</td>\n",
       "      <td>84714.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>28343.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>154365.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13434.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>Blue</td>\n",
       "      <td>181577.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14043.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors    Price\n",
       "0   Honda  White        35431.0    4.0  15323.0\n",
       "1     BMW   Blue       192714.0    5.0  19943.0\n",
       "2   Honda  White        84714.0    4.0  28343.0\n",
       "3  Toyota  White       154365.0    4.0  13434.0\n",
       "4  Nissan   Blue       181577.0    3.0  14043.0"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import car sales missing data\n",
    "car_sales_missing=pd.read_csv(\"data/car-sales-extended-missing-data.csv\")\n",
    "car_sales_missing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "2600db67-1833-4290-862d-243f0e3c0ccb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make             49\n",
       "Colour           50\n",
       "Odometer (KM)    50\n",
       "Doors            50\n",
       "Price            50\n",
       "dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#this is to show the number of missing data\n",
    "car_sales_missing.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b982a3cc-7bd8-4383-b221-56fa4de0d911",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create X and y\n",
    "X=car_sales_missing.drop(\"Price\",axis=1)\n",
    "y=car_sales_missing[\"Price\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "8b780b28-9ad2-4682-9c7e-18d095641b5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<1000x16 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 4000 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#let's convert the data into numbers\n",
    "# turn the categories into numbers\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.compose import ColumnTransformer\n",
    "\n",
    "categorical_features=[\"Make\",\"Colour\",\"Doors\"]\n",
    "one_hot=OneHotEncoder()\n",
    "transformer=ColumnTransformer([(\"one_hot\",\n",
    "                                one_hot,\n",
    "                                categorical_features)],\n",
    "                                remainder=\"passthrough\")\n",
    "transformed_X=transformer.fit_transform(X)\n",
    "transformed_X"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b60d93ef-138c-4e30-8187-ccedf6b5c27d",
   "metadata": {},
   "source": [
    "## Previous version shows \"contains NaN\" error but not in this version\n",
    "### Option 1: Fill missing data with Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "1b901183-90cb-4a10-a65b-30ba51beb65c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fill the \"Make\" column\n",
    "car_sales_missing[\"Make\"].fillna(\"missing\",inplace=True)\n",
    "\n",
    "# fill the \"Colour\" column\n",
    "car_sales_missing[\"Colour\"].fillna(\"missing\",inplace=True)\n",
    "\n",
    "# fill the \"Odometer (KM)\" column\n",
    "car_sales_missing[\"Odometer (KM)\"].fillna(car_sales_missing[\"Odometer (KM)\"].mean(),inplace=True)\n",
    "\n",
    "# fill the \"Doors\" column\n",
    "car_sales_missing[\"Doors\"].fillna(4,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "a8b61046-5347-4cfc-96e9-276b429f2ad3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make              0\n",
       "Colour            0\n",
       "Odometer (KM)     0\n",
       "Doors             0\n",
       "Price            50\n",
       "dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check our dataframe again\n",
    "car_sales_missing.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "9aa9a032-c0c3-4060-a583-7d706d71ebc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove rows with missing price value\n",
    "car_sales_missing.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "b35e8d27-bb31-4eba-b33f-b9c1ca0df986",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make             0\n",
       "Colour           0\n",
       "Odometer (KM)    0\n",
       "Doors            0\n",
       "Price            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales_missing.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "71cff12b-9b63-432a-835a-bb58020ed70c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "950"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(car_sales_missing) # we lost some data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "f4a7c1f7-48f7-4b0a-a190-3a362e8d52ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=car_sales_missing.drop(\"Price\",axis=1)\n",
    "y=car_sales_missing[\"Price\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "ade57990-73ea-4613-b99a-64cf1e1ad44b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.00000e+00, 1.00000e+00, 0.00000e+00, ..., 0.00000e+00,\n",
       "        3.54310e+04, 1.53230e+04],\n",
       "       [1.00000e+00, 0.00000e+00, 0.00000e+00, ..., 1.00000e+00,\n",
       "        1.92714e+05, 1.99430e+04],\n",
       "       [0.00000e+00, 1.00000e+00, 0.00000e+00, ..., 0.00000e+00,\n",
       "        8.47140e+04, 2.83430e+04],\n",
       "       ...,\n",
       "       [0.00000e+00, 0.00000e+00, 1.00000e+00, ..., 0.00000e+00,\n",
       "        6.66040e+04, 3.15700e+04],\n",
       "       [0.00000e+00, 1.00000e+00, 0.00000e+00, ..., 0.00000e+00,\n",
       "        2.15883e+05, 4.00100e+03],\n",
       "       [0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 0.00000e+00,\n",
       "        2.48360e+05, 1.27320e+04]])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.compose import ColumnTransformer\n",
    "\n",
    "categorical_features=[\"Make\",\"Colour\",\"Doors\"]\n",
    "one_hot=OneHotEncoder()\n",
    "transformer=ColumnTransformer([(\"one_hot\",\n",
    "                                one_hot,\n",
    "                                categorical_features)],\n",
    "                                remainder=\"passthrough\")\n",
    "transformed_X=transformer.fit_transform(car_sales_missing)\n",
    "transformed_X"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8d64415-582a-471e-b74b-49cc76fbc9f1",
   "metadata": {},
   "source": [
    "### Option 2: Fill missing data with Scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "04b60caa-676e-43a8-a4e2-41a96a7bf735",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Honda</td>\n",
       "      <td>White</td>\n",
       "      <td>35431.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15323.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Blue</td>\n",
       "      <td>192714.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>19943.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Honda</td>\n",
       "      <td>White</td>\n",
       "      <td>84714.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>28343.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>154365.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13434.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>Blue</td>\n",
       "      <td>181577.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14043.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors    Price\n",
       "0   Honda  White        35431.0    4.0  15323.0\n",
       "1     BMW   Blue       192714.0    5.0  19943.0\n",
       "2   Honda  White        84714.0    4.0  28343.0\n",
       "3  Toyota  White       154365.0    4.0  13434.0\n",
       "4  Nissan   Blue       181577.0    3.0  14043.0"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales_missing=pd.read_csv(\"data/car-sales-extended-missing-data.csv\")\n",
    "car_sales_missing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "045c7638-3300-40d9-ba54-84f1db7c7a7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make             49\n",
       "Colour           50\n",
       "Odometer (KM)    50\n",
       "Doors            50\n",
       "Price            50\n",
       "dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales_missing.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "80be4e7d-acb0-4570-81cc-d1b6c307b1f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make             47\n",
       "Colour           46\n",
       "Odometer (KM)    48\n",
       "Doors            47\n",
       "Price             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#drop the rows with no labels\n",
    "car_sales_missing.dropna(subset=[\"Price\"],inplace=True)\n",
    "car_sales_missing.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "dfb7ab4b-fd4d-4cdb-bf19-f5db511ed626",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=car_sales_missing.drop(\"Price\",axis=1)\n",
    "y=car_sales_missing[\"Price\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "e842a888-c006-4710-99a0-bfc2104d2c22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['Honda', 'White', 4.0, 35431.0],\n",
       "       ['BMW', 'Blue', 5.0, 192714.0],\n",
       "       ['Honda', 'White', 4.0, 84714.0],\n",
       "       ...,\n",
       "       ['Nissan', 'Blue', 4.0, 66604.0],\n",
       "       ['Honda', 'White', 4.0, 215883.0],\n",
       "       ['Toyota', 'Blue', 4.0, 248360.0]], dtype=object)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fill missing values with Scikit-Learn\n",
    "from sklearn.impute import SimpleImputer \n",
    "from sklearn.compose import ColumnTransformer\n",
    "\n",
    "#Fill categorical values with 'missing' and numerical values with mean\n",
    "cat_imputer=SimpleImputer(strategy=\"constant\",fill_value=\"missing\")\n",
    "door_imputer=SimpleImputer(strategy=\"constant\",fill_value=4)\n",
    "num_imputer=SimpleImputer(strategy=\"mean\")\n",
    "\n",
    "# Define columns\n",
    "cat_features=[\"Make\",\"Colour\"]\n",
    "door_feature=[\"Doors\"]\n",
    "num_features=[\"Odometer (KM)\"]\n",
    "\n",
    "#Create an imputer (something that fills missing data)\n",
    "imputer=ColumnTransformer([\n",
    "    (\"cat_imputer\",cat_imputer,cat_features),\n",
    "    (\"door_imputer\",door_imputer,door_feature),\n",
    "    (\"num_imputer\",num_imputer,num_features)\n",
    "])\n",
    "\n",
    "# transform the data\n",
    "filled_X=imputer.fit_transform(X)\n",
    "filled_X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "5962e7c3-559b-46bb-965d-fe6a31657df6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Honda</td>\n",
       "      <td>White</td>\n",
       "      <td>4.0</td>\n",
       "      <td>35431.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Blue</td>\n",
       "      <td>5.0</td>\n",
       "      <td>192714.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Honda</td>\n",
       "      <td>White</td>\n",
       "      <td>4.0</td>\n",
       "      <td>84714.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>4.0</td>\n",
       "      <td>154365.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>Blue</td>\n",
       "      <td>3.0</td>\n",
       "      <td>181577.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour Doors Odometer (KM)\n",
       "0   Honda  White   4.0       35431.0\n",
       "1     BMW   Blue   5.0      192714.0\n",
       "2   Honda  White   4.0       84714.0\n",
       "3  Toyota  White   4.0      154365.0\n",
       "4  Nissan   Blue   3.0      181577.0"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales_filled=pd.DataFrame(filled_X,\n",
    "                              columns=[\"Make\",\"Colour\",\"Doors\",\"Odometer (KM)\"])\n",
    "car_sales_filled.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "9b214338-4e53-480a-8566-ed69db258a11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make             0\n",
       "Colour           0\n",
       "Doors            0\n",
       "Odometer (KM)    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales_filled.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "eaa8f747-9e5a-4bc2-9a5f-4a855c45cc31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<950x15 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 3800 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.compose import ColumnTransformer\n",
    "\n",
    "categorical_features=[\"Make\",\"Colour\",\"Doors\"]\n",
    "one_hot=OneHotEncoder()\n",
    "transformer=ColumnTransformer([(\"one_hot\",\n",
    "                                one_hot,\n",
    "                                categorical_features)],\n",
    "                                remainder=\"passthrough\")\n",
    "transformed_X=transformer.fit_transform(car_sales_filled)\n",
    "transformed_X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "c32fa1a0-b31c-4115-904e-167d4cb029b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.21990196728583944"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now we've got our data as numbers and filled (no missing values)\n",
    "# Let's fit a model\n",
    "np.random.seed(42)\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test=train_test_split(transformed_X,\n",
    "                                                     y,\n",
    "                                                     test_size=0.2)\n",
    "#the order of X_train,X_test,y_train,y_test matters\n",
    "model=RandomForestRegressor()\n",
    "model.fit(X_train,y_train)\n",
    "model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "dccab8d4-1be2-4688-8ab0-19234a760508",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(950, 950, 1000)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(car_sales_filled),len(car_sales_missing),len(car_sales)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "2b4890fe-6285-4da6-bdb8-acf0cc0c7825",
   "metadata": {},
   "outputs": [],
   "source": [
    "what_were_covering =['0. An end-to-end Scikit-Learn workflow',\n",
    "'1. Getting the data ready',\n",
    "'2. Choose the right estimator/algorithm for our program',\n",
    "'3. Fit the model/algorithm and use it to make predictions on our data',\n",
    "'4. Evaluating a model',\n",
    "'5. improve a model',\n",
    "'6. save and load a trained model',\n",
    "'7. Put it all together']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "2184bc8b-17e5-4d3f-bef0-8adb11c4794e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['0. An end-to-end Scikit-Learn workflow',\n",
       " '1. Getting the data ready',\n",
       " '2. Choose the right estimator/algorithm for our program',\n",
       " '3. Fit the model/algorithm and use it to make predictions on our data',\n",
       " '4. Evaluating a model',\n",
       " '5. improve a model',\n",
       " '6. save and load a trained model',\n",
       " '7. Put it all together']"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "what_were_covering"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66a44f72-edf6-4c99-88f3-16876619fdf6",
   "metadata": {},
   "source": [
    "## 2. Choosing the right estimator/algorithm for your problem\n",
    "some things to note:\n",
    "* Sklearn refers to machine learning models,algorithms as estimators.\n",
    "* Classification problem - predicting a category (heart disease or not)\n",
    "    * Sometimes you'll see `clf` (short for classifier) used as a classification estimator\n",
    "* Regression problem - predicting a number (selling price of a car)\n",
    "\n",
    "If you're working on a ML problem and looking to use Sklearn and not sure what model you should use, refer to the sklearn ML map: https://scikit-learn.org/stable/tutorial/machine_learning_map/index.html"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e503579c-74fe-408b-bf48-ef0c2b554a50",
   "metadata": {},
   "source": [
    "## 2.1 Picking a ML model for regression problem\n",
    "Let's use the California Housing dataset - https://scikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_california_housing.html#sklearn.datasets.fetch_california_housing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "117157e2-78d1-4a40-8ff9-62c21ea0b7ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'data': array([[   8.3252    ,   41.        ,    6.98412698, ...,    2.55555556,\n",
       "           37.88      , -122.23      ],\n",
       "        [   8.3014    ,   21.        ,    6.23813708, ...,    2.10984183,\n",
       "           37.86      , -122.22      ],\n",
       "        [   7.2574    ,   52.        ,    8.28813559, ...,    2.80225989,\n",
       "           37.85      , -122.24      ],\n",
       "        ...,\n",
       "        [   1.7       ,   17.        ,    5.20554273, ...,    2.3256351 ,\n",
       "           39.43      , -121.22      ],\n",
       "        [   1.8672    ,   18.        ,    5.32951289, ...,    2.12320917,\n",
       "           39.43      , -121.32      ],\n",
       "        [   2.3886    ,   16.        ,    5.25471698, ...,    2.61698113,\n",
       "           39.37      , -121.24      ]]),\n",
       " 'target': array([4.526, 3.585, 3.521, ..., 0.923, 0.847, 0.894]),\n",
       " 'frame': None,\n",
       " 'target_names': ['MedHouseVal'],\n",
       " 'feature_names': ['MedInc',\n",
       "  'HouseAge',\n",
       "  'AveRooms',\n",
       "  'AveBedrms',\n",
       "  'Population',\n",
       "  'AveOccup',\n",
       "  'Latitude',\n",
       "  'Longitude'],\n",
       " 'DESCR': '.. _california_housing_dataset:\\n\\nCalifornia Housing dataset\\n--------------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 20640\\n\\n    :Number of Attributes: 8 numeric, predictive attributes and the target\\n\\n    :Attribute Information:\\n        - MedInc        median income in block group\\n        - HouseAge      median house age in block group\\n        - AveRooms      average number of rooms per household\\n        - AveBedrms     average number of bedrooms per household\\n        - Population    block group population\\n        - AveOccup      average number of household members\\n        - Latitude      block group latitude\\n        - Longitude     block group longitude\\n\\n    :Missing Attribute Values: None\\n\\nThis dataset was obtained from the StatLib repository.\\nhttps://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.html\\n\\nThe target variable is the median house value for California districts,\\nexpressed in hundreds of thousands of dollars ($100,000).\\n\\nThis dataset was derived from the 1990 U.S. census, using one row per census\\nblock group. A block group is the smallest geographical unit for which the U.S.\\nCensus Bureau publishes sample data (a block group typically has a population\\nof 600 to 3,000 people).\\n\\nA household is a group of people residing within a home. Since the average\\nnumber of rooms and bedrooms in this dataset are provided per household, these\\ncolumns may take surprisingly large values for block groups with few households\\nand many empty houses, such as vacation resorts.\\n\\nIt can be downloaded/loaded using the\\n:func:`sklearn.datasets.fetch_california_housing` function.\\n\\n.. topic:: References\\n\\n    - Pace, R. Kelley and Ronald Barry, Sparse Spatial Autoregressions,\\n      Statistics and Probability Letters, 33 (1997) 291-297\\n'}"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get California Housing dataset\n",
    "from sklearn.datasets import fetch_california_housing\n",
    "housing = fetch_california_housing()\n",
    "housing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "b1d4abe7-000b-4f39-b736-563c702a01c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.3252</td>\n",
       "      <td>41.0</td>\n",
       "      <td>6.984127</td>\n",
       "      <td>1.023810</td>\n",
       "      <td>322.0</td>\n",
       "      <td>2.555556</td>\n",
       "      <td>37.88</td>\n",
       "      <td>-122.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.3014</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.238137</td>\n",
       "      <td>0.971880</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>2.109842</td>\n",
       "      <td>37.86</td>\n",
       "      <td>-122.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.2574</td>\n",
       "      <td>52.0</td>\n",
       "      <td>8.288136</td>\n",
       "      <td>1.073446</td>\n",
       "      <td>496.0</td>\n",
       "      <td>2.802260</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.6431</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5.817352</td>\n",
       "      <td>1.073059</td>\n",
       "      <td>558.0</td>\n",
       "      <td>2.547945</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.8462</td>\n",
       "      <td>52.0</td>\n",
       "      <td>6.281853</td>\n",
       "      <td>1.081081</td>\n",
       "      <td>565.0</td>\n",
       "      <td>2.181467</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20635</th>\n",
       "      <td>1.5603</td>\n",
       "      <td>25.0</td>\n",
       "      <td>5.045455</td>\n",
       "      <td>1.133333</td>\n",
       "      <td>845.0</td>\n",
       "      <td>2.560606</td>\n",
       "      <td>39.48</td>\n",
       "      <td>-121.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20636</th>\n",
       "      <td>2.5568</td>\n",
       "      <td>18.0</td>\n",
       "      <td>6.114035</td>\n",
       "      <td>1.315789</td>\n",
       "      <td>356.0</td>\n",
       "      <td>3.122807</td>\n",
       "      <td>39.49</td>\n",
       "      <td>-121.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20637</th>\n",
       "      <td>1.7000</td>\n",
       "      <td>17.0</td>\n",
       "      <td>5.205543</td>\n",
       "      <td>1.120092</td>\n",
       "      <td>1007.0</td>\n",
       "      <td>2.325635</td>\n",
       "      <td>39.43</td>\n",
       "      <td>-121.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20638</th>\n",
       "      <td>1.8672</td>\n",
       "      <td>18.0</td>\n",
       "      <td>5.329513</td>\n",
       "      <td>1.171920</td>\n",
       "      <td>741.0</td>\n",
       "      <td>2.123209</td>\n",
       "      <td>39.43</td>\n",
       "      <td>-121.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20639</th>\n",
       "      <td>2.3886</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5.254717</td>\n",
       "      <td>1.162264</td>\n",
       "      <td>1387.0</td>\n",
       "      <td>2.616981</td>\n",
       "      <td>39.37</td>\n",
       "      <td>-121.24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20640 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
       "0      8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88   \n",
       "1      8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86   \n",
       "2      7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85   \n",
       "3      5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85   \n",
       "4      3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85   \n",
       "...       ...       ...       ...        ...         ...       ...       ...   \n",
       "20635  1.5603      25.0  5.045455   1.133333       845.0  2.560606     39.48   \n",
       "20636  2.5568      18.0  6.114035   1.315789       356.0  3.122807     39.49   \n",
       "20637  1.7000      17.0  5.205543   1.120092      1007.0  2.325635     39.43   \n",
       "20638  1.8672      18.0  5.329513   1.171920       741.0  2.123209     39.43   \n",
       "20639  2.3886      16.0  5.254717   1.162264      1387.0  2.616981     39.37   \n",
       "\n",
       "       Longitude  \n",
       "0        -122.23  \n",
       "1        -122.22  \n",
       "2        -122.24  \n",
       "3        -122.25  \n",
       "4        -122.25  \n",
       "...          ...  \n",
       "20635    -121.09  \n",
       "20636    -121.21  \n",
       "20637    -121.22  \n",
       "20638    -121.32  \n",
       "20639    -121.24  \n",
       "\n",
       "[20640 rows x 8 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_df=pd.DataFrame(housing[\"data\"],columns=housing['feature_names'])\n",
    "housing_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "6240305a-f3aa-415e-a2a6-cbf7a2ff7165",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.3252</td>\n",
       "      <td>41.0</td>\n",
       "      <td>6.984127</td>\n",
       "      <td>1.023810</td>\n",
       "      <td>322.0</td>\n",
       "      <td>2.555556</td>\n",
       "      <td>37.88</td>\n",
       "      <td>-122.23</td>\n",
       "      <td>4.526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.3014</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.238137</td>\n",
       "      <td>0.971880</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>2.109842</td>\n",
       "      <td>37.86</td>\n",
       "      <td>-122.22</td>\n",
       "      <td>3.585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.2574</td>\n",
       "      <td>52.0</td>\n",
       "      <td>8.288136</td>\n",
       "      <td>1.073446</td>\n",
       "      <td>496.0</td>\n",
       "      <td>2.802260</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.24</td>\n",
       "      <td>3.521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.6431</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5.817352</td>\n",
       "      <td>1.073059</td>\n",
       "      <td>558.0</td>\n",
       "      <td>2.547945</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>3.413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.8462</td>\n",
       "      <td>52.0</td>\n",
       "      <td>6.281853</td>\n",
       "      <td>1.081081</td>\n",
       "      <td>565.0</td>\n",
       "      <td>2.181467</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>3.422</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
       "0  8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88   \n",
       "1  8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86   \n",
       "2  7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85   \n",
       "3  5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85   \n",
       "4  3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85   \n",
       "\n",
       "   Longitude  target  \n",
       "0    -122.23   4.526  \n",
       "1    -122.22   3.585  \n",
       "2    -122.24   3.521  \n",
       "3    -122.25   3.413  \n",
       "4    -122.25   3.422  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_df[\"target\"]=housing[\"target\"]\n",
    "housing_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "a2b88632-5f10-448e-a188-98695f881b79",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"['MedHouseVal'] not found in axis\"",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[68], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m housing_df\u001b[38;5;241m=\u001b[39mhousing_df\u001b[38;5;241m.\u001b[39mdrop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMedHouseVal\u001b[39m\u001b[38;5;124m\"\u001b[39m,axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m      2\u001b[0m housing_df\n",
      "File \u001b[1;32m~\\codesss\\miniconda_projects\\env\\Lib\\site-packages\\pandas\\core\\frame.py:5344\u001b[0m, in \u001b[0;36mDataFrame.drop\u001b[1;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[0;32m   5196\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdrop\u001b[39m(\n\u001b[0;32m   5197\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m   5198\u001b[0m     labels: IndexLabel \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   5205\u001b[0m     errors: IgnoreRaise \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mraise\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   5206\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m DataFrame \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   5207\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   5208\u001b[0m \u001b[38;5;124;03m    Drop specified labels from rows or columns.\u001b[39;00m\n\u001b[0;32m   5209\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   5342\u001b[0m \u001b[38;5;124;03m            weight  1.0     0.8\u001b[39;00m\n\u001b[0;32m   5343\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m-> 5344\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39mdrop(\n\u001b[0;32m   5345\u001b[0m         labels\u001b[38;5;241m=\u001b[39mlabels,\n\u001b[0;32m   5346\u001b[0m         axis\u001b[38;5;241m=\u001b[39maxis,\n\u001b[0;32m   5347\u001b[0m         index\u001b[38;5;241m=\u001b[39mindex,\n\u001b[0;32m   5348\u001b[0m         columns\u001b[38;5;241m=\u001b[39mcolumns,\n\u001b[0;32m   5349\u001b[0m         level\u001b[38;5;241m=\u001b[39mlevel,\n\u001b[0;32m   5350\u001b[0m         inplace\u001b[38;5;241m=\u001b[39minplace,\n\u001b[0;32m   5351\u001b[0m         errors\u001b[38;5;241m=\u001b[39merrors,\n\u001b[0;32m   5352\u001b[0m     )\n",
      "File \u001b[1;32m~\\codesss\\miniconda_projects\\env\\Lib\\site-packages\\pandas\\core\\generic.py:4711\u001b[0m, in \u001b[0;36mNDFrame.drop\u001b[1;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[0;32m   4709\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m axis, labels \u001b[38;5;129;01min\u001b[39;00m axes\u001b[38;5;241m.\u001b[39mitems():\n\u001b[0;32m   4710\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m labels \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 4711\u001b[0m         obj \u001b[38;5;241m=\u001b[39m obj\u001b[38;5;241m.\u001b[39m_drop_axis(labels, axis, level\u001b[38;5;241m=\u001b[39mlevel, errors\u001b[38;5;241m=\u001b[39merrors)\n\u001b[0;32m   4713\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m inplace:\n\u001b[0;32m   4714\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_update_inplace(obj)\n",
      "File \u001b[1;32m~\\codesss\\miniconda_projects\\env\\Lib\\site-packages\\pandas\\core\\generic.py:4753\u001b[0m, in \u001b[0;36mNDFrame._drop_axis\u001b[1;34m(self, labels, axis, level, errors, only_slice)\u001b[0m\n\u001b[0;32m   4751\u001b[0m         new_axis \u001b[38;5;241m=\u001b[39m axis\u001b[38;5;241m.\u001b[39mdrop(labels, level\u001b[38;5;241m=\u001b[39mlevel, errors\u001b[38;5;241m=\u001b[39merrors)\n\u001b[0;32m   4752\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 4753\u001b[0m         new_axis \u001b[38;5;241m=\u001b[39m axis\u001b[38;5;241m.\u001b[39mdrop(labels, errors\u001b[38;5;241m=\u001b[39merrors)\n\u001b[0;32m   4754\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m axis\u001b[38;5;241m.\u001b[39mget_indexer(new_axis)\n\u001b[0;32m   4756\u001b[0m \u001b[38;5;66;03m# Case for non-unique axis\u001b[39;00m\n\u001b[0;32m   4757\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "File \u001b[1;32m~\\codesss\\miniconda_projects\\env\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:7000\u001b[0m, in \u001b[0;36mIndex.drop\u001b[1;34m(self, labels, errors)\u001b[0m\n\u001b[0;32m   6998\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mask\u001b[38;5;241m.\u001b[39many():\n\u001b[0;32m   6999\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m errors \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m-> 7000\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mlabels[mask]\u001b[38;5;241m.\u001b[39mtolist()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m not found in axis\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m   7001\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m indexer[\u001b[38;5;241m~\u001b[39mmask]\n\u001b[0;32m   7002\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdelete(indexer)\n",
      "\u001b[1;31mKeyError\u001b[0m: \"['MedHouseVal'] not found in axis\""
     ]
    }
   ],
   "source": [
    "housing_df=housing_df.drop(\"MedHouseVal\",axis=1)\n",
    "housing_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "61f1afef-b765-4d99-81c3-e47739629cb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5758549611440126"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import algorithm/estimator\n",
    "from sklearn.linear_model import Ridge\n",
    "\n",
    "# Setup random seed\n",
    "np.random.seed(42)\n",
    "\n",
    "# Create the data\n",
    "X=housing_df.drop(\"target\",axis=1)\n",
    "y=housing_df[\"target\"] # median house price in $100,000s\n",
    "\n",
    "# Split into train and test\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2)\n",
    "\n",
    "# Instantiate and fit the model (on the training set)\n",
    "model=Ridge()\n",
    "model.fit(X_train,y_train)\n",
    "\n",
    "# Check the score of the model (on the test set)\n",
    "model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "1dd41587-3502-4100-97c5-7979e8bfb057",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.41655189098028245"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# trying different model - ElasticNet\n",
    "# import algorithm/estimator\n",
    "from sklearn.linear_model import ElasticNet\n",
    "\n",
    "# Setup random seed\n",
    "np.random.seed(42)\n",
    "\n",
    "# Create the data\n",
    "X=housing_df.drop(\"target\",axis=1)\n",
    "y=housing_df[\"target\"] # median house price in $100,000s\n",
    "\n",
    "# Split into train and test\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2)\n",
    "\n",
    "# Instantiate and fit the model (on the training set)\n",
    "model=ElasticNet()\n",
    "model.fit(X_train,y_train)\n",
    "\n",
    "# Check the score of the model (on the test set)\n",
    "model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "4d39d5a9-2c0a-4979-947a-6c7aa4e12c3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8065734772187598"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# trying different model - Ensemble methods\n",
    "# import algorithm/estimator\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "# Setup random seed\n",
    "np.random.seed(42)\n",
    "\n",
    "# Create the data\n",
    "X=housing_df.drop(\"target\",axis=1)\n",
    "y=housing_df[\"target\"] # median house price in $100,000s\n",
    "\n",
    "# Split into train and test\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2)\n",
    "\n",
    "# Instantiate and fit the model (on the training set)\n",
    "model=RandomForestRegressor()\n",
    "model.fit(X_train,y_train)\n",
    "\n",
    "# Check the score of the model (on the test set)\n",
    "model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b863aa3-ebe8-44b8-923b-0bd435ac9884",
   "metadata": {},
   "source": [
    "## 2.2 Picking a ml model for a classification problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "ee7236ba-0929-4b81-8e2d-206963a60aa4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'data': array([[5.1, 3.5, 1.4, 0.2],\n",
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       "        [7.9, 3.8, 6.4, 2. ],\n",
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       "        [6.3, 2.8, 5.1, 1.5],\n",
       "        [6.1, 2.6, 5.6, 1.4],\n",
       "        [7.7, 3. , 6.1, 2.3],\n",
       "        [6.3, 3.4, 5.6, 2.4],\n",
       "        [6.4, 3.1, 5.5, 1.8],\n",
       "        [6. , 3. , 4.8, 1.8],\n",
       "        [6.9, 3.1, 5.4, 2.1],\n",
       "        [6.7, 3.1, 5.6, 2.4],\n",
       "        [6.9, 3.1, 5.1, 2.3],\n",
       "        [5.8, 2.7, 5.1, 1.9],\n",
       "        [6.8, 3.2, 5.9, 2.3],\n",
       "        [6.7, 3.3, 5.7, 2.5],\n",
       "        [6.7, 3. , 5.2, 2.3],\n",
       "        [6.3, 2.5, 5. , 1.9],\n",
       "        [6.5, 3. , 5.2, 2. ],\n",
       "        [6.2, 3.4, 5.4, 2.3],\n",
       "        [5.9, 3. , 5.1, 1.8]]),\n",
       " 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]),\n",
       " 'frame': None,\n",
       " 'target_names': array(['setosa', 'versicolor', 'virginica'], dtype='<U10'),\n",
       " 'DESCR': '.. _iris_dataset:\\n\\nIris plants dataset\\n--------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 150 (50 in each of three classes)\\n    :Number of Attributes: 4 numeric, predictive attributes and the class\\n    :Attribute Information:\\n        - sepal length in cm\\n        - sepal width in cm\\n        - petal length in cm\\n        - petal width in cm\\n        - class:\\n                - Iris-Setosa\\n                - Iris-Versicolour\\n                - Iris-Virginica\\n                \\n    :Summary Statistics:\\n\\n    ============== ==== ==== ======= ===== ====================\\n                    Min  Max   Mean    SD   Class Correlation\\n    ============== ==== ==== ======= ===== ====================\\n    sepal length:   4.3  7.9   5.84   0.83    0.7826\\n    sepal width:    2.0  4.4   3.05   0.43   -0.4194\\n    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\\n    petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\\n    ============== ==== ==== ======= ===== ====================\\n\\n    :Missing Attribute Values: None\\n    :Class Distribution: 33.3% for each of 3 classes.\\n    :Creator: R.A. Fisher\\n    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\\n    :Date: July, 1988\\n\\nThe famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\\nfrom Fisher\\'s paper. Note that it\\'s the same as in R, but not as in the UCI\\nMachine Learning Repository, which has two wrong data points.\\n\\nThis is perhaps the best known database to be found in the\\npattern recognition literature.  Fisher\\'s paper is a classic in the field and\\nis referenced frequently to this day.  (See Duda & Hart, for example.)  The\\ndata set contains 3 classes of 50 instances each, where each class refers to a\\ntype of iris plant.  One class is linearly separable from the other 2; the\\nlatter are NOT linearly separable from each other.\\n\\n.. topic:: References\\n\\n   - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\\n     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\\n     Mathematical Statistics\" (John Wiley, NY, 1950).\\n   - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\\n     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\\n   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\\n     Structure and Classification Rule for Recognition in Partially Exposed\\n     Environments\".  IEEE Transactions on Pattern Analysis and Machine\\n     Intelligence, Vol. PAMI-2, No. 1, 67-71.\\n   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\\n     on Information Theory, May 1972, 431-433.\\n   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\\n     conceptual clustering system finds 3 classes in the data.\\n   - Many, many more ...',\n",
       " 'feature_names': ['sepal length (cm)',\n",
       "  'sepal width (cm)',\n",
       "  'petal length (cm)',\n",
       "  'petal width (cm)'],\n",
       " 'filename': 'iris.csv',\n",
       " 'data_module': 'sklearn.datasets.data'}"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# trail\n",
    "from sklearn.datasets import load_iris\n",
    "iris=load_iris()\n",
    "iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "3ddf2cb6-700b-4f15-8549-c23d8bffc806",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
       "0                  5.1               3.5                1.4               0.2\n",
       "1                  4.9               3.0                1.4               0.2\n",
       "2                  4.7               3.2                1.3               0.2\n",
       "3                  4.6               3.1                1.5               0.2\n",
       "4                  5.0               3.6                1.4               0.2\n",
       "..                 ...               ...                ...               ...\n",
       "145                6.7               3.0                5.2               2.3\n",
       "146                6.3               2.5                5.0               1.9\n",
       "147                6.5               3.0                5.2               2.0\n",
       "148                6.2               3.4                5.4               2.3\n",
       "149                5.9               3.0                5.1               1.8\n",
       "\n",
       "[150 rows x 4 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df=pd.DataFrame(iris['data'],columns=iris['feature_names'])\n",
    "iris_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "9bb9b9f9-d8a4-4b46-b743-ee72d43a6155",
   "metadata": {},
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       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                  5.1               3.5                1.4               0.2   \n",
       "1                  4.9               3.0                1.4               0.2   \n",
       "2                  4.7               3.2                1.3               0.2   \n",
       "3                  4.6               3.1                1.5               0.2   \n",
       "4                  5.0               3.6                1.4               0.2   \n",
       "..                 ...               ...                ...               ...   \n",
       "145                6.7               3.0                5.2               2.3   \n",
       "146                6.3               2.5                5.0               1.9   \n",
       "147                6.5               3.0                5.2               2.0   \n",
       "148                6.2               3.4                5.4               2.3   \n",
       "149                5.9               3.0                5.1               1.8   \n",
       "\n",
       "     target  \n",
       "0         0  \n",
       "1         0  \n",
       "2         0  \n",
       "3         0  \n",
       "4         0  \n",
       "..      ...  \n",
       "145       2  \n",
       "146       2  \n",
       "147       2  \n",
       "148       2  \n",
       "149       2  \n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df['target']=iris['target']\n",
    "iris_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "df64b51a-64a0-4428-b17b-765c5631221c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# trying different model - Ensemble methods\n",
    "# import algorithm/estimator\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Setup random seed\n",
    "np.random.seed(42)\n",
    "\n",
    "# Create the data\n",
    "X=iris_df.drop(\"target\",axis=1)\n",
    "y=iris_df[\"target\"] \n",
    "\n",
    "# Split into train and test\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2)\n",
    "\n",
    "# Instantiate and fit the model (on the training set)\n",
    "model=RandomForestClassifier()\n",
    "model.fit(X_train,y_train)\n",
    "\n",
    "# Check the score of the model (on the test set)\n",
    "model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "2789bda3-873a-4944-91cc-f96faaca9ac7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart_disease=pd.read_csv(\"data/heart-disease.csv\")\n",
    "heart_disease.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "8e06e3ad-7d97-44c8-8e7f-effc712e367f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "303"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(heart_disease)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5a602fd-29b7-4cea-b889-bbbcf1052a41",
   "metadata": {},
   "source": [
    "`linearSVC`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "f044eff6-4022-4cae-8517-8449b181899c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8688524590163934"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the linearSVC estimator class\n",
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "# Setup random seed\n",
    "np.random.seed(42)\n",
    "\n",
    "# Create the data\n",
    "X=heart_disease.drop(\"target\",axis=1)\n",
    "y=heart_disease[\"target\"] \n",
    "\n",
    "# Split into train and test\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2)\n",
    "\n",
    "# Instantiate and fit the model (on the training set)\n",
    "model=LinearSVC()\n",
    "model.fit(X_train,y_train)\n",
    "\n",
    "# Check the score of the model (on the test set)\n",
    "model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "a1026751-a04d-4197-a89b-d36cdabcda53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "target\n",
       "1    165\n",
       "0    138\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart_disease[\"target\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "a2b254e8-87e7-4e59-9348-5eed17c0a73d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8524590163934426"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the RandomForestClassifier estimator class\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Setup random seed\n",
    "np.random.seed(42)\n",
    "\n",
    "# Create the data\n",
    "X=heart_disease.drop(\"target\",axis=1)\n",
    "y=heart_disease[\"target\"] \n",
    "\n",
    "# Split into train and test\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2)\n",
    "\n",
    "# Instantiate and fit the model (on the training set)\n",
    "model=RandomForestClassifier()\n",
    "model.fit(X_train,y_train)\n",
    "\n",
    "# Check the score of the model (on the test set)\n",
    "model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a937502c-b298-4bc2-912c-867874b96ffc",
   "metadata": {},
   "source": [
    "Note:\n",
    "* if you have structured data, use ensemble methods\n",
    "* if you have unstructured data, use deep learning or transfer learning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e183e364-4d23-4f72-aeae-00c44ff18ad0",
   "metadata": {},
   "source": [
    "## 3. Fit the model/algorithm and use it to make predictions on our data\n",
    "\n",
    "### 3.1 Fitting the model to the data\n",
    "\n",
    "Different names for:\n",
    "* `X` = features, feature variables, data\n",
    "* `y` = labels, targets, target variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "edbc8edd-fd8a-4324-9a08-d7c2c9f2a65f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8524590163934426"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the RandomForestClassifier estimator class\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Setup random seed\n",
    "np.random.seed(42)\n",
    "\n",
    "# Create the data\n",
    "X=heart_disease.drop(\"target\",axis=1)\n",
    "y=heart_disease[\"target\"] \n",
    "\n",
    "# Split into train and test\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2)\n",
    "\n",
    "# Instantiate and fit the model (on the training set)\n",
    "model=RandomForestClassifier()\n",
    "\n",
    "# fit the model to the data (training the ml model)\n",
    "model.fit(X_train,y_train)\n",
    "\n",
    "# Check the score of the model (on the test set)(use the patterns the model hs learned)\n",
    "model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f219a31-c214-4136-8070-16a24827f9d2",
   "metadata": {},
   "source": [
    "## 3.2 Make predictions using a ml model\n",
    "2 ways to make predictions:\n",
    "1. `predict()`\n",
    "2. `predict_proba()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "6c18b37b-2e5f-4c16-9f4f-82568db586b6",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Expected 2D array, got 1D array instead:\narray=[1. 3. 4. 9. 8.].\nReshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[82], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# Use a trained model to make predictions\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m clf\u001b[38;5;241m.\u001b[39mpredict(np\u001b[38;5;241m.\u001b[39marray([\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m3\u001b[39m,\u001b[38;5;241m4\u001b[39m,\u001b[38;5;241m9\u001b[39m,\u001b[38;5;241m8\u001b[39m]))\n",
      "File \u001b[1;32m~\\codesss\\miniconda_projects\\env\\Lib\\site-packages\\sklearn\\ensemble\\_forest.py:820\u001b[0m, in \u001b[0;36mForestClassifier.predict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    799\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpredict\u001b[39m(\u001b[38;5;28mself\u001b[39m, X):\n\u001b[0;32m    800\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    801\u001b[0m \u001b[38;5;124;03m    Predict class for X.\u001b[39;00m\n\u001b[0;32m    802\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    818\u001b[0m \u001b[38;5;124;03m        The predicted classes.\u001b[39;00m\n\u001b[0;32m    819\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 820\u001b[0m     proba \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpredict_proba(X)\n\u001b[0;32m    822\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_outputs_ \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m    823\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclasses_\u001b[38;5;241m.\u001b[39mtake(np\u001b[38;5;241m.\u001b[39margmax(proba, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m), axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n",
      "File \u001b[1;32m~\\codesss\\miniconda_projects\\env\\Lib\\site-packages\\sklearn\\ensemble\\_forest.py:862\u001b[0m, in \u001b[0;36mForestClassifier.predict_proba\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    860\u001b[0m check_is_fitted(\u001b[38;5;28mself\u001b[39m)\n\u001b[0;32m    861\u001b[0m \u001b[38;5;66;03m# Check data\u001b[39;00m\n\u001b[1;32m--> 862\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_X_predict(X)\n\u001b[0;32m    864\u001b[0m \u001b[38;5;66;03m# Assign chunk of trees to jobs\u001b[39;00m\n\u001b[0;32m    865\u001b[0m n_jobs, _, _ \u001b[38;5;241m=\u001b[39m _partition_estimators(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_estimators, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_jobs)\n",
      "File \u001b[1;32m~\\codesss\\miniconda_projects\\env\\Lib\\site-packages\\sklearn\\ensemble\\_forest.py:602\u001b[0m, in \u001b[0;36mBaseForest._validate_X_predict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    599\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    600\u001b[0m \u001b[38;5;124;03mValidate X whenever one tries to predict, apply, predict_proba.\"\"\"\u001b[39;00m\n\u001b[0;32m    601\u001b[0m check_is_fitted(\u001b[38;5;28mself\u001b[39m)\n\u001b[1;32m--> 602\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_data(X, dtype\u001b[38;5;241m=\u001b[39mDTYPE, accept_sparse\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcsr\u001b[39m\u001b[38;5;124m\"\u001b[39m, reset\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m    603\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m issparse(X) \u001b[38;5;129;01mand\u001b[39;00m (X\u001b[38;5;241m.\u001b[39mindices\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m!=\u001b[39m np\u001b[38;5;241m.\u001b[39mintc \u001b[38;5;129;01mor\u001b[39;00m X\u001b[38;5;241m.\u001b[39mindptr\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m!=\u001b[39m np\u001b[38;5;241m.\u001b[39mintc):\n\u001b[0;32m    604\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo support for np.int64 index based sparse matrices\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[1;32m~\\codesss\\miniconda_projects\\env\\Lib\\site-packages\\sklearn\\base.py:565\u001b[0m, in \u001b[0;36mBaseEstimator._validate_data\u001b[1;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[0;32m    563\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mValidation should be done on X, y or both.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    564\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m no_val_X \u001b[38;5;129;01mand\u001b[39;00m no_val_y:\n\u001b[1;32m--> 565\u001b[0m     X \u001b[38;5;241m=\u001b[39m check_array(X, input_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mX\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcheck_params)\n\u001b[0;32m    566\u001b[0m     out \u001b[38;5;241m=\u001b[39m X\n\u001b[0;32m    567\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m no_val_X \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m no_val_y:\n",
      "File \u001b[1;32m~\\codesss\\miniconda_projects\\env\\Lib\\site-packages\\sklearn\\utils\\validation.py:902\u001b[0m, in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[0;32m    900\u001b[0m     \u001b[38;5;66;03m# If input is 1D raise error\u001b[39;00m\n\u001b[0;32m    901\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m array\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m--> 902\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    903\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExpected 2D array, got 1D array instead:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124marray=\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    904\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mReshape your data either using array.reshape(-1, 1) if \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    905\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124myour data has a single feature or array.reshape(1, -1) \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    906\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mif it contains a single sample.\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(array)\n\u001b[0;32m    907\u001b[0m         )\n\u001b[0;32m    909\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dtype_numeric \u001b[38;5;129;01mand\u001b[39;00m array\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;241m.\u001b[39mkind \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUSV\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m    910\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    911\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdtype=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnumeric\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m is not compatible with arrays of bytes/strings.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    912\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mConvert your data to numeric values explicitly instead.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    913\u001b[0m     )\n",
      "\u001b[1;31mValueError\u001b[0m: Expected 2D array, got 1D array instead:\narray=[1. 3. 4. 9. 8.].\nReshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample."
     ]
    }
   ],
   "source": [
    "# Use a trained model to make predictions\n",
    "clf.predict(np.array([1,3,4,9,8])) # this doesn't work..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "1912013b-55d0-4628-9c7d-6e827910f8e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>57</td>\n",
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       "      <td>276</td>\n",
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       "      <td>1</td>\n",
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       "      <td>170</td>\n",
       "      <td>288</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>159</td>\n",
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       "      <td>0</td>\n",
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       "      <th>111</th>\n",
       "      <td>57</td>\n",
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       "      <td>56</td>\n",
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       "      <td>1.9</td>\n",
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       "      <td>265</td>\n",
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       "      <td>130</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>69</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>140</td>\n",
       "      <td>254</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>146</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>129</td>\n",
       "      <td>196</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
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       "      <td>144</td>\n",
       "      <td>193</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>3.4</td>\n",
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       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>60</td>\n",
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       "      <td>145</td>\n",
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       "      <td>0</td>\n",
       "      <td>142</td>\n",
       "      <td>1</td>\n",
       "      <td>2.8</td>\n",
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       "      <td>2</td>\n",
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       "      <th>184</th>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
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       "      <td>150</td>\n",
       "      <td>243</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "179   57    1   0       150   276    0        0      112      1      0.6   \n",
       "228   59    1   3       170   288    0        0      159      0      0.2   \n",
       "111   57    1   2       150   126    1        1      173      0      0.2   \n",
       "246   56    0   0       134   409    0        0      150      1      1.9   \n",
       "60    71    0   2       110   265    1        0      130      0      0.0   \n",
       "..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
       "249   69    1   2       140   254    0        0      146      0      2.0   \n",
       "104   50    1   2       129   196    0        1      163      0      0.0   \n",
       "300   68    1   0       144   193    1        1      141      0      3.4   \n",
       "193   60    1   0       145   282    0        0      142      1      2.8   \n",
       "184   50    1   0       150   243    0        0      128      0      2.6   \n",
       "\n",
       "     slope  ca  thal  \n",
       "179      1   1     1  \n",
       "228      1   0     3  \n",
       "111      2   1     3  \n",
       "246      1   2     3  \n",
       "60       2   1     2  \n",
       "..     ...  ..   ...  \n",
       "249      1   3     3  \n",
       "104      2   0     2  \n",
       "300      1   2     3  \n",
       "193      1   2     3  \n",
       "184      1   0     3  \n",
       "\n",
       "[61 rows x 13 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "7d6aa29a-b01c-4240-9595-20de41ef2e5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0,\n",
       "       1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "72d672fa-bd61-4528-8197-2311cee19c39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0,\n",
       "        0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0]], dtype=int64)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([y_test]) # to display as formatted in the array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "aa93463e-460f-4e34-b1d0-6e9a6bf6842d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8524590163934426"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compare predictions to truth labels to evaluate the model\n",
    "y_preds=model.predict(X_test)\n",
    "np.mean(y_preds==y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "9f2cbca6-7794-41d1-8dc0-651958989bf0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8524590163934426"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "6ffe119b-3ebf-46d1-bce4-0e3a40e3a3ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8524590163934426"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(y_test,y_preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4650ae09-755f-4b61-a0f0-0e37099f23c4",
   "metadata": {},
   "source": [
    "Make predictions with `predict_proba()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "10359bae-fb20-427a-ac9b-92d54eb09d06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.89, 0.11],\n",
       "       [0.49, 0.51],\n",
       "       [0.43, 0.57],\n",
       "       [0.84, 0.16],\n",
       "       [0.18, 0.82]])"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# predict_proba() returns probabilities of a classification label(basically tells the probability of both the labels, highest one is selected)\n",
    "model.predict_proba(X_test[0:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "15e70d2d-c2d0-4164-9c20-857956e21439",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 0, 1], dtype=int64)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's predict() on the same data....\n",
    "model.predict(X_test[0:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "7e1ac940-71f0-4929-92d4-150166162006",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "target\n",
       "1    165\n",
       "0    138\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart_disease[\"target\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "380c7d67-45fe-4bb1-b117-327ab12fca15",
   "metadata": {},
   "source": [
    "`predict()` can also be used fo regression models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "38ae5291-944d-427a-b927-4d94b4eb9773",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.3252</td>\n",
       "      <td>41.0</td>\n",
       "      <td>6.984127</td>\n",
       "      <td>1.023810</td>\n",
       "      <td>322.0</td>\n",
       "      <td>2.555556</td>\n",
       "      <td>37.88</td>\n",
       "      <td>-122.23</td>\n",
       "      <td>4.526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.3014</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.238137</td>\n",
       "      <td>0.971880</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>2.109842</td>\n",
       "      <td>37.86</td>\n",
       "      <td>-122.22</td>\n",
       "      <td>3.585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.2574</td>\n",
       "      <td>52.0</td>\n",
       "      <td>8.288136</td>\n",
       "      <td>1.073446</td>\n",
       "      <td>496.0</td>\n",
       "      <td>2.802260</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.24</td>\n",
       "      <td>3.521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.6431</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5.817352</td>\n",
       "      <td>1.073059</td>\n",
       "      <td>558.0</td>\n",
       "      <td>2.547945</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>3.413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.8462</td>\n",
       "      <td>52.0</td>\n",
       "      <td>6.281853</td>\n",
       "      <td>1.081081</td>\n",
       "      <td>565.0</td>\n",
       "      <td>2.181467</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>3.422</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
       "0  8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88   \n",
       "1  8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86   \n",
       "2  7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85   \n",
       "3  5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85   \n",
       "4  3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85   \n",
       "\n",
       "   Longitude  target  \n",
       "0    -122.23   4.526  \n",
       "1    -122.22   3.585  \n",
       "2    -122.24   3.521  \n",
       "3    -122.25   3.413  \n",
       "4    -122.25   3.422  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "99e0c1be-be5a-4b80-bf59-5f19512662d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8065734772187598"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "#create the data\n",
    "X=housing_df.drop(\"target\",axis=1)\n",
    "y=housing_df['target']\n",
    "\n",
    "# split into training and test sets\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2)\n",
    "\n",
    "# create model instance\n",
    "clf=RandomForestRegressor()\n",
    "\n",
    "# fit the data\n",
    "clf.fit(X_train,y_train)\n",
    "\n",
    "# check score\n",
    "clf.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "a0c5ef9f-d182-45dc-8b54-ec1c45f62032",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.49384  , 0.75494  , 4.9285964, ..., 4.8363785, 0.71782  ,\n",
       "       1.67901  ])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_preds=clf.predict(X_test)\n",
    "y_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "ef54ad19-ecf3-4a2c-a0ba-99e3c513229f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.477  , 0.458  , 5.00001, ..., 5.00001, 0.723  , 1.515  ])"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "2a08a33f-9fb3-4eb4-80a0-e812cc9a4139",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.32659871732073664"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# compare the predictions to the truth\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "mean_absolute_error(y_test,y_preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3885ddd2-7615-454a-b948-272341bdbee0",
   "metadata": {},
   "source": [
    "## 4. Evaluating a ML model\n",
    "\n",
    "3 ways to evaluate Scikit-Learn models/estimators:\n",
    "1. Estimators's built-in `score()` method\n",
    "2. The `scoring` parameter\n",
    "3. Problem-specific metric functions\n",
    "\n",
    "Resource: https://scikit-learn.org/stable/modules/model_evaluation.html"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3fd2062-24f5-4a77-ab21-50f9d50f1c47",
   "metadata": {},
   "source": [
    "### 4.1 Evaluating a model with the`score` method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "ed4ca175-18af-4db4-88f0-e45a8bfce1bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "np.random.seed(42)\n",
    "\n",
    "# create X & y\n",
    "X=heart_disease.drop(\"target\",axis=1)\n",
    "y=heart_disease['target']\n",
    "\n",
    "# create train/test\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2)\n",
    "\n",
    "# create classifier model instance\n",
    "model=RandomForestClassifier()\n",
    "\n",
    "# Fit classifier to training data\n",
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "9fcc9ab8-a8ac-4736-8da0-140a74a31891",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The highest value for the .score() method is 1.0, the lowest is 0.0\n",
    "model.score(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "27b06ac2-80db-4157-86f3-12501cb39643",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8524590163934426"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a22359b1-aac1-4911-bf7e-9c906f6c727f",
   "metadata": {},
   "source": [
    "Let's use the `score()` on our regression problem.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "a72f34e0-4de3-4ddb-8b96-c9f4fb7b27ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "np.random.seed(42)\n",
    "\n",
    "# create X & y\n",
    "X=heart_disease.drop(\"target\",axis=1)\n",
    "y=heart_disease['target']\n",
    "\n",
    "# create train/test\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2)\n",
    "\n",
    "# create classifier model instance\n",
    "model=RandomForestRegressor(n_estimators=100)\n",
    "\n",
    "# Fit classifier to training data\n",
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "c75a532a-02cd-4f45-b4d7-dba30c753d87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.924203269641995"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the default score() evaluation metric is r-squared for regression algorithms\n",
    "model.score(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "bce2439a-dc6f-4b78-8178-41d0cd14e88e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5106393318965518"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33f00ecc-f551-44ac-b970-d1e262e183d4",
   "metadata": {},
   "source": [
    "### 4.2 Evaluating a model with the`scoring` parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "e28b2d21-85f2-451f-bc1c-96fef29df640",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "np.random.seed(42)\n",
    "\n",
    "# create X & y\n",
    "X=heart_disease.drop(\"target\",axis=1)\n",
    "y=heart_disease['target']\n",
    "\n",
    "# create train/test\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2)\n",
    "\n",
    "# create classifier model instance\n",
    "model=RandomForestClassifier()\n",
    "\n",
    "# Fit classifier to training data\n",
    "model.fit(X_train,y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "704a25df-8fc2-4d32-bdd4-e90e78f93698",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8524590163934426"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "0aac508d-1fe9-4951-b5f6-06cbe3dc6bf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.81967213, 0.86885246, 0.81967213, 0.78333333, 0.76666667])"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(model,X,y,cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "92fac691-f728-46a4-8360-54c9bdb72bc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.90322581, 0.80645161, 0.87096774, 0.9       , 0.86666667,\n",
       "       0.8       , 0.73333333, 0.86666667, 0.73333333, 0.8       ])"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(model,X,y,cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "3b212efb-2350-4b2f-b4c0-383d21b97df4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.8524590163934426, 0.8248087431693989)"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "\n",
    "# single training and test split score\n",
    "model_single_score=model.score(X_test,y_test)\n",
    "\n",
    "# take the mean of the 5-fold cross-validation score\n",
    "model_cross_val_score=np.mean(cross_val_score(model,X,y,cv=5))\n",
    "\n",
    "# Compare the two\n",
    "model_single_score, model_cross_val_score\n",
    "# note: always the cross-validation score is preferred than a single score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "6ea0913d-a817-4212-8644-486be7e424b1",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "ClassifierMixin.score() missing 2 required positional arguments: 'X' and 'y'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[108], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# default scoring parameter of classifier = mean accuracy\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m model\u001b[38;5;241m.\u001b[39mscore()\n",
      "\u001b[1;31mTypeError\u001b[0m: ClassifierMixin.score() missing 2 required positional arguments: 'X' and 'y'"
     ]
    }
   ],
   "source": [
    "# default scoring parameter of classifier = mean accuracy\n",
    "model.score()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "225594b6-f159-460e-9754-276ee7ebdcc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.78688525, 0.86885246, 0.80327869, 0.78333333, 0.76666667])"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# scoring parameter set to None by default\n",
    "cross_val_score(model,X,y,scoring=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ac4a454-e1f2-4378-acc3-0b125c85c02d",
   "metadata": {},
   "source": [
    "### 4.2.1 Classification model evaluation metrics\n",
    "\n",
    "1. Accuracy - shows how accurately the score is evaluated\n",
    "2. Area under Receiver Operating Characteristic curve (AUC/ROC)\n",
    "3. Confustion matrix\n",
    "4. Classfication report\n",
    "\n",
    "**Accuracy**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "4d49cfab-a245-42cf-a03e-eb3d1189bf35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  "
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart_disease.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "7a0a9949-df8c-4510-8157-39d34dbb9e40",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "X=heart_disease.drop(\"target\",axis=1)\n",
    "y=heart_disease['target']\n",
    "\n",
    "clf=RandomForestClassifier()\n",
    "cross_val_score=cross_val_score(clf,X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "b6ffb994-a635-45ec-aa2a-66c604c134c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8248087431693989"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(cross_val_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "e738d12d-5a3e-4a2c-8a0d-f1d6fb1ff570",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Heart Disease Classifier Cross-Validated Accuracy: 82.48% \n"
     ]
    }
   ],
   "source": [
    "print(f\"Heart Disease Classifier Cross-Validated Accuracy: {np.mean(cross_val_score)*100:.2f}% \")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c092fe81-83d2-484e-9d6a-354138df6020",
   "metadata": {},
   "source": [
    "**Area under the Receiver Operating Characteristic curve (AUC/ROC)**\n",
    "* Area under curve (AUC)\n",
    "* ROC curve\n",
    "\n",
    "ROC curves are a comparison of a model's true positive rate (tpr) v/s a models false positive rate (fpr).\n",
    "\n",
    "* True positive = model predicts 1 when truth is 1\n",
    "* False positive = model predicts 1 when truth is 0\n",
    "* True negative = model predicts 0 when truth is 0\n",
    "* False negative = model predicts 0 when truth is 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "71e5faba-81e3-4383-bfbb-dae00915d386",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create X_test... etc\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "f65962a2-e1e9-440d-86cc-955023c3b345",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[0.51, 0.49],\n",
       "        [0.17, 0.83],\n",
       "        [0.51, 0.49],\n",
       "        [0.72, 0.28],\n",
       "        [0.43, 0.57],\n",
       "        [0.12, 0.88],\n",
       "        [0.3 , 0.7 ],\n",
       "        [0.97, 0.03],\n",
       "        [0.15, 0.85],\n",
       "        [0.4 , 0.6 ]]),\n",
       " 61)"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "\n",
    "# fit the model\n",
    "clf.fit(X_train,y_train)\n",
    "\n",
    "# Make predictions with probabilities\n",
    "y_probs=clf.predict_proba(X_test)\n",
    "\n",
    "y_probs[:10],len(y_probs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "1fa87da0-1e2a-4b46-96a9-e258d96588ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.51, 0.17, 0.51, 0.72, 0.43, 0.12, 0.3 , 0.97, 0.15, 0.4 ]),\n",
       " array([0.49, 0.83, 0.49, 0.28, 0.57, 0.88, 0.7 , 0.03, 0.85, 0.6 ]))"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_probs_positive=y_probs[:,1]\n",
    "y_probs_negative=y_probs[:,0]\n",
    "y_probs_negative[:10],y_probs_positive[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "2bb0f8e0-9260-44bf-a128-1ef031d319f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.03448276, 0.03448276, 0.03448276, 0.03448276,\n",
       "       0.03448276, 0.03448276, 0.06896552, 0.06896552, 0.06896552,\n",
       "       0.10344828, 0.10344828, 0.13793103, 0.13793103, 0.13793103,\n",
       "       0.20689655, 0.20689655, 0.20689655, 0.27586207, 0.37931034,\n",
       "       0.37931034, 0.48275862, 0.48275862, 0.55172414, 0.55172414,\n",
       "       1.        ])"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate fpr,tpr and thresholds\n",
    "fpr,tpr,thresholds=roc_curve(y_test,y_probs_positive)\n",
    "\n",
    "# check the fprs\n",
    "fpr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "e5aa12e9-5c36-4920-985e-9726676db1ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a function for plotting ROC curves\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def plot_roc_curve(fpr,tpr):\n",
    "    \"\"\"\n",
    "    Plots a ROC curve given the false positive rate (fpr) and \n",
    "    True positive rate (tpr) of a model.\n",
    "    \"\"\"\n",
    "    # plot roc curve\n",
    "    plt.plot(fpr,tpr,color=\"orange\",label=\"ROC\")\n",
    "    # plot line with no predictive power (baseline)\n",
    "    plt.plot([0,1],[0,1],color=\"darkblue\", linestyle=\"--\",label= \"Guessing\")\n",
    "    # customize the plot\n",
    "    plt.xlabel(\"False positive rate (fpr)\")\n",
    "    plt.ylabel(\"True positive rate (tpr)\")\n",
    "    plt.title(\"Receiver Operating Characteristic (ROC) curve\")\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "plot_roc_curve(fpr,tpr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "98fc5dc9-48b6-4330-9134-66cf7d843218",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8669181034482759"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "roc_auc_score(y_test,y_probs_positive)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "04879909-7d3c-4a71-8243-6a9d849e4c9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot perfect ROC curve and AUC score\n",
    "fpr,tpr,thresolds=roc_curve(y_test,y_test)\n",
    "plot_roc_curve(fpr,tpr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "7c8454ab-502d-4808-a27e-030e45104301",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# perfect AUC score \n",
    "roc_auc_score(y_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccbce25c-271c-414c-9f2d-cd45ed8cddab",
   "metadata": {},
   "source": [
    "**Confusion Matrix**\n",
    "\n",
    "A confusion matrix is a quick way to compare the labels a model predicts and the actual labels it was supposed to predict.\n",
    "\n",
    "In essence, giving you an idea of where the model is getting confused."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "a7aaf553-e36f-4903-a04e-311b859c7b39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[23,  6],\n",
       "       [ 6, 26]], dtype=int64)"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "y_preds=clf.predict(X_test)\n",
    "\n",
    "confusion_matrix(y_test,y_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "26c4cdfd-be18-4d0c-819e-e60a89183b4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Predicted Labels</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual Labels</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted Labels   0   1\n",
       "Actual Labels           \n",
       "0                 23   6\n",
       "1                  6  26"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# visualize confusion matrix with pd.crosstab()\n",
    "pd.crosstab(y_test,\n",
    "            y_preds,\n",
    "            rownames=[\"Actual Labels\"],\n",
    "            colnames=[\"Predicted Labels\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "c205836f-3fa9-42e5-918e-6b76080e0c86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "61"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "23+6+6+26"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "af4abbc0-0abc-4957-b945-ce8b8e6bf6fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "61"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "d693b93c-01db-414b-8026-6adf223db32a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Channels:\n",
      " - defaults\n",
      "Platform: win-64\n",
      "Collecting package metadata (repodata.json): ...working... done\n",
      "Solving environment: ...working... done\n",
      "\n",
      "# All requested packages already installed.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# to install a module directly from jupyter notebook\n",
    "import sys\n",
    "!conda install --yes --prefix {sys.prefix} seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "f31557e1-c68c-4f6b-b1c5-671399d394ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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g5wQAAJgKlRMAAIyicuIShBMAAIxqPr8A06ywrQMAAEyFygkAAEaxreMShBMAAIziVWKXYFsHAACYiqHKSUlJiQ4cOKCsrCwVFBSotLRUNptNXl5eCgoKUvfu3TV48GAFBAQ09noBADAPToh1CafCyf/+7/9q2bJl+vDDD3X16lXZ6uhStlgscnd3V0xMjGbPni0fH59GWSwAAKbCto5LOBxOysrKNG7cOH377bfy9PTUAw88oK5duyooKEienp6SpPLychUUFOjkyZM6ePCgtmzZom+++UYJCQkEFAAA4BCHw8maNWuUlZWlYcOGadGiRfL39693fFFRkebNm6eUlBRt2LBBM2bMuNG1AgBgKjbe1nEJhxti9+7dq6CgIC1fvrzBYCJJ/v7+WrZsme644w7t3bv3RtYIAIA5Vdka5w/XcTic/Pjjj4qMjJSHh4fDk7dp00aRkZH64YcfDC0OAABTs1U1zh+u43A4CQwM1I8//uj0A06fPi1vb2+n7wMAALcmh8NJ3759lZGRoY8//tjhybds2aLjx49rwIABhhYHAICpsa3jEg43xM6YMUPJycmaN2+eEhMT9fDDD6tbt24KDg6Wl5eXLBaLysrKdOHCBZ08eVKfffaZUlNT5eXlRTMsAKBloiHWJRwOJ506ddLGjRs1e/ZspaWl6csvv6x3vM1mU/v27bVkyRJ16dLlhhcKAABuDU4dwtanTx8lJSUpMTFRKSkpys7Orj4htlWrVvLx8VFwcLC6d++uoUOH6pFHHlGbNm1ctXYAAJoWWzIu4fTx9W5ubnryySf15JNPumI9AAA0H7xp4xL88B8AADAVQz/8BwAAxLaOixBOAAAwyEzH1+/cuVPbt29XVlaWysrKFBgYqPvvv1+TJ09WREREjfFffvml1q1bp6ysLJWXlys8PFzPPPOMRo8eLYvF4tSzMzMztWrVKh09elSXLl1SWFiYoqOjNX78eLm7uzv9XdjWAQCgGbPZbHr55Zc1Z84cHT58WBEREXrooYfk5uamTz75RKNGjVJqaup19/zjH//QhAkTlJ6errvuuksDBw5UTk6OXn31Vc2bN8+p56ekpOiZZ57R559/rk6dOmnIkCEqKCjQ0qVLNWXKFF29etXp70TlBAAAo0ywrbNr1y4lJibqjjvu0Pr169WjRw9JUmVlpVasWKE1a9Zozpw52rdvn3x8fPT999/rzTfflNVq1aZNm3TXXXdJks6ePavx48fro48+0tChQzVixIgGn11cXKz4+HhJ0rp16zRkyBBJv/z47+TJk5WWlqaEhARNmjTJqe9E5QQAAKNMcELs9u3bJUkvv/xydTCRfnm7Ni4uTl27dlVhYaHS0tIkSevXr1dlZaUmTZpUHUwkqX379lqwYEH1GEds3rxZly9fVnR0dHUwkX758d/FixdLkjZs2KDKykqnvhPhBAAAo0zww39+fn6KiIhQv379alyzWCzq3LmzJOn8+fOSftmGkaRHH320xvgHHnhAvr6+yszMrB5fn/3799c5V0REhLp166YLFy4oIyPD8S8kwgkAAM3aypUr9emnnyosLKzGtcrKSh07dkySFBISoosXL6qwsFDu7u4KDw+vMd7Nza3682+//bbBZ588eVKS1K1bt1qv20+Iz8rKcuzL/H/0nAAAYFQj9ZwMHz683uvJycmG5t2yZYvOnDkjf39/DRo0SHl5eZKktm3bqlWr2usTQUFBkqSCgoJ65y4uLlZ5ebkkKTg4+Ibm+i3CCQAABtlM0BBbl6+++kpvv/22JCk+Pl4+Pj4qLS2VJHl6etZ5n/1nZ+xj62K/7uHhUWfQsT+nobl+i3ACAEATM1oZqUtKSori4uJUUVGhsWPHKiYmRpKqQ4Qj55jYbPUHr8ac67cIJwAAGGXCysmmTZu0ePFiVVZWaty4cXrttdeqr/n4+EhS9XZMba5cuSJJ8vb2rvc59rmuXLmiqqqqWqsn9ud4eXk59R0IJwAAGGWiE2KvXbumhQsXauvWrbJYLHrppZf04osvXjfG3hty8eJF2Wy2Wqse9v4Qe79IXaxWq6xWqy5fvqwLFy7U2nfi6Fy/xds6AAA0c+Xl5ZoyZYq2bt0qLy8vLV++vEYwkX45fyQ4OFgVFRXVzbG/VllZqe+++06S1L179wafa39L59SpU7Vet3/uyFy/RjgBAMAoExzCVllZqenTpys1NVWBgYFKSEio93TXoUOHSpI+++yzGtfS0tJUUlKiHj16qF27dg0+2z5XUlJSjWs5OTnKzs5WQECA7rnnHke/jiTCCQAAxpkgnKxevVqpqany9vbWxo0b1adPn3rHjxs3Tm5ublq7dq2++eab6s/Pnj2rN998U5I0ZcqU6+4pKSlRTk6O8vPzr/t81KhRslqt2rFjx3VNvUVFRdW/0TNx4kSnf/zPYnO2hbYJle16p6mXAJiS7+hlTb0EwHSuVZxx+TNKXmz492cc4btmr6H7iouLFRUVpdLSUnXq1Em9e/euc+zIkSOrKx1/+9vf9O6778rNzU0DBgyQp6enDh48qNLSUsXExOjPf/7zdfd+9NFHmjt3rkJDQ6tPhbXbvXu35syZI5vNpsjISAUGBio9PV1FRUV68MEHtXr1aqfDCQ2xAAAY1NT/fn/o0KHqM0Ryc3OVm5tb59iePXtWh5PJkycrIiJC77//vjIyMmSxWBQREaGxY8fqqaeecmoNI0eOVEhISHUlJisrS2FhYZo6dapiY2OdDiYSlROgRaByAtR0Myonl/5Q8zdljPBbV7P/41ZG5QQAAKNMeM5JS0BDLAAAMBUqJwAAGGTm39ZpzggnAAAYRThxCbZ1AACAqVA5AQDAKPP8tE6LQjgBAMAgek5cg20dAABgKlROAAAwisqJSxBOAAAwip4Tl2BbBwAAmAqVEwAADKIh1jUIJwAAGMW2jksQTgAAMIjKiWvQcwIAAEyFygkAAEaxreMShBMAAAyyEU5cgm0dAABgKlROAAAwisqJSxBOAAAwiG0d12BbBwAAmAqVEwAAjKJy4hKEEwAADGJbxzXY1gEAAKZC5QQAAIOonLgG4QQAAIMIJ65BOAEAwCibpalX0CLRcwIAAEyFygkAAAaxreMahBMAAAyyVZlzWyc3N1fR0dEaNWqUFixYUP35Dz/8oOHDhzs0x4wZM/Qf//EfDY5LTU3VpEmT6rzu7e2tI0eOOPRMO8IJAAAtyMWLFzVt2jSVlZXVuObt7a2RI0fWeW9RUZEOHDggSerZs6dDzzt+/LgkqXfv3urUqVON623atHFonl8jnAAAYJDZtnVOnDihWbNmKS8vr9brAQEBeuedd2q9VlVVpRdeeEGS9OKLL+rhhx926JlHjx6VJMXFxWnIkCEGVl0T4QQAAINsJnlbp7i4WH/729+UkJCgiooKdejQQT/88INTc6xatUppaWm69957NXPmTIfvs1dOevXq5dTz6sPbOgAANHMJCQlav369AgICtHr1akVHRzt1f25urtasWSN3d3f95S9/kZubm0P3Xbp0SadPn1bHjh3l7+/v/MLrQOUEAACDzLKt065dO73yyiuKjY2Vp6enjh075tT9b775pq5evaqJEyeqS5cuDt9nf07Hjh21cuVK7d27V/n5+bJarRo0aJCmTZum8PBwp9YiEU4AADCssd7WaegNmuTk5Hqvx8TEGH72oUOHlJqaKm9vb/3hD39w6l57OElNTVV6err69++vkJAQHTt2TLt371ZycrJWrVqlQYMGOTUv4QQAgFvY+vXrJUn//u//roCAAKfutfeb9O/fX8uXL1fbtm0lSRUVFVqyZIk2b96suLg47du3T35+fg7PSzgBAMAgm61x5mmoMuIqOTk5+uKLL+Tu7q6JEyc6ff+SJUs0Y8YMBQUFyWq1Vn/u4eGh+fPn6/Dhwzpx4oR27typZ5991uF5aYgFAMAgW5WlUf6aSmJiomw2mwYPHqygoCCn7/fw8FB4ePh1wcTOzc1NUVFRkqTMzEyn5qVyAgCAQWY9IdZRSUlJkqTf//73Lpk/JCREkmo9EK4+VE4AALgFnT59Wjk5OXJ3d3f4SPtfu3LliubPn6+pU6fq559/rnXMuXPnJP3yNpEzCCcAABhkszXOX1P417/+JUm6++675e3t7fT9bdq0UVpamvbv319rz0xFRYX27NkjSdXbO44inAAAYFBz7jmx94Hce++9DY4tKSlRTk6O8vPzr/s8NjZWkrR06VJlZWVVf15eXq65c+cqPz9f/fv31+DBg51aGz0nAADcgk6fPi3plwPUGrJv3z7NnTtXoaGh2r9/f/XnEyZM0OHDh5WSkqKnn35affv2lb+/v77++msVFhYqPDxcy5Ytc3pthBMAAAwyy2/rGPHTTz9Jcr4f5Nfc3d21atUqbdu2TTt27NDRo0dVVVWlsLAwxcbGauLEiYa2jCw2W1PtdjmvbFftv6QI3Op8Rzv/byZAS3et4ozLn3HqrscaZZ4ux5MaZZ6Wgp4TAABgKmzrAABgUFUz3tYxM8IJAAAGNeeeEzNjWwcAAJgKlRMAAAxq7sfXmxXhBAAAg5rP+67NC+EEAACDqJy4Bj0nAADAVKicAABgEK8SuwbhBAAAg3iV2DXY1gEAAKZC5QQAAIN4W8c1CCcAABhEz4lrsK0DAABMhcoJAAAG0RDrGoQTAAAMoufENdjWAQAApkLlBAAAg2iIdY1mFU58Ry9r6iUAplR29kBTLwG4JdFz4hrNKpwAAGAmVE5cg54TAABgKlROAAAwiJd1XINwAgCAQWzruAbbOgAAwFSonAAAYBBv67gG4QQAAIOqmnoBLRTbOgAAwFSonAAAYJBNbOu4AuEEAACDqniX2CUIJwAAtDC5ubmKjo7WqFGjtGDBghrXt27dWuvndl27dlViYqLDz8vMzNSqVat09OhRXbp0SWFhYYqOjtb48ePl7u7u9PoJJwAAGFRlwm2dixcvatq0aSorK6tzzLFjxyRJAwcOVFBQUI3rISEhDj8vJSVFM2bMUFVVlfr16yc/Pz+lp6dr6dKl+vLLL7V27VqnAwrhBAAAg8zWc3LixAnNmjVLeXl59Y6zh5M//elP6ty5s+HnFRcXKz4+XpK0bt06DRkyRJJUVFSkyZMnKy0tTQkJCZo0aZJT8/K2DgAABlU10t+NKi4u1tKlSzVmzBjl5eWpQ4cOdY69evWqsrOz5evrq06dOt3Qczdv3qzLly8rOjq6OphIkr+/vxYvXixJ2rBhgyorK52al3ACAEAzl5CQoPXr1ysgIECrV69WdHR0nWNPnTqliooK9erVSxbLjVV+9u/fL0l69NFHa1yLiIhQt27ddOHCBWVkZDg1L+EEAACDbLI0yt+NateunV555RUlJSVp2LBh9Y61b+kEBwfrrbfe0ogRI9SnTx8NHTpUr7/+us6fP+/wc0+ePClJ6tatW63Xu3TpIknKyspyeE6JnhMAAAxrrBNihw8fXu/15OTkeq/HxMQ4/Cx7OPnkk09ktVrVr18/hYSE6NixY/rggw+0b98+bdiwQd27d693nuLiYpWXl0v6JejUxt5sW1BQ4PD6JMIJAAC3lOPHj0uSHnvsMS1atEhWq1WSVFJSovnz5yspKUkzZ87Unj171Lp13TGhtLRUkuTh4aFWrWrfiPH09LxurKMIJwAAGNRYlZOGKiONaePGjTp9+rTuvPNOeXh4VH/u6+urxYsX68iRI8rNzdUXX3xR7xaRPZA40rdiszl3Wh09JwAAGGSWnhNneHp6qmvXrtcFEzsfHx/df//9kn45WK0+Pj4+kqQrV66oqqr2mGbf9vHy8nJqjYQTAABQzX4AW32HuEmS1Wqt3hK6cOFCrWPsvSa1HfRWH8IJAAAGVVka5+9mOX/+vObOnauZM2fWefbIuXPnJP3yBlBD7G/pnDp1qtbr9s8baq79LcIJAAAGVcnSKH83i6+vr/bs2aOkpCQdOnSoxvWioiKlpKTIYrHooYceanC+oUOHSpKSkpJqXMvJyVF2drYCAgJ0zz33OLVOwgkAALcIb2/v6gPa3njjDZ05c6b6WnFxsWbNmqWSkhI98cQTCg8Pr75WUlKinJwc5efnXzffqFGjZLVatWPHjuuaeouKijRv3jxJ0sSJE/ltHQAAbhbn3kExhzlz5ujEiRPKyMjQ448/rr59+8rT01Pp6ekqKSnRfffdp9dff/26e/bt26e5c+cqNDS0+lRY6ZdekjfeeENz5szR9OnTFRkZqcDAQKWnp6uoqEgPPvignn/+eafXSDgBAMCgxnqV+GayWq3avHmzNm7cqMTERH399ddyc3NT586d9cQTT2jcuHFOVTpGjhypkJAQrV27Vt98842ysrIUFhamqVOnKjY21umqiSRZbM6+fNyEWnuENvUSAFMqO3ugqZcAmI572/CGB92g7SHjGmWe0ec2N8o8LQU9JwAAwFTY1gEAwKBms/XQzBBOAAAwqDn2nDQHbOsAAABToXICAIBBN/N011sJ4QQAAINu5umutxK2dQAAgKlQOQEAwCDe1nENwgkAAAbRc+IabOsAAABToXICAIBBnHPiGoQTAAAMoufENQgnAAAYRM+Ja9BzAgAATIXKCQAABtFz4hqEEwAADCKcuAbbOgAAwFSonAAAYJCNhliXIJwAAGAQ2zquwbYOAAAwFSonAAAYROXENQgnAAAYxAmxrsG2DgAAMBUqJwAAGMTx9a5BOAEAwCB6TlyDcAIAgEGEE9eg5wQAAJgKlRMAAAzibR3XIJwAAGAQDbGuQTgBAKCFyc3NVXR0tEaNGqUFCxbUuF5WVqYNGzYoKSlJeXl5qqqqUmhoqIYPH64XXnhB/v7+Dj8rNTVVkyZNqvO6t7e3jhw54tT6CScAABhkxobYixcvatq0aSorK6v1elFRkZ599lllZ2fL19dXkZGRcnNzU2ZmptatW6dPP/1UW7ZsUbt27Rx63vHjxyVJvXv3VqdOnWpcb9OmjdPfgXACAIBBZus5OXHihGbNmqW8vLw6xyxdulTZ2dkaMGCA3nvvPQUEBEiSLl26pNmzZ+vAgQN67bXXtG7dOoeeefToUUlSXFychgwZcuNfQrytAwBAs1dcXKylS5dqzJgxysvLU4cOHWodV15ersTEREnSkiVLqoOJJPn5+emtt96SxWLRgQMH9PPPPzv0bHvlpFevXjf4Lf4P4QQAAIOqZGuUvxuVkJCg9evXKyAgQKtXr1Z0dHSt4woLC3X33Xerb9++Cg0NrXE9MDBQt912m2w2mwoKChp87qVLl3T69Gl17NjRqT6VhrCtAwCAQWbpOWnXrp1eeeUVxcbGytPTU8eOHat1XGhoqLZs2VLnPHl5eSoqKlKrVq0UHBzc4HPtz+nYsaNWrlypvXv3Kj8/X1arVYMGDdK0adMUHh7u9PchnAAA0MzFxMQ0yjzvvPOOJOmBBx5wqBJiDyepqalKT09X//79FRISomPHjmn37t1KTk7WqlWrNGjQIKfWQTgBAMCgxmqIHT58eL3Xk5OTG+lJdVuzZo0+++wzeXp6as6cOQ7dY+836d+/v5YvX662bdtKkioqKrRkyRJt3rxZcXFx2rdvn/z8/BxeCz0nAAAYVNVIf01txYoVWrZsmVq1aqVFixape/fuDt23ZMkS/dd//ZfWrFlTHUwkycPDQ/Pnz1fPnj1VVFSknTt3OrUeKicAABjUWCfE3ozKSG0qKir06quvaufOnWrdurUWL16s3/3udw7f7+HhUWdPiZubm6KionTixAllZmY6tS7CCQAAt6DCwkJNnz5dR44ckdVq1Xvvvddo55TYhYSESFKdB8LVhW0dAAAMMsurxM7Kz89XTEyMjhw5og4dOuiDDz5wOphcuXJF8+fP19SpU+s8E+XcuXOS5PBps3aEEwAADLI10t/NdP78eT333HM6c+aM+vTpow8//FBdu3Z1ep42bdooLS1N+/fvr3VbqqKiQnv27JEkRUVFOTU34QQAgFvIH//4R507d07du3fX+++/r8DAwAbvKSkpUU5OjvLz86/7PDY2VtIvR+JnZWVVf15eXq65c+cqPz9f/fv31+DBg51aIz0nAAAYZIY3bZyRlpamgwcPSpJ8fX31+uuv1zl21qxZCgsLkyTt27dPc+fOVWhoqPbv3189ZsKECTp8+LBSUlL09NNPq2/fvvL399fXX3+twsJChYeHa9myZU6vk3ACAIBBTdEvciNSUlKq//s///nPeseOHz++OpzUxd3dXatWrdK2bdu0Y8cOHT16VFVVVQoLC1NsbKwmTpwob29vp9dpsdlszeb/2dYeNX8HAIBUdvZAUy8BMB33ts4fm+6sVzqNbZR53sr9R6PM01JQOQEAwKBm82/3zQzhBAAAg5pbz0lzwds6AADAVKicAABgUHNriG0uCCcAABhENHENwgkAAAbRc+Ia9JwAAABToXICAIBBNjZ2XIJwAgCAQWzruAbbOgAAwFSonAAAYBCvErsG4QQAAIOIJq7Btg4AADAVKicAABjEto5rEE4AADCIt3Vcg20dAABgKlROAAAwiEPYXINwAgCAQWzruAbhBAAAg6icuAY9JwAAwFScqpz8+c9/vqGHvfrqqzd0PwAAZsK2jms4FU527Nih8vLy6v9tszlezrJYLIQTAECLUuXEPwfhOKfCyccff6xp06bpu+++U1hYmJ588klXrQsAANyinAonnTp10qZNmzR27FidPn1avXr1UlRUlIuWBgCAuVE3cQ2nG2IDAwP13nvvyd3dXQsXLlRFRYUr1gUAgOlVydYof7ieobd1evbsqWeffVZnz57Vli1bGntNAADgFmb4nJMpU6bIy8tLVqu1MdcDAECzwTknrmE4nPj5+WnGjBmNuRYAAJoVXiV2DQ5hAwAApsLx9QAAGEQzq2tQOQEAwCBbI/2nseXm5uree+/VwoUL6xzz5ZdfasKECRo0aJAiIyP19NNPa9u2bU4dsGqXmZmpqVOn6sEHH9Q999yj3//+91q/fr2uXr1qaP2EEwAADKpqpL/GdPHiRU2bNk1lZWV1jvnHP/6hCRMmKD09XXfddZcGDhyonJwcvfrqq5o3b55Tz0tJSdEzzzyjzz//XJ06ddKQIUNUUFCgpUuXasqUKYYCCts6AAC0ECdOnNCsWbOUl5dX55jvv/9eb775pqxWqzZt2qS77rpLknT27FmNHz9eH330kYYOHaoRI0Y0+Lzi4mLFx8dLktatW6chQ4ZIkoqKijR58mSlpaUpISFBkyZNcup7UDkBAMAgm83WKH83qri4WEuXLtWYMWOUl5enDh061Dl2/fr1qqys1KRJk6qDiSS1b99eCxYsqB7jiM2bN+vy5cuKjo6uDiaS5O/vr8WLF0uSNmzYoMrKSqe+D+EEAACDzHJCbEJCgtavX6+AgACtXr1a0dHRdY5NSUmRJD366KM1rj3wwAPy9fVVZmamzp8/3+Bz9+/fX+dcERER6tatmy5cuKCMjAwHv8kvCCcAADRz7dq10yuvvKKkpCQNGzasznEXL15UYWGh3N3dFR4eXuO6m5tb9efffvttg889efKkJKlbt261Xu/SpYskKSsrq8G5fo2eEwAADGqsZtbhw4fXez05Obne6zExMQ49p6CgQJLUtm1btWpVe30iKCjourF1KS4uVnl5uSQpODj4hub6LcIJAAAGNbfj60tLSyVJnp6edY5p06bNdWMbmsvDw6POoGN/TkNz/RbhBACAJtZQZaSx2EOExWJpcGxDjbqNOddvEU4AADCouZ0Q6+PjI0nV2zG1uXLliiTJ29vbobmuXLmiqqqqWqsn9ud4eXk5tU4aYgEAMMgsrxI7yt4bcvHixTqfa+8PsfeL1MVqtcpqtUqSLly4cENz/RbhBACAW4S/v7+Cg4NVUVFR60FtlZWV+u677yRJ3bt3b3A++1s6p06dqvW6/XNH5vo1wgkAAAaZ8fj6hgwdOlSS9Nlnn9W4lpaWppKSEvXo0UPt2rVzeK6kpKQa13JycpSdna2AgADdc889Tq2RcAIAgEFm/eG/+owbN05ubm5au3atvvnmm+rPz549qzfffFOSNGXKlOvuKSkpUU5OjvLz86/7fNSoUbJardqxY8d1Tb1FRUXVv9EzceJEubu7O7VGGmIBADCouTXESlKPHj0UFxend999V7GxsRowYIA8PT118OBBlZaWKiYmRo8//vh19+zbt09z585VaGho9amw0i+9JG+88YbmzJmj6dOnKzIyUoGBgUpPT1dRUZEefPBBPf/8806vkXACAMAtZvLkyYqIiND777+vjIwMWSwWRUREaOzYsXrqqaecmmvkyJEKCQmprsRkZWUpLCxMU6dOVWxsrNNVE0my2G5mm/ANau0R2tRLAEyp7OyBpl4CYDrubWsez97Yhneo+ZsyRiT/ULP/41ZG5QQAAIOa47ZOc0BDLAAAMBUqJwAAGNTcflunuSCcAABgUFXzadtsVtjWAQAApkLlBAAAg6ibuAbhBAAAg3hbxzUIJwAAGEQ4cQ16TgAAgKlQOQEAwKBmdMh6s0I4AQDAILZ1XINtHQAAYCpUTgAAMIgTYl2DcAIAgEH0nLgG2zoAAMBUqJwAAGAQDbGuQTgBAMAgtnVcg20dAABgKlROAAAwiG0d1yCcAABgEK8SuwbhBAAAg6roOXEJek4AAICpUDkBAMAgtnVcg3ACAIBBbOu4Bts6AADAVKicAABgENs6rkE4AQDAILZ1XINtHQAAYCpUTgAAMIhtHdcgnAAAYJAZtnWGDRumM2fONDhuwIAB2rRpU71jysvL1bdvX1VWVtY5Zvv27erdu7fT63QG4QQAgGbs4Ycf1k8//VTrNZvNpr179+ratWu6++67G5wrKytLlZWVat++ve67775ax/j7+9/Ich1COAEAwCAzbOvMmzevzmsrV67UtWvX1L9/f8XHxzc417FjxyRJI0eO1OzZsxttjc4inAAAYJDNVtXUS6jTwYMH9Z//+Z/y8/PTu+++q9atG/5Hvj2cuHrbpiGEEwAADKoyQeWkNhUVFVqwYIGqqqo0Z84cBQcHO3Qf4QQAALjE+++/r9zcXPXu3VujR4926J6Kigrl5OTI399fX331lbZu3apTp07JZrOpT58+euGFFzR48GAXr/wXFpvNBK3GDmrtEdrUSwBMqezsgaZeAmA67m3DXf6MjgGNU2HoGhlU7/Xk5GSH57p8+bKioqJUUlKidevW6aGHHnLovoyMDMXExEiSLBaL7r33XgUGBurUqVPKzc2VJL388suaPHmyw2sxisoJAAAGmXFb54MPPlBJSYnuvvtuh4OJJB0/flySFBQUpFWrVl23tfPxxx9r/vz5+utf/6rIyEj179+/0df9a4QTAACamDOVkfpUVlYqISFBkpyucIwZM0YPPfSQ3NzcavSoPPXUUzp27Jg2bdqkhIQEl4cTjq8HAMAgm83WKH+N5dChQzp//rxuu+02DRs2zKl7W7Vqpfbt29fZPDt8+HBJUmZm5g2vsyFUTgAAMMgMJ8T+WlJSkiTpsccek4eHR6PO3a5dO0lSWVlZo85bGyonAAC0EP/zP/8jSRoxYoTT965atUozZ87UV199Vev1H3/8UdL/hRRXonICAIBBZjgh1u7ChQs6e/asWrdurcjISKfv//7775WUlCRPT08NGjSoxvWPP/5YkhQVFXWjS20QlRMAAAwyU89JRkaGJKlbt27y9vauc9zVq1eVk5OjnJwcXb16tfrz2NhYWSwW7dq1S7t27brunoSEBO3cuVP+/v567rnnGmW99aFyAgBAC3D69GlJUlhYWL3jzp8/r8cff1zSL28JdejQQZIUGRmp2bNn691339Uf//hH/f3vf9edd96pkydP6rvvvpO3t7dWrlypwMBA134REU4AADDMTOec/Pzzz5KkkJAQw3NMnjxZvXv31oYNG/Svf/1LOTk5uuOOOzRmzBi9+OKLCg29OYehckIs0AJwQixQ0804IbatX7dGmefipexGmaeloHICAIBBZnuVuKWgIRYAAJgKlRMAAAxqRp0RzQrhBAAAg8zUENuSsK0DAABMhcoJAAAGsa3jGoQTAAAM4m0d12BbBwAAmAqVEwAADDLTD/+1JIQTAAAMYlvHNdjWAQAApkLlBAAAg3hbxzUIJwAAGETPiWsQTgAAMIjKiWvQcwIAAEyFygkAAAZROXENwgkAAAYRTVyDbR0AAGAqFhs1KQAAYCJUTgAAgKkQTgAAgKkQTgAAgKkQTgAAgKkQTgAAgKkQTgAAgKkQTgAAgKkQTgAAgKkQTgAAgKkQTgAAgKkQTgAAgKkQTgAAgKkQTgAAgKkQTuCw77//XvHx8fq3f/s39enTR48++qiWLVum0tLSpl4aYBq5ubm69957tXDhwqZeCtBsEU7gkIyMDI0aNUq7d+9W27ZtFRUVpdLSUq1Zs0bPPPOMLl++3NRLBJrcxYsXNW3aNJWVlTX1UoBmjXCCBl27dk2zZ89WaWmp/vKXv2jbtm1asWKF/vu//1vDhg3Tt99+q7/+9a9NvUygSZ04cUKxsbHKyclp6qUAzR7hBA3as2ePTp8+rUGDBmn06NHVn3t6emrRokXy9vbWhx9+qOLi4iZcJdA0iouLtXTpUo0ZM0Z5eXnq0KFDUy8JaPYIJ2jQ/v37JUmPPPJIjWu33367Bg4cqKtXr+rAgQM3e2lAk0tISND69esVEBCg1atXKzo6uqmXBDR7hBM0KDs7W5LUvXv3Wq936dJFkpSVlXXT1gSYRbt27fTKK68oKSlJw4YNa+rlAC1C66ZeAMyvoKBAkhQcHFzr9aCgoOvGAbeSmJiYpl4C0OJQOUGD7K8Ke3p61nrd/jmvFAMAGgPhBA1yc3OTJFkslnrH2Wy2m7EcAEALRzhBg3x8fCSpzrMbysvLJUleXl43bU0AgJaLcIIG2XtKLly4UOt1e6+JfRwAADeCcIIG2d/SOXXqVK3X7Z/X9TYPAADOIJygQUOHDpUkJSUl1bj2888/6+DBg3J3d9fgwYNv9tIAAC0Q4QQNeuSRR9S+fXulpqZq8+bN1Z+Xl5dr/vz5Ki0t1ejRo9W2bdsmXCUAoKXgnBM0yNPTU0uWLNHkyZO1cOFC7dixQx06dNCRI0dUUFCgu+66S/Hx8U29TABAC0HlBA4ZOHCgtm3bpscee0xnz57V559/Ll9fX02bNk2bNm2S1Wpt6iUCAFoIi43DKQAAgIlQOQEAAKZCOAEAAKZCOAEAAKZCOAEAAKZCOAEAAKZCOAEAAKZCOAEAAKZCOAEAAKZCOAEAAKZCOAEAAKZCOAEAAKZCOAEAAKby/wCMTGPdx8xl2QAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make our confusion matrix more visual with Seaborn's heatmap()\n",
    "import seaborn as sns\n",
    "\n",
    "# set the font scale\n",
    "sns.set(font_scale=1.5)\n",
    "\n",
    "# Create a confusion matrix\n",
    "conf_mat=confusion_matrix(y_test,y_preds)\n",
    "\n",
    "# plot it using seaborn\n",
    "sns.heatmap(conf_mat);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4642f23-7c84-4ee0-a5c1-0c9d525ee3bd",
   "metadata": {},
   "source": [
    "## Creating a confusion matrix using Scikit-Learn \n",
    "To use the new methods of creating a confusion matrix with Scikit-Learn you will need sklearn version 1.0+"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "9cb75293-c90f-4940-b653-184b8355176a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.2.2'"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sklearn\n",
    "sklearn.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "0f6a6e57-07f2-48bf-9345-2dd1940f50b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x1ab96418fd0>"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "\n",
    "ConfusionMatrixDisplay.from_estimator(estimator=clf,X=X,y=y) # without the prediction being ready"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "bae7d199-8189-4f80-aee4-154128fc9404",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ConfusionMatrixDisplay.from_predictions(y_true=y_test,\n",
    "                                        y_pred=y_preds)\n",
    "plt.grid(False) # to remove the gridlines from the plot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6b52f18-02ae-4c77-ad4b-5030583003b5",
   "metadata": {},
   "source": [
    "**Classification Report**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "320f424a-a1f4-4e32-8dcb-3c64dc7f4f68",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.79      0.79        29\n",
      "           1       0.81      0.81      0.81        32\n",
      "\n",
      "    accuracy                           0.80        61\n",
      "   macro avg       0.80      0.80      0.80        61\n",
      "weighted avg       0.80      0.80      0.80        61\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "print(classification_report(y_test, y_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "6ccdcb1b-ad95-4702-b1a2-48b01fce672f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0.0</th>\n",
       "      <th>1.0</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>macro avg</th>\n",
       "      <th>weighted avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>precision</th>\n",
       "      <td>0.99990</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.9999</td>\n",
       "      <td>0.499950</td>\n",
       "      <td>0.99980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.9999</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.99990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f1-score</th>\n",
       "      <td>0.99995</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.9999</td>\n",
       "      <td>0.499975</td>\n",
       "      <td>0.99985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>support</th>\n",
       "      <td>9999.00000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.9999</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0.0  1.0  accuracy     macro avg  weighted avg\n",
       "precision     0.99990  0.0    0.9999      0.499950       0.99980\n",
       "recall        1.00000  0.0    0.9999      0.500000       0.99990\n",
       "f1-score      0.99995  0.0    0.9999      0.499975       0.99985\n",
       "support    9999.00000  1.0    0.9999  10000.000000   10000.00000"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Where precision and recall become valuable\n",
    "disease_true=np.zeros(10000)\n",
    "disease_true[0]=1 # only one positive case\n",
    "\n",
    "disease_preds=np.zeros(10000) # model predicts every case as 0\n",
    "\n",
    "pd.DataFrame(classification_report(disease_true,\n",
    "                                   disease_preds,\n",
    "                                   output_dict=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bcf34de-b80a-4f51-90bb-6aea7dac0d3f",
   "metadata": {},
   "source": [
    "### 4.2.2 Regression model evaluation metrics\n",
    "documentation: https://scikit-learn.org/stable/modules/model_evaluation.html#regression-metrics\n",
    "\n",
    "The ones we're going to cover are:\n",
    "1. R^2 (r-squared) or coefficient of determination\n",
    "2. Mean absolute error (MAE)\n",
    "3. Mean squared error (MSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "2d33fce9-a028-434d-b69f-6cf3d783e38b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "X= housing_df.drop(\"target\",axis=1)\n",
    "y=housing_df[\"target\"]\n",
    "\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2)\n",
    "\n",
    "model=RandomForestRegressor()\n",
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "9f9f75e2-d7e5-4a1d-b10b-a4bbfb75bf45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8065734772187598"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "f4b6bfdd-d1ab-482a-ab39-532935eded06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20046    0.47700\n",
       "3024     0.45800\n",
       "15663    5.00001\n",
       "20484    2.18600\n",
       "9814     2.78000\n",
       "          ...   \n",
       "15362    2.63300\n",
       "16623    2.66800\n",
       "18086    5.00001\n",
       "2144     0.72300\n",
       "3665     1.51500\n",
       "Name: target, Length: 4128, dtype: float64"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "33194c19-677b-4a8a-91b5-9016ab92cd70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0550030959302323"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "144094d4-1367-4499-9712-92c85de5c319",
   "metadata": {},
   "source": [
    "#### R^2\n",
    "What R-squared does: Compares your models predictions to the mean of the targets. Values can range from negative infinity (a very poor model) to 1. For example, if all your model does is predict the mean of the targets, it's R^2 value would be 0. And if your model perfectly predicts a range of numbers it's R^2 value would be 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "8bf45614-edc0-4c76-a295-2059ad1f0dcf",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "\n",
    "# fill an array woth y_test mean\n",
    "y_test_mean=np.full(len(y_test),y_test.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "1f76ae53-9700-4096-b854-db5642252f9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.0550031, 2.0550031, 2.0550031, 2.0550031, 2.0550031, 2.0550031,\n",
       "       2.0550031, 2.0550031, 2.0550031, 2.0550031])"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_mean[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "2691161c-560e-4141-b184-632f28e4b123",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_true=y_test,\n",
    "         y_pred=y_test_mean) # poorest model (average/mean of test values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "e69f9705-96a2-4672-bec5-85b96969a26a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_true=y_test,\n",
    "         y_pred=y_test) # perfect model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12534f4c-6ceb-4db4-b955-602b117413bd",
   "metadata": {},
   "source": [
    "#### Mean absolute error (MAE)\n",
    "MAE is the avg of the absolute differences between predictions and actual values.\n",
    "\n",
    "It gives you an idea of how wrong your models predictions are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "64153a15-b469-4429-bcb0-7f5cf222db2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.32659871732073664"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error\n",
    "y_preds=model.predict(X_test)\n",
    "mae=mean_absolute_error(y_test,y_preds)\n",
    "mae "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "24776650-2c85-4b2e-bf5f-4ef06b80551c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>actual values</th>\n",
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       "      <th>20046</th>\n",
       "      <td>0.47700</td>\n",
       "      <td>0.493840</td>\n",
       "      <td>0.016840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3024</th>\n",
       "      <td>0.45800</td>\n",
       "      <td>0.754940</td>\n",
       "      <td>0.296940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15663</th>\n",
       "      <td>5.00001</td>\n",
       "      <td>4.928596</td>\n",
       "      <td>-0.071414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20484</th>\n",
       "      <td>2.18600</td>\n",
       "      <td>2.543160</td>\n",
       "      <td>0.357160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9814</th>\n",
       "      <td>2.78000</td>\n",
       "      <td>2.331760</td>\n",
       "      <td>-0.448240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13311</th>\n",
       "      <td>1.58700</td>\n",
       "      <td>1.652530</td>\n",
       "      <td>0.065530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7113</th>\n",
       "      <td>1.98200</td>\n",
       "      <td>2.343230</td>\n",
       "      <td>0.361230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7668</th>\n",
       "      <td>1.57500</td>\n",
       "      <td>1.661820</td>\n",
       "      <td>0.086820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18246</th>\n",
       "      <td>3.40000</td>\n",
       "      <td>2.474890</td>\n",
       "      <td>-0.925110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5723</th>\n",
       "      <td>4.46600</td>\n",
       "      <td>4.834478</td>\n",
       "      <td>0.368478</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       actual values  predicted values  differences\n",
       "20046        0.47700          0.493840     0.016840\n",
       "3024         0.45800          0.754940     0.296940\n",
       "15663        5.00001          4.928596    -0.071414\n",
       "20484        2.18600          2.543160     0.357160\n",
       "9814         2.78000          2.331760    -0.448240\n",
       "13311        1.58700          1.652530     0.065530\n",
       "7113         1.98200          2.343230     0.361230\n",
       "7668         1.57500          1.661820     0.086820\n",
       "18246        3.40000          2.474890    -0.925110\n",
       "5723         4.46600          4.834478     0.368478"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame(data={\"actual values\":y_test,\n",
    "                      \"predicted values\": y_preds})\n",
    "df[\"differences\"]=df[\"predicted values\"]-df[\"actual values\"]\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "47751e7f-16b7-4aeb-8009-d0fc1468c55d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.32659871732073664"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MAE using formulas and differences\n",
    "np.abs(df[\"differences\"]).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49563789-6d43-46ae-ada3-1af957428630",
   "metadata": {},
   "source": [
    "#### Mean squared error (MSE)\n",
    "MSE is the mean of the square of the errors between predicted values and actual values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "f8682b4f-2bed-4d22-9767-5f6644fe855d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2534678520824551"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "y_preds=model.predict(X_test)\n",
    "mse=mean_squared_error(y_test,y_preds)\n",
    "mse "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "df533d4b-0c95-475c-b2b0-819fdfff4127",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>actual values</th>\n",
       "      <th>predicted values</th>\n",
       "      <th>differences</th>\n",
       "      <th>squared differences</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20046</th>\n",
       "      <td>0.47700</td>\n",
       "      <td>0.493840</td>\n",
       "      <td>0.016840</td>\n",
       "      <td>0.000284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3024</th>\n",
       "      <td>0.45800</td>\n",
       "      <td>0.754940</td>\n",
       "      <td>0.296940</td>\n",
       "      <td>0.088173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15663</th>\n",
       "      <td>5.00001</td>\n",
       "      <td>4.928596</td>\n",
       "      <td>-0.071414</td>\n",
       "      <td>0.005100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20484</th>\n",
       "      <td>2.18600</td>\n",
       "      <td>2.543160</td>\n",
       "      <td>0.357160</td>\n",
       "      <td>0.127563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9814</th>\n",
       "      <td>2.78000</td>\n",
       "      <td>2.331760</td>\n",
       "      <td>-0.448240</td>\n",
       "      <td>0.200919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13311</th>\n",
       "      <td>1.58700</td>\n",
       "      <td>1.652530</td>\n",
       "      <td>0.065530</td>\n",
       "      <td>0.004294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7113</th>\n",
       "      <td>1.98200</td>\n",
       "      <td>2.343230</td>\n",
       "      <td>0.361230</td>\n",
       "      <td>0.130487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7668</th>\n",
       "      <td>1.57500</td>\n",
       "      <td>1.661820</td>\n",
       "      <td>0.086820</td>\n",
       "      <td>0.007538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18246</th>\n",
       "      <td>3.40000</td>\n",
       "      <td>2.474890</td>\n",
       "      <td>-0.925110</td>\n",
       "      <td>0.855829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5723</th>\n",
       "      <td>4.46600</td>\n",
       "      <td>4.834478</td>\n",
       "      <td>0.368478</td>\n",
       "      <td>0.135776</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       actual values  predicted values  differences  squared differences\n",
       "20046        0.47700          0.493840     0.016840             0.000284\n",
       "3024         0.45800          0.754940     0.296940             0.088173\n",
       "15663        5.00001          4.928596    -0.071414             0.005100\n",
       "20484        2.18600          2.543160     0.357160             0.127563\n",
       "9814         2.78000          2.331760    -0.448240             0.200919\n",
       "13311        1.58700          1.652530     0.065530             0.004294\n",
       "7113         1.98200          2.343230     0.361230             0.130487\n",
       "7668         1.57500          1.661820     0.086820             0.007538\n",
       "18246        3.40000          2.474890    -0.925110             0.855829\n",
       "5723         4.46600          4.834478     0.368478             0.135776"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"squared differences\"]=np.square(df[\"differences\"])\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "ace5cf85-242b-4d4f-9438-fb0a4f719103",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2534678520824551"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"squared differences\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "ff9c2ba1-5e51-4ea1-8d3d-c3d1ce715b9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_large_error=df.copy()\n",
    "df_large_error.iloc[0][\"squared differences\"]=16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "65f43096-003a-4bb8-9b9b-9a8650a491c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>actual values</th>\n",
       "      <th>predicted values</th>\n",
       "      <th>differences</th>\n",
       "      <th>squared differences</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20046</th>\n",
       "      <td>0.47700</td>\n",
       "      <td>0.493840</td>\n",
       "      <td>0.016840</td>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3024</th>\n",
       "      <td>0.45800</td>\n",
       "      <td>0.754940</td>\n",
       "      <td>0.296940</td>\n",
       "      <td>0.088173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15663</th>\n",
       "      <td>5.00001</td>\n",
       "      <td>4.928596</td>\n",
       "      <td>-0.071414</td>\n",
       "      <td>0.005100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20484</th>\n",
       "      <td>2.18600</td>\n",
       "      <td>2.543160</td>\n",
       "      <td>0.357160</td>\n",
       "      <td>0.127563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9814</th>\n",
       "      <td>2.78000</td>\n",
       "      <td>2.331760</td>\n",
       "      <td>-0.448240</td>\n",
       "      <td>0.200919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>15362</th>\n",
       "      <td>2.63300</td>\n",
       "      <td>2.220380</td>\n",
       "      <td>-0.412620</td>\n",
       "      <td>0.170255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16623</th>\n",
       "      <td>2.66800</td>\n",
       "      <td>1.947760</td>\n",
       "      <td>-0.720240</td>\n",
       "      <td>0.518746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18086</th>\n",
       "      <td>5.00001</td>\n",
       "      <td>4.836378</td>\n",
       "      <td>-0.163632</td>\n",
       "      <td>0.026775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2144</th>\n",
       "      <td>0.72300</td>\n",
       "      <td>0.717820</td>\n",
       "      <td>-0.005180</td>\n",
       "      <td>0.000027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3665</th>\n",
       "      <td>1.51500</td>\n",
       "      <td>1.679010</td>\n",
       "      <td>0.164010</td>\n",
       "      <td>0.026899</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4128 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       actual values  predicted values  differences  squared differences\n",
       "20046        0.47700          0.493840     0.016840            16.000000\n",
       "3024         0.45800          0.754940     0.296940             0.088173\n",
       "15663        5.00001          4.928596    -0.071414             0.005100\n",
       "20484        2.18600          2.543160     0.357160             0.127563\n",
       "9814         2.78000          2.331760    -0.448240             0.200919\n",
       "...              ...               ...          ...                  ...\n",
       "15362        2.63300          2.220380    -0.412620             0.170255\n",
       "16623        2.66800          1.947760    -0.720240             0.518746\n",
       "18086        5.00001          4.836378    -0.163632             0.026775\n",
       "2144         0.72300          0.717820    -0.005180             0.000027\n",
       "3665         1.51500          1.679010     0.164010             0.026899\n",
       "\n",
       "[4128 rows x 4 columns]"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_large_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "4afae9cc-1170-4dae-97e2-f78cecbc8012",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2573437523766412"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate MSE with large error\n",
    "df_large_error[\"squared differences\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "1805c06a-d7be-459e-9889-7ea99cf922ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>actual values</th>\n",
       "      <th>predicted values</th>\n",
       "      <th>differences</th>\n",
       "      <th>squared differences</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20046</th>\n",
       "      <td>0.47700</td>\n",
       "      <td>0.493840</td>\n",
       "      <td>0.016840</td>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3024</th>\n",
       "      <td>20.00000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15663</th>\n",
       "      <td>20.00000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20484</th>\n",
       "      <td>20.00000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9814</th>\n",
       "      <td>20.00000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>15362</th>\n",
       "      <td>2.63300</td>\n",
       "      <td>2.220380</td>\n",
       "      <td>-0.412620</td>\n",
       "      <td>0.170255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16623</th>\n",
       "      <td>2.66800</td>\n",
       "      <td>1.947760</td>\n",
       "      <td>-0.720240</td>\n",
       "      <td>0.518746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18086</th>\n",
       "      <td>5.00001</td>\n",
       "      <td>4.836378</td>\n",
       "      <td>-0.163632</td>\n",
       "      <td>0.026775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2144</th>\n",
       "      <td>0.72300</td>\n",
       "      <td>0.717820</td>\n",
       "      <td>-0.005180</td>\n",
       "      <td>0.000027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3665</th>\n",
       "      <td>1.51500</td>\n",
       "      <td>1.679010</td>\n",
       "      <td>0.164010</td>\n",
       "      <td>0.026899</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4128 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       actual values  predicted values  differences  squared differences\n",
       "20046        0.47700          0.493840     0.016840            16.000000\n",
       "3024        20.00000         20.000000    20.000000            20.000000\n",
       "15663       20.00000         20.000000    20.000000            20.000000\n",
       "20484       20.00000         20.000000    20.000000            20.000000\n",
       "9814        20.00000         20.000000    20.000000            20.000000\n",
       "...              ...               ...          ...                  ...\n",
       "15362        2.63300          2.220380    -0.412620             0.170255\n",
       "16623        2.66800          1.947760    -0.720240             0.518746\n",
       "18086        5.00001          4.836378    -0.163632             0.026775\n",
       "2144         0.72300          0.717820    -0.005180             0.000027\n",
       "3665         1.51500          1.679010     0.164010             0.026899\n",
       "\n",
       "[4128 rows x 4 columns]"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_large_error.iloc[1:100]=20\n",
    "df_large_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "1a589eca-129a-448b-b0ff-dce18d105f92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7334180712088106"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_large_error[\"squared differences\"].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5ac68a7-3bf8-43da-a6fb-c1e4facf3d19",
   "metadata": {},
   "source": [
    "### 4.3.2 Finally using the`scoring()` parameter "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "38c730ed-1d45-4201-a961-5b6ad0a74a7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "X=heart_disease.drop(\"target\",axis=1)\n",
    "y=heart_disease[\"target\"]\n",
    "\n",
    "clf=RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "8c1b74d8-d754-43e7-8780-04ebc498affd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.81967213, 0.90163934, 0.83606557, 0.78333333, 0.78333333])"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(42) \n",
    "# cross-validation accuracy\n",
    "cv_acc= cross_val_score(clf,X,y,cv=5,scoring=None) # if scoring=None, estimators's default scoring evaulation metric is used (accuracy for classification models)\n",
    "cv_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "08f835a8-9300-41bf-b002-db3857a68f41",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The cross-validated accuracy is: 82.48%\n"
     ]
    }
   ],
   "source": [
    "# cross-validated accuracy \n",
    "print(f\"The cross-validated accuracy is: {np.mean(cv_acc)*100:.2f}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "59296f65-d61c-46a6-a87d-cc7dd5137917",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The cross-validated accuracy is: 82.48%\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(42) \n",
    "# cross-validation accuracy\n",
    "cv_acc= cross_val_score(clf,X,y,cv=5,scoring=\"accuracy\") # if scoring=None, estimators's default scoring evaulation metric is used (accuracy for classification models)\n",
    "print(f\"The cross-validated accuracy is: {np.mean(cv_acc)*100:.2f}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "5c18fd20-e3dd-407f-9da5-86f4b1885a6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.82352941, 0.93548387, 0.84848485, 0.79411765, 0.76315789])"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# precision\n",
    "np.random.seed(42)\n",
    "cv_precision= cross_val_score(clf,X,y,cv=5,scoring=\"precision\")\n",
    "cv_precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "362721bf-bcbc-4e5f-a76f-cb025cc5e265",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The cross-validated precision is: 83.30%\n"
     ]
    }
   ],
   "source": [
    "# cross-validated precision\n",
    "print(f\"The cross-validated precision is: {np.mean(cv_precision)*100:.2f}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "53f4e97f-3d9f-4723-aaff-b81f80783919",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The cross-validated Recall is: 85.45%\n"
     ]
    }
   ],
   "source": [
    "# Recall\n",
    "np.random.seed(42)\n",
    "cv_recall=cross_val_score(clf,X,y,cv=5,scoring=\"recall\")\n",
    "# cross-validated Recall\n",
    "print(f\"The cross-validated Recall is: {np.mean(cv_recall)*100:.2f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13034c9a-b394-4345-a01a-7a3f26700c7a",
   "metadata": {},
   "source": [
    " Let's see the `scoring` parameter being using for a regression problem..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "e91c8118-ea12-4116-a39d-8cbefbf8d223",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "X=housing_df.drop(\"target\",axis=1)\n",
    "y=housing_df[\"target\"]\n",
    "\n",
    "clf=RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "498a6d2e-efc6-4a10-bad6-c1002b603ec2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.46620501732073655"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# neg_mean_absolute_error\n",
    "np.random.seed(42)\n",
    "cv_mae=cross_val_score(clf,X,y,cv=5,scoring=\"neg_mean_absolute_error\")\n",
    "np.mean(cv_mae)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "d3adb516-73a8-490b-a494-0f76ac890b42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.46141356443710196"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# neg_mean_squared_error\n",
    "np.random.seed(42)\n",
    "cv_mse=cross_val_score(clf,X,y,cv=3,scoring=\"neg_mean_squared_error\")\n",
    "np.mean(cv_mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "1801ad62-569d-43c2-bbc8-f292be1a542f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The cross-validated R2 is: 65.45%\n"
     ]
    }
   ],
   "source": [
    "# R^2\n",
    "np.random.seed(42)\n",
    "cv_r2=cross_val_score(clf,X,y,cv=3,scoring=None)\n",
    "np.mean(cv_r2)\n",
    "# cross-validated R^2\n",
    "print(f\"The cross-validated R2 is: {np.mean(cv_r2)*100:.2f}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "58479401-2cf5-4b59-930c-7fcd42d48e60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([-0.54256026, -0.40902123, -0.43706982, -0.46918522, -0.47318856]),\n",
       " array([-0.51017222, -0.33368897, -0.5403795 ]))"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_mae,cv_mse"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe61cab6-f164-42bf-af59-63a4e71b9b97",
   "metadata": {},
   "source": [
    "## 4.3 Using different evaluation metrics as Scikit-Learn functions\n",
    "the 3rd way to evaluate Scikit-Learn machine learning models/estimators is to using the `sklearn.metrics` module "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "27db8f87-c367-4077-ba59-588a55e8b8a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8524590163934426"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score,precision_score,recall_score,f1_score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "#creating X and y\n",
    "X=heart_disease.drop(\"target\",axis=1)\n",
    "y=heart_disease[\"target\"]\n",
    "\n",
    "#splitting the data\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2)\n",
    "\n",
    "#creating the model\n",
    "model=RandomForestClassifier()\n",
    "\n",
    "#fitting the model\n",
    "model.fit(X_train,y_train)\n",
    "\n",
    "# predictions\n",
    "y_preds=model.predict(X_test)\n",
    "\n",
    "# Evaluate the model using evaluation functions\n",
    "accuracy_score(y_test,y_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "94b14849-5771-42c2-8570-3f4a6789376c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8484848484848485"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_score(y_test,y_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "f792244a-52da-4ccb-b482-5de8a43f86bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.875"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_test,y_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "6ec022ee-ab57-4af6-90fc-4c48cb952867",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8615384615384615"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_test,y_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "03b73772-f2bf-4ee0-9443-93f9d2611a06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8065734772187598"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score,mean_absolute_error,mean_squared_error\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "#creating X and y\n",
    "X=housing_df.drop(\"target\",axis=1)\n",
    "y=housing_df[\"target\"]\n",
    "\n",
    "#splitting the data\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2)\n",
    "\n",
    "#creating the model\n",
    "model=RandomForestRegressor()\n",
    "\n",
    "#fitting the model\n",
    "model.fit(X_train,y_train)\n",
    "\n",
    "# predictions\n",
    "y_preds=model.predict(X_test)\n",
    "\n",
    "# Evaluate the model using evaluation functions\n",
    "r2_score(y_test,y_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "5a09fbe9-4eeb-45f5-81f1-ed03cc4a1df6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.32659871732073664"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_error(y_test,y_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "bed984e0-0161-4851-93b5-5aa5ddd119c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2534678520824551"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(y_test,y_preds) # you can decorate the output if you want"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11daf755-a86d-4fcf-8339-772a04677cfc",
   "metadata": {},
   "source": [
    "## 5. Improving a model\n",
    "\n",
    "First predictions = baseline predictions.\n",
    "first model = baseline model.\n",
    "\n",
    "From a data perspective:\n",
    "* Could we collect more data? (generally, the more data, the better)\n",
    "* Could we improve our data?\n",
    "\n",
    "From a model perspective:\n",
    "* Is there a better model we could use?\n",
    "* Could we imporve the currect model?\n",
    "\n",
    "Hyperparameters vs. Parameters\n",
    "* Parameters = model find these patterns in data\n",
    "* Hyperparamters = settings on a model you can adjust to (potentially) imporve it's abitily to find patterns\n",
    "\n",
    "Three ways to adjust hyperparameters:\n",
    "1. By hand\n",
    "2. Randomly with RandomSearchCV\n",
    "3. Exhaustively with GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "f826fde5-a310-4acc-9598-444f827492de",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "clf=RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "75e6d42e-5ec3-4327-92f6-8b7356e84d3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bootstrap': True,\n",
       " 'ccp_alpha': 0.0,\n",
       " 'class_weight': None,\n",
       " 'criterion': 'gini',\n",
       " 'max_depth': None,\n",
       " 'max_features': 'sqrt',\n",
       " 'max_leaf_nodes': None,\n",
       " 'max_samples': None,\n",
       " 'min_impurity_decrease': 0.0,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 2,\n",
       " 'min_weight_fraction_leaf': 0.0,\n",
       " 'n_estimators': 100,\n",
       " 'n_jobs': None,\n",
       " 'oob_score': False,\n",
       " 'random_state': None,\n",
       " 'verbose': 0,\n",
       " 'warm_start': False}"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.get_params()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27f93eb6-6c10-4799-ac2c-b0b81d678ab3",
   "metadata": {},
   "source": [
    "## 5.1 Tuning hyperparameters by hand\n",
    "\n",
    "Let's make 3. sets, training, validation and test.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "5de3e120-5b7b-4033-aa5d-11be808343ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bootstrap': True,\n",
       " 'ccp_alpha': 0.0,\n",
       " 'class_weight': None,\n",
       " 'criterion': 'gini',\n",
       " 'max_depth': None,\n",
       " 'max_features': 'sqrt',\n",
       " 'max_leaf_nodes': None,\n",
       " 'max_samples': None,\n",
       " 'min_impurity_decrease': 0.0,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 2,\n",
       " 'min_weight_fraction_leaf': 0.0,\n",
       " 'n_estimators': 100,\n",
       " 'n_jobs': None,\n",
       " 'oob_score': False,\n",
       " 'random_state': None,\n",
       " 'verbose': 0,\n",
       " 'warm_start': False}"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.get_params()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f36f44d-20fd-4f1a-9d00-532254f52284",
   "metadata": {},
   "source": [
    "We're going to try and adjust:\n",
    "* `max_depth`\n",
    "* `max_features`\n",
    "* `min_samples_leaf`\n",
    "* `min_samples_split`\n",
    "* `n_estimators`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "58585520-34b7-459c-a4d7-ac5ddaa87047",
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_preds(y_true,y_preds):\n",
    "    \"\"\"\n",
    "    Performs evaluation comparison on y_true labels vs. y_preds labels on a classification.\n",
    "    \"\"\"\n",
    "    accuracy = accuracy_score(y_true,y_preds)\n",
    "    precision = precision_score(y_true,y_preds)\n",
    "    recall = recall_score(y_true,y_preds)\n",
    "    f1 = f1_score(y_true,y_preds)\n",
    "    metrics_dict = {\"accuracy\": round(accuracy,2),\n",
    "                    \"precision\": round(precision,2),\n",
    "                    \"recall\": round(recall,2),\n",
    "                    \"f1\": round(f1,2)}\n",
    "    print(f\"Acc: {accuracy * 100:.2f}\")\n",
    "    print(f\"Precision: {precision:.2f}\")\n",
    "    print(f\"Recall: {recall:.2f}\")\n",
    "    print(f\"f1 score: {f1:.2f}\")\n",
    "\n",
    "    return metrics_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "6fb544de-9140-4641-937c-a85285d36570",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acc: 82.22\n",
      "Precision: 0.81\n",
      "Recall: 0.88\n",
      "f1 score: 0.85\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.82, 'precision': 0.81, 'recall': 0.88, 'f1': 0.85}"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "# Shuffle the data\n",
    "heart_disease_shuffled = heart_disease.sample(frac=1)\n",
    "\n",
    "# Split into X,y\n",
    "X=heart_disease_shuffled.drop(\"target\",axis=1)\n",
    "y=heart_disease_shuffled[\"target\"]\n",
    "\n",
    "# Split the data into train, validation & test sets\n",
    "train_split = round(0.7*len(heart_disease_shuffled)) # 70% of data\n",
    "valid_split = round(train_split + 0.15*len(heart_disease_shuffled)) # 15% of data\n",
    "X_train,y_train = X[:train_split],y[:train_split]\n",
    "X_valid,y_valid = X[train_split:valid_split], y[train_split:valid_split]\n",
    "X_test,y_test = X[valid_split:], y[:valid_split]\n",
    "\n",
    "clf = RandomForestClassifier()\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "# Make baseline predictions\n",
    "y_preds = clf.predict(X_valid)\n",
    "\n",
    "# Evaluate the classifier on validation set\n",
    "baseline_metrics = evaluate_preds(y_valid,y_preds)\n",
    "baseline_metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "d41d524b-035b-4b94-83c5-874aa3d2353d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acc: 80.00\n",
      "Precision: 0.81\n",
      "Recall: 0.84\n",
      "f1 score: 0.82\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "\n",
    "# Create a second classifier with different hyperparameters\n",
    "clf_2 = RandomForestClassifier(max_depth=10)\n",
    "clf_2.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions with different hyperparameters\n",
    "y_preds_2 = clf_2.predict(X_valid)\n",
    "\n",
    "# Evaluate the 2nd classifier\n",
    "clf_2_metrics = evaluate_preds(y_valid, y_preds_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f81a1b0-9fe6-42f8-abab-84575a791423",
   "metadata": {},
   "source": [
    "## 5.2 Hyperparameter tuning with RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "5d547b68-4ffd-4410-8b40-39d30f9ddc71",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=1200; total time=   2.5s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=1200; total time=   2.9s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=1200; total time=   2.6s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=1200; total time=   3.0s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=1200; total time=   2.7s\n",
      "[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=100; total time=   0.1s\n",
      "[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=100; total time=   0.3s\n",
      "[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=100; total time=   0.1s\n",
      "[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=100; total time=   0.1s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=   0.5s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=   0.4s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=   0.4s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=   0.3s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=   0.4s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=100; total time=   0.1s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=100; total time=   0.1s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=100; total time=   0.3s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=4, n_estimators=10; total time=   0.0s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=4, n_estimators=10; total time=   0.0s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=4, n_estimators=10; total time=   0.0s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=4, n_estimators=10; total time=   0.0s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=4, n_estimators=10; total time=   0.0s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=10; total time=   0.0s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=10; total time=   0.0s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=10; total time=   0.0s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=10; total time=   0.0s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=10; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=500; total time=   1.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=500; total time=   1.9s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=500; total time=   2.3s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=500; total time=   1.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=500; total time=   1.1s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=200; total time=   0.3s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=200; total time=   0.4s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=200; total time=   0.5s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=200; total time=   0.5s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=200; total time=   0.4s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=4, n_estimators=200; total time=   0.5s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=4, n_estimators=200; total time=   0.4s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=4, n_estimators=200; total time=   0.3s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=4, n_estimators=200; total time=   0.3s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=4, n_estimators=200; total time=   0.4s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=1000; total time=   2.1s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=1000; total time=   2.7s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=1000; total time=   2.0s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=1000; total time=   2.4s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=1000; total time=   1.9s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(n_jobs=1),\n",
       "                   param_distributions={&#x27;max_depth&#x27;: [None, 5, 10, 20, 30],\n",
       "                                        &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;],\n",
       "                                        &#x27;min_samples_leaf&#x27;: [1, 2, 4],\n",
       "                                        &#x27;min_samples_split&#x27;: [2, 4, 6],\n",
       "                                        &#x27;n_estimators&#x27;: [10, 100, 200, 500,\n",
       "                                                         1000, 1200]},\n",
       "                   verbose=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(n_jobs=1),\n",
       "                   param_distributions={&#x27;max_depth&#x27;: [None, 5, 10, 20, 30],\n",
       "                                        &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;],\n",
       "                                        &#x27;min_samples_leaf&#x27;: [1, 2, 4],\n",
       "                                        &#x27;min_samples_split&#x27;: [2, 4, 6],\n",
       "                                        &#x27;n_estimators&#x27;: [10, 100, 200, 500,\n",
       "                                                         1000, 1200]},\n",
       "                   verbose=2)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_jobs=1)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_jobs=1)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(n_jobs=1),\n",
       "                   param_distributions={'max_depth': [None, 5, 10, 20, 30],\n",
       "                                        'max_features': ['auto', 'sqrt'],\n",
       "                                        'min_samples_leaf': [1, 2, 4],\n",
       "                                        'min_samples_split': [2, 4, 6],\n",
       "                                        'n_estimators': [10, 100, 200, 500,\n",
       "                                                         1000, 1200]},\n",
       "                   verbose=2)"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "grid = {\"n_estimators\": [10, 100, 200, 500, 1000, 1200],\n",
    "        \"max_depth\":[None, 5, 10, 20, 30],\n",
    "        \"max_features\":[\"auto\",\"sqrt\"],\n",
    "        \"min_samples_split\":[2,4,6],\n",
    "        \"min_samples_leaf\":[1,2,4]}\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "# Split into X & y\n",
    "X=heart_disease_shuffled.drop(\"target\",axis=1)\n",
    "y=heart_disease_shuffled[\"target\"]\n",
    "\n",
    "# Split into train and test sets\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2)\n",
    "\n",
    "# Instantiate RandomForstClassifier\n",
    "clf = RandomForestClassifier(n_jobs=1) # n_jobs defines how much computer power you want to dedicate to program \n",
    "\n",
    "# Setup RanRandomizedSearchCV\n",
    "rs_clf = RandomizedSearchCV(estimator=clf,\n",
    "                            param_distributions=grid,\n",
    "                            n_iter=10, # number of models to try\n",
    "                            cv=5,\n",
    "                            verbose=2)\n",
    "\n",
    "# fit the RandomizedSearchCV version of clf\n",
    "rs_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "b631f758-7ec8-4dd9-adbc-121e57b81f21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 200,\n",
       " 'min_samples_split': 6,\n",
       " 'min_samples_leaf': 2,\n",
       " 'max_features': 'sqrt',\n",
       " 'max_depth': None}"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_clf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "cd3bf2f7-8723-43a0-ad57-7835ce51daf9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acc: 81.97\n",
      "Precision: 0.77\n",
      "Recall: 0.86\n",
      "f1 score: 0.81\n"
     ]
    }
   ],
   "source": [
    "# Make predictions with the best hyperparameters\n",
    "rs_y_preds = rs_clf.predict(X_test)\n",
    "\n",
    "# Evaluate the predictions\n",
    "rs_metrics = evaluate_preds(y_test, rs_y_preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fb0a446-c213-44cb-825a-97bef776bf99",
   "metadata": {},
   "source": [
    "## 5.3 Hyperparameter tuning with GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "92503fe5-cc36-4485-b3d5-cadfd4126db5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': [10, 100, 200, 500, 1000, 1200],\n",
       " 'max_depth': [None, 5, 10, 20, 30],\n",
       " 'max_features': ['auto', 'sqrt'],\n",
       " 'min_samples_split': [2, 4, 6],\n",
       " 'min_samples_leaf': [1, 2, 4]}"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "911de291-4698-4822-b21b-f6d094395392",
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_2 = {'n_estimators': [100, 200, 500],\n",
    "          'max_depth': [None, 5, 10, 20, 30],\n",
    "          'max_features': ['auto', 'sqrt'],\n",
    "          'min_samples_split': [6],\n",
    "          'min_samples_leaf': [1, 2]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "c29ad022-de92-4b11-8c73-e288b3949539",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV, train_test_split\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "# Split into X & y\n",
    "X=heart_disease_shuffled.drop(\"target\",axis=1)\n",
    "y=heart_disease_shuffled[\"target\"]\n",
    "\n",
    "# Split into train and test sets\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2)\n",
    "\n",
    "# # Instantiate RandomForstClassifier\n",
    "# clf = RandomForestClassifier(n_jobs=1) # n_jobs defines how much computer power you want to dedicate to program \n",
    "\n",
    "# # Setup GridSearchCV\n",
    "# gs_clf = GridSearchCV(estimator=clf,\n",
    "#                       param_grid=grid_2,\n",
    "#                       cv=5,\n",
    "#                       verbose=2)\n",
    "\n",
    "# fit the RandomizedSearchCV version of clf\n",
    "# gs_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "6e06b341-3839-49ef-8964-1a001036b590",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 20,\n",
       " 'max_features': 'auto',\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 6,\n",
       " 'n_estimators': 200}"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs_clf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "49d8ffea-b1bc-41bd-92b2-2a87ffb574d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acc: 80.33\n",
      "Precision: 0.75\n",
      "Recall: 0.86\n",
      "f1 score: 0.80\n"
     ]
    }
   ],
   "source": [
    "# Make predictions with the best hyperparameters\n",
    "gs_y_preds = gs_clf.predict(X_test)\n",
    "\n",
    "# Evaluate the predictions\n",
    "gs_metrics = evaluate_preds(y_test, gs_y_preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9396463d-19ce-4842-9138-df888defd3a0",
   "metadata": {},
   "source": [
    "Let's compare our different models metrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "d0bd31ce-ace8-4853-a4ee-ebc4e76ab387",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "compare_metrics = pd.DataFrame({\"baseline\": baseline_metrics,\n",
    "                                \"clf_2\": clf_2_metrics,\n",
    "                                \"random search\": rs_metrics,\n",
    "                                \"grid search\": gs_metrics})\n",
    "\n",
    "compare_metrics.plot.bar(figsize=(10, 8));"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "debcfff6-293a-4963-9ae9-07ec5fbd6100",
   "metadata": {},
   "source": [
    "## 6. Saving and loading training machine learning models\n",
    "\n",
    "Two ways to save and load machine learning models:\n",
    "1. With Python's `pickle` module\n",
    "2. With the `joblib` module\n",
    "\n",
    "**Pickle**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "f5e5ea3b-a268-48bd-bfaa-c202f154b0e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "# Save an existing model to file\n",
    "pickle.dump(gs_clf,open(\"gs_random_forest_model_1.pkl\",\"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "a6c06c43-f872-43f8-8f5f-058e523530cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load a saved model\n",
    "loaded_pickle_model = pickle.load(open(\"gs_random_forest_model_1.pkl\",\"rb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "4fd33f21-4d76-46e8-be32-2c975bce7254",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acc: 80.33\n",
      "Precision: 0.75\n",
      "Recall: 0.86\n",
      "f1 score: 0.80\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.8, 'precision': 0.75, 'recall': 0.86, 'f1': 0.8}"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make some predictions\n",
    "pickle_y_preds = loaded_pickle_model.predict(X_test)\n",
    "evaluate_preds(y_test, pickle_y_preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8926cae6-f03b-44e1-8e99-e533951c563b",
   "metadata": {},
   "source": [
    "**Joblib**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "88c6ede2-f191-455a-ac5c-43026a96672a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['gs_random_forest_model_1.joblib']"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from joblib import dump,load \n",
    "\n",
    "# Save model to file\n",
    "dump(gs_clf, filename=\"gs_random_forest_model_1.joblib\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "ec7c2f88-129a-4f3d-b3a1-14cd2710b6d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import a saved joblib model\n",
    "loaded_joblib_model = load(filename=\"gs_random_forest_model_1.joblib\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "0444192c-d9da-417c-8df9-6b5ef490309b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acc: 80.33\n",
      "Precision: 0.75\n",
      "Recall: 0.86\n",
      "f1 score: 0.80\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.8, 'precision': 0.75, 'recall': 0.86, 'f1': 0.8}"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make and evaluate joblib predictions\n",
    "joblib_y_preds = loaded_joblib_model.predict(X_test)\n",
    "evaluate_preds(y_test, joblib_y_preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "446dd8e9-2abd-43e0-8fb2-02c23004d39b",
   "metadata": {},
   "source": [
    "## 7. Putting it all together!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "318a7516-9795-4449-8c05-876de00ac1ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Honda</td>\n",
       "      <td>White</td>\n",
       "      <td>35431.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15323.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Blue</td>\n",
       "      <td>192714.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>19943.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Honda</td>\n",
       "      <td>White</td>\n",
       "      <td>84714.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>28343.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>154365.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13434.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>Blue</td>\n",
       "      <td>181577.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14043.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Black</td>\n",
       "      <td>35820.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>32042.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>NaN</td>\n",
       "      <td>White</td>\n",
       "      <td>155144.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5716.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>Blue</td>\n",
       "      <td>66604.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>31570.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>Honda</td>\n",
       "      <td>White</td>\n",
       "      <td>215883.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>248360.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12732.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Make Colour  Odometer (KM)  Doors    Price\n",
       "0     Honda  White        35431.0    4.0  15323.0\n",
       "1       BMW   Blue       192714.0    5.0  19943.0\n",
       "2     Honda  White        84714.0    4.0  28343.0\n",
       "3    Toyota  White       154365.0    4.0  13434.0\n",
       "4    Nissan   Blue       181577.0    3.0  14043.0\n",
       "..      ...    ...            ...    ...      ...\n",
       "995  Toyota  Black        35820.0    4.0  32042.0\n",
       "996     NaN  White       155144.0    3.0   5716.0\n",
       "997  Nissan   Blue        66604.0    4.0  31570.0\n",
       "998   Honda  White       215883.0    4.0   4001.0\n",
       "999  Toyota   Blue       248360.0    4.0  12732.0\n",
       "\n",
       "[1000 rows x 5 columns]"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"data/car-sales-extended-missing-data.csv\")\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "b9ac90c9-3ca0-4814-a98c-56ef408f6ea3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make              object\n",
       "Colour            object\n",
       "Odometer (KM)    float64\n",
       "Doors            float64\n",
       "Price            float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "080f93e3-4471-40c6-ba5b-5fa8c5e77091",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make             49\n",
       "Colour           50\n",
       "Odometer (KM)    50\n",
       "Doors            50\n",
       "Price            50\n",
       "dtype: int64"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6343fd71-536d-4e7b-9a22-804c0a1c1a0a",
   "metadata": {},
   "source": [
    "Steps we want to do (all in one cell):\n",
    "1. Fill missing data\n",
    "2. Convert data to numbers\n",
    "3. Build a model on the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "8a2bcfad-2358-4d40-bd9f-5212042b8189",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.22188417408787875"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting data ready\n",
    "import pandas as pd\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "# Modelling\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "\n",
    "# Setup random seed\n",
    "import numpy as np\n",
    "np.random.seed(42)\n",
    "\n",
    "# Import data and drop rows with missing labels\n",
    "data = pd.read_csv(\"data/car-sales-extended-missing-data.csv\")\n",
    "data.dropna(subset=[\"Price\"],inplace=True)\n",
    "\n",
    "# Define different features and transformer pipeline\n",
    "categorical_features = [\"Make\",\"Colour\"]\n",
    "categorical_transformer = Pipeline(steps=[\n",
    "    (\"imputer\", SimpleImputer(strategy=\"constant\",fill_value=\"missing\")),\n",
    "    (\"onehot\", OneHotEncoder(handle_unknown=\"ignore\"))])\n",
    "\n",
    "door_feature = [\"Doors\"]\n",
    "door_transformer = Pipeline(steps=[\n",
    "    (\"imputer\", SimpleImputer(strategy=\"constant\",fill_value=4))])\n",
    "\n",
    "numeric_feature = [\"Odometer (KM)\"]\n",
    "numeric_transformer = Pipeline(steps=[\n",
    "    (\"imputer\", SimpleImputer(strategy=\"mean\"))\n",
    "])\n",
    "\n",
    "# Setup preprocessing steps (fill missing values, then convert to numbers)\n",
    "preprocessor = ColumnTransformer(\n",
    "                    transformers=[\n",
    "                        (\"cat\", categorical_transformer,categorical_features),\n",
    "                        (\"door\", door_transformer, door_feature),\n",
    "                        (\"num\", numeric_transformer,numeric_feature)\n",
    "                    ])\n",
    "\n",
    "# Creating a preprocessing and modelling pipeline\n",
    "model = Pipeline(steps=[(\"preprocessor\", preprocessor),\n",
    "                        (\"model\", RandomForestRegressor())])\n",
    "\n",
    "# Split data\n",
    "X = data.drop(\"Price\",axis=1)\n",
    "y = data[\"Price\"]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.2)\n",
    "\n",
    "# Fit and score the model\n",
    "model.fit(X_train, y_train)\n",
    "model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd188b16-fc3d-4920-a812-e82656321d1f",
   "metadata": {},
   "source": [
    "It's also possible to use `GridSearchCV` or `RandomizedSearchCV` with our `pipeline`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "e67a1dea-75ac-4600-b1d1-917094454e11",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 16 candidates, totalling 80 fits\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=2, model__n_estimators=100, preprocessor__num__imputer__strategy=mean; total time=   0.5s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=2, model__n_estimators=100, preprocessor__num__imputer__strategy=mean; total time=   0.4s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=2, model__n_estimators=100, preprocessor__num__imputer__strategy=mean; total time=   0.4s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=2, model__n_estimators=100, preprocessor__num__imputer__strategy=mean; total time=   0.4s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=2, model__n_estimators=100, preprocessor__num__imputer__strategy=mean; total time=   0.4s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=2, model__n_estimators=100, preprocessor__num__imputer__strategy=median; total time=   0.4s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=2, model__n_estimators=100, preprocessor__num__imputer__strategy=median; total time=   0.4s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=2, model__n_estimators=100, preprocessor__num__imputer__strategy=median; total time=   0.4s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=2, model__n_estimators=100, preprocessor__num__imputer__strategy=median; total time=   0.4s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=2, model__n_estimators=100, preprocessor__num__imputer__strategy=median; total time=   0.4s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   4.3s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   5.6s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   7.1s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   6.5s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   6.8s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   6.7s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   8.5s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   9.3s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   5.8s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   4.5s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=4, model__n_estimators=100, preprocessor__num__imputer__strategy=mean; total time=   0.4s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=4, model__n_estimators=100, preprocessor__num__imputer__strategy=mean; total time=   0.4s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=4, model__n_estimators=100, preprocessor__num__imputer__strategy=mean; total time=   0.4s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=4, model__n_estimators=100, preprocessor__num__imputer__strategy=mean; total time=   0.6s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=4, model__n_estimators=100, preprocessor__num__imputer__strategy=mean; total time=   0.3s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=4, model__n_estimators=100, preprocessor__num__imputer__strategy=median; total time=   0.4s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=4, model__n_estimators=100, preprocessor__num__imputer__strategy=median; total time=   0.3s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=4, model__n_estimators=100, preprocessor__num__imputer__strategy=median; total time=   0.3s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=4, model__n_estimators=100, preprocessor__num__imputer__strategy=median; total time=   0.4s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=4, model__n_estimators=100, preprocessor__num__imputer__strategy=median; total time=   0.3s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   4.1s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   9.4s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   6.5s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   6.4s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   3.7s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   4.1s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   3.6s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   3.8s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   3.6s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   3.7s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=2, model__n_estimators=100, preprocessor__num__imputer__strategy=mean; total time=   0.2s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=2, model__n_estimators=100, preprocessor__num__imputer__strategy=mean; total time=   0.2s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=2, model__n_estimators=100, preprocessor__num__imputer__strategy=mean; total time=   0.2s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=2, model__n_estimators=100, preprocessor__num__imputer__strategy=mean; total time=   0.3s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=2, model__n_estimators=100, preprocessor__num__imputer__strategy=mean; total time=   0.2s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=2, model__n_estimators=100, preprocessor__num__imputer__strategy=median; total time=   0.2s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=2, model__n_estimators=100, preprocessor__num__imputer__strategy=median; total time=   0.2s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=2, model__n_estimators=100, preprocessor__num__imputer__strategy=median; total time=   0.2s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=2, model__n_estimators=100, preprocessor__num__imputer__strategy=median; total time=   0.2s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=2, model__n_estimators=100, preprocessor__num__imputer__strategy=median; total time=   0.2s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   2.4s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   2.5s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   2.5s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   2.4s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   2.4s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   2.5s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   2.4s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   2.3s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   2.3s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   2.4s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=4, model__n_estimators=100, preprocessor__num__imputer__strategy=mean; total time=   0.2s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=4, model__n_estimators=100, preprocessor__num__imputer__strategy=mean; total time=   0.2s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=4, model__n_estimators=100, preprocessor__num__imputer__strategy=mean; total time=   0.2s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=4, model__n_estimators=100, preprocessor__num__imputer__strategy=mean; total time=   0.2s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=4, model__n_estimators=100, preprocessor__num__imputer__strategy=mean; total time=   0.2s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=4, model__n_estimators=100, preprocessor__num__imputer__strategy=median; total time=   0.2s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=4, model__n_estimators=100, preprocessor__num__imputer__strategy=median; total time=   0.2s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=4, model__n_estimators=100, preprocessor__num__imputer__strategy=median; total time=   0.2s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=4, model__n_estimators=100, preprocessor__num__imputer__strategy=median; total time=   0.2s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=4, model__n_estimators=100, preprocessor__num__imputer__strategy=median; total time=   0.2s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   2.6s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   2.3s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   2.4s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   2.4s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   2.5s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   2.3s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   2.3s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   2.3s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   2.3s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   2.3s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                                        ColumnTransformer(transformers=[(&#x27;cat&#x27;,\n",
       "                                                                         Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                                          SimpleImputer(fill_value=&#x27;missing&#x27;,\n",
       "                                                                                                        strategy=&#x27;constant&#x27;)),\n",
       "                                                                                         (&#x27;onehot&#x27;,\n",
       "                                                                                          OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                                                         [&#x27;Make&#x27;,\n",
       "                                                                          &#x27;Colour&#x27;]),\n",
       "                                                                        (&#x27;door&#x27;,\n",
       "                                                                         Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                                          SimpleImputer(fill_value=4,\n",
       "                                                                                                        strategy=&#x27;constant&#x27;))]),\n",
       "                                                                         [&#x27;Doors&#x27;]),\n",
       "                                                                        (&#x27;num&#x27;,\n",
       "                                                                         Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                                          SimpleImputer())]),\n",
       "                                                                         [&#x27;Odometer &#x27;\n",
       "                                                                          &#x27;(KM)&#x27;])])),\n",
       "                                       (&#x27;model&#x27;, RandomForestRegressor())]),\n",
       "             param_grid={&#x27;model__max_depth&#x27;: [None, 5],\n",
       "                         &#x27;model__max_features&#x27;: [&#x27;auto&#x27;],\n",
       "                         &#x27;model__min_samples_split&#x27;: [2, 4],\n",
       "                         &#x27;model__n_estimators&#x27;: [100, 1000],\n",
       "                         &#x27;preprocessor__num__imputer__strategy&#x27;: [&#x27;mean&#x27;,\n",
       "                                                                  &#x27;median&#x27;]},\n",
       "             verbose=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                                        ColumnTransformer(transformers=[(&#x27;cat&#x27;,\n",
       "                                                                         Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                                          SimpleImputer(fill_value=&#x27;missing&#x27;,\n",
       "                                                                                                        strategy=&#x27;constant&#x27;)),\n",
       "                                                                                         (&#x27;onehot&#x27;,\n",
       "                                                                                          OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                                                         [&#x27;Make&#x27;,\n",
       "                                                                          &#x27;Colour&#x27;]),\n",
       "                                                                        (&#x27;door&#x27;,\n",
       "                                                                         Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                                          SimpleImputer(fill_value=4,\n",
       "                                                                                                        strategy=&#x27;constant&#x27;))]),\n",
       "                                                                         [&#x27;Doors&#x27;]),\n",
       "                                                                        (&#x27;num&#x27;,\n",
       "                                                                         Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                                          SimpleImputer())]),\n",
       "                                                                         [&#x27;Odometer &#x27;\n",
       "                                                                          &#x27;(KM)&#x27;])])),\n",
       "                                       (&#x27;model&#x27;, RandomForestRegressor())]),\n",
       "             param_grid={&#x27;model__max_depth&#x27;: [None, 5],\n",
       "                         &#x27;model__max_features&#x27;: [&#x27;auto&#x27;],\n",
       "                         &#x27;model__min_samples_split&#x27;: [2, 4],\n",
       "                         &#x27;model__n_estimators&#x27;: [100, 1000],\n",
       "                         &#x27;preprocessor__num__imputer__strategy&#x27;: [&#x27;mean&#x27;,\n",
       "                                                                  &#x27;median&#x27;]},\n",
       "             verbose=2)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;cat&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer(fill_value=&#x27;missing&#x27;,\n",
       "                                                                                 strategy=&#x27;constant&#x27;)),\n",
       "                                                                  (&#x27;onehot&#x27;,\n",
       "                                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                                  [&#x27;Make&#x27;, &#x27;Colour&#x27;]),\n",
       "                                                 (&#x27;door&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer(fill_value=4,\n",
       "                                                                                 strategy=&#x27;constant&#x27;))]),\n",
       "                                                  [&#x27;Doors&#x27;]),\n",
       "                                                 (&#x27;num&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer())]),\n",
       "                                                  [&#x27;Odometer (KM)&#x27;])])),\n",
       "                (&#x27;model&#x27;, RandomForestRegressor())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">preprocessor: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;cat&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                  SimpleImputer(fill_value=&#x27;missing&#x27;,\n",
       "                                                                strategy=&#x27;constant&#x27;)),\n",
       "                                                 (&#x27;onehot&#x27;,\n",
       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                 [&#x27;Make&#x27;, &#x27;Colour&#x27;]),\n",
       "                                (&#x27;door&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                  SimpleImputer(fill_value=4,\n",
       "                                                                strategy=&#x27;constant&#x27;))]),\n",
       "                                 [&#x27;Doors&#x27;]),\n",
       "                                (&#x27;num&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputer&#x27;, SimpleImputer())]),\n",
       "                                 [&#x27;Odometer (KM)&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">cat</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Make&#x27;, &#x27;Colour&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(fill_value=&#x27;missing&#x27;, strategy=&#x27;constant&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">door</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Doors&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(fill_value=4, strategy=&#x27;constant&#x27;)</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Odometer (KM)&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-24\" type=\"checkbox\" ><label for=\"sk-estimator-id-24\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer()</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-25\" type=\"checkbox\" ><label for=\"sk-estimator-id-25\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[('preprocessor',\n",
       "                                        ColumnTransformer(transformers=[('cat',\n",
       "                                                                         Pipeline(steps=[('imputer',\n",
       "                                                                                          SimpleImputer(fill_value='missing',\n",
       "                                                                                                        strategy='constant')),\n",
       "                                                                                         ('onehot',\n",
       "                                                                                          OneHotEncoder(handle_unknown='ignore'))]),\n",
       "                                                                         ['Make',\n",
       "                                                                          'Colour']),\n",
       "                                                                        ('door',\n",
       "                                                                         Pipeline(steps=[('imputer',\n",
       "                                                                                          SimpleImputer(fill_value=4,\n",
       "                                                                                                        strategy='constant'))]),\n",
       "                                                                         ['Doors']),\n",
       "                                                                        ('num',\n",
       "                                                                         Pipeline(steps=[('imputer',\n",
       "                                                                                          SimpleImputer())]),\n",
       "                                                                         ['Odometer '\n",
       "                                                                          '(KM)'])])),\n",
       "                                       ('model', RandomForestRegressor())]),\n",
       "             param_grid={'model__max_depth': [None, 5],\n",
       "                         'model__max_features': ['auto'],\n",
       "                         'model__min_samples_split': [2, 4],\n",
       "                         'model__n_estimators': [100, 1000],\n",
       "                         'preprocessor__num__imputer__strategy': ['mean',\n",
       "                                                                  'median']},\n",
       "             verbose=2)"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use GridSearchCV with our regression Pipeline\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "pipe_grid = {\n",
    "    \"preprocessor__num__imputer__strategy\": [\"mean\", \"median\"],\n",
    "    \"model__n_estimators\": [100,1000],\n",
    "    \"model__max_depth\": [None,5],\n",
    "    \"model__max_features\": [\"auto\"],\n",
    "    \"model__min_samples_split\": [2, 4]\n",
    "}\n",
    "\n",
    "gs_model = GridSearchCV(model, pipe_grid, cv=5, verbose=2)\n",
    "gs_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "e790c489-b80c-40d5-8556-8f9af6f2d30a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3339554263158365"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs_model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3f5872a-1fa4-4309-a1e1-ff07187cda21",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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