Classification_Task3_20D070056.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7C5rAxwPGDQf"
   },
   "source": [
    "# Importing useful libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "id": "8qvrslgsF4Mn"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "from pandas import Series, DataFrame\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6aYOLI2BHF6m"
   },
   "source": [
    "## Loading the dataset.\n",
    "The dataset can be found [here](https://github.com/shreedharmalpani/Intro-To-ML-Hello-FOSS/blob/main/iris.csv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "id": "_hccks2pF4Mq"
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"iris.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "id": "um0L09IOF4Ms"
   },
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species\n",
       "0           5.1          3.5           1.4          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2  setosa\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2  setosa"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "id": "cbx8gP4zF4Mt"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 5 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   sepal_length  150 non-null    float64\n",
      " 1   sepal_width   150 non-null    float64\n",
      " 2   petal_length  150 non-null    float64\n",
      " 3   petal_width   150 non-null    float64\n",
      " 4   species       150 non-null    object \n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 6.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vjH1pAqoJna2"
   },
   "source": [
    "# Data Cleaning & Data Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PNfSyZF1F4Mu"
   },
   "source": [
    "### 1) Remove unneeded columns\n",
    "### 2) Check for duplicate rows \n",
    "### 2) Check for rows with missing values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": true,
    "id": "HKFsVhubF4Mx"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</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>142</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>147 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width    species\n",
       "0             5.1          3.5           1.4          0.2     setosa\n",
       "1             4.9          3.0           1.4          0.2     setosa\n",
       "2             4.7          3.2           1.3          0.2     setosa\n",
       "3             4.6          3.1           1.5          0.2     setosa\n",
       "4             5.0          3.6           1.4          0.2     setosa\n",
       "..            ...          ...           ...          ...        ...\n",
       "142           6.7          3.0           5.2          2.3  virginica\n",
       "143           6.3          2.5           5.0          1.9  virginica\n",
       "144           6.5          3.0           5.2          2.0  virginica\n",
       "145           6.2          3.4           5.4          2.3  virginica\n",
       "146           5.9          3.0           5.1          1.8  virginica\n",
       "\n",
       "[147 rows x 5 columns]"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df=df.drop_duplicates().reset_index()\n",
    "df=df[['sepal_length','sepal_width','petal_length','petal_width','species']]\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "L1cnZS5XF4M2"
   },
   "source": [
    "### EDA (Exploratory Data Analysis) with Iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "id": "DjsnhAraF4M7",
    "outputId": "d8d58921-fa2f-4d75-c884-268986342894"
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##Displaying a scatter plot to show the distribution of Sepal Length vs width the dataset\n",
    "\n",
    "fig = df[df.species == 'setosa'].plot(kind='scatter', x='petal_length', y='petal_width', color='orange', label='Setosa')\n",
    "df[df.species == 'versicolor'].plot(kind='scatter', x='petal_length', y='petal_width', color='blue', label='Versicolor', ax=fig)\n",
    "df[df.species == 'virginica'].plot(kind='scatter', x='petal_length', y='petal_width', color='green', label='Virginica', ax=fig)\n",
    "\n",
    "fig.set_xlabel('Petal Length')\n",
    "fig.set_ylabel('Petal Width')\n",
    "fig.set_title('Petal Length Vs Width')\n",
    "\n",
    "fig=plt.gcf()\n",
    "fig.set_size_inches(10, 7)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "id": "O_eYByFvF4M5",
    "outputId": "b7404bc7-4879-482e-ca9d-8d9680a8f6a7"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Display a scatter plot to show the distribution of Sepal Length vs width the dataset (Like previous Petal lenght vs width scatter plot)\n",
    "fig = df[df.species == 'setosa'].plot(kind='scatter', x='sepal_length', y='sepal_width', color='orange', label='Setosa')\n",
    "df[df.species == 'versicolor'].plot(kind='scatter', x='sepal_length', y='sepal_width', color='blue', label='Versicolor', ax=fig)\n",
    "df[df.species == 'virginica'].plot(kind='scatter', x='sepal_length', y='sepal_width', color='green', label='Virginica', ax=fig)\n",
    "\n",
    "fig.set_xlabel('Sepal Length')\n",
    "fig.set_ylabel('Sepal Width')\n",
    "fig.set_title('Sepal Length Vs Width')\n",
    "\n",
    "\n",
    "fig=plt.gcf()\n",
    "fig.set_size_inches(10, 7)\n",
    "plt.show()\n",
    "#example plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "id": "EIh_yKQAF4M6",
    "outputId": "91706df0-4dd1-4a8b-92e7-41e04e6e602d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1caf6d11f10>"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 444.75x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 444.75x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot the FacetGrid plot using the seaborn library\n",
    "facetGrid = sns.FacetGrid(df, hue =\"species\", height=5)\n",
    "facetGrid.map(plt.scatter, \"sepal_length\", \"sepal_width\")\n",
    "facetGrid.add_legend()\n",
    "\n",
    "FacetGrid = sns.FacetGrid(df, hue =\"species\", height=5)\n",
    "FacetGrid.map(plt.scatter, \"petal_length\", \"petal_width\")\n",
    "FacetGrid.add_legend()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "id": "yadZQxoKF4M8",
    "outputId": "639d4b96-87a7-45d0-f358-ef061d08d230"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the distritbution of the features using histgram\n",
    "d=df[['sepal_length','sepal_width','petal_length','petal_width']]\n",
    "columns = d.columns\n",
    "for column in columns:\n",
    "    df.hist(column)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xb-AFaG3PU0D"
   },
   "source": [
    "## Importing alll the necessary packages to use the various classification algorithms\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "id": "cJVjbgAjF4M_"
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression # for Logistic Regression Algorithm\n",
    "from sklearn.model_selection import train_test_split # to split the dataset for training and testing \n",
    "from sklearn import svm # for suport vector machine algorithm\n",
    "from sklearn import metrics # for checking the model accuracy\n",
    "from sklearn.tree import DecisionTreeClassifier # for using DTA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "id": "8LizCSuWF4NA"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(147, 5)"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WW5Hp1fFF4NC"
   },
   "source": [
    "Now, when we train any algorithm, the number of features and their correlation plays an important role. If there are features and many of the features are highly correlated, then training an algorithm with all the featues will reduce the accuracy. Thus features selection should be done carefully. This dataset has less featues but still we will see the correlation.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "id": "YABeXMklF4ND",
    "outputId": "a212e140-712f-4e6f-f8f4-b6664bf29ff1"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,4))\n",
    "sns.heatmap(df.corr(), annot=True, cmap='cubehelix_r') # draws heatmap with input as correlation matrix calculated by df.corr() \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gsd6QaoaF4NE"
   },
   "source": [
    "Observation--->\n",
    "The Sepal Width and Length are not correlated The Petal Width and Length are highly correlated\n",
    "We will use all the features for training the algorithm and check the accuracy.\n",
    "\n",
    "Then we will use 1 Petal Feature and 1 Sepal Feature to check the accuracy of the algorithm as we are using only 2 features that are not correlated. Thus we can have a variance in the dataset which may help in better accuracy. We will check it later.\n",
    "\n",
    "Steps To Be followed When Applying an Algorithm\n",
    "\n",
    "Split the dataset into training and testing dataset. The testing dataset is generally smaller than training one as it will help in training the model better.\n",
    "\n",
    "Select any algorithm based on the problem (classification or regression) whatever you feel may be good.\n",
    "Then pass the training dataset to the algorithm to train it. We use the .fit() method\n",
    "Then pass the testing data to the trained algorithm to predict the outcome. We use the .predict() method.\n",
    "We then check the accuracy by passing the predicted outcome and the actual output to the model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QAD_cNirF4NF"
   },
   "source": [
    "# Splitting The Data into Training And Testing Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "id": "ZqSRd9GzF4NF",
    "outputId": "9504d228-e1ff-4c00-a125-63dd9e0f7d67"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(102, 5)\n",
      "(45, 5)\n"
     ]
    }
   ],
   "source": [
    "train, test = train_test_split(df, test_size=0.3) # our main data split into train and test\n",
    "# the attribute test_size=0.3 splits the data into 70% and 30% ratio. train=70% and test=30%\n",
    "print(train.shape)\n",
    "print(test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "id": "yO2J2FpjF4NG"
   },
   "outputs": [],
   "source": [
    "train_X = train[['sepal_length','sepal_width','petal_length','petal_width']] # taking the training data features\n",
    "train_y = train.species # output of the training data\n",
    "\n",
    "test_X = test[['sepal_length','sepal_width','petal_length','petal_width']] # taking test data feature\n",
    "test_y = test.species # output value of the test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "id": "yR9D2qgQF4NG"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>30</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
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       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>5.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.3</td>\n",
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       "      <th>115</th>\n",
       "      <td>7.7</td>\n",
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       "      <td>6.7</td>\n",
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       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>5.5</td>\n",
       "      <td>2.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width\n",
       "30            4.8          3.1           1.6          0.2\n",
       "51            5.5          2.3           4.0          1.3\n",
       "115           7.7          3.8           6.7          2.2\n",
       "79            5.5          2.4           3.7          1.0\n",
       "24            4.8          3.4           1.9          0.2"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "id": "KcGbNGkcF4NH"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
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       "      <td>6.7</td>\n",
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       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
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       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.3</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.1</td>\n",
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       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
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       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width\n",
       "142           6.7          3.0           5.2          2.3\n",
       "38            5.0          3.5           1.3          0.3\n",
       "12            4.8          3.0           1.4          0.1\n",
       "27            5.2          3.5           1.5          0.2\n",
       "135           6.4          3.1           5.5          1.8"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "id": "5sFmts-IF4NI"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30         setosa\n",
       "51     versicolor\n",
       "115     virginica\n",
       "79     versicolor\n",
       "24         setosa\n",
       "Name: species, dtype: object"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_y.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "S_w4Me2bF4NL"
   },
   "source": [
    "## Logistic Regression "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "id": "gOQ5JrqrF4NL"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of Logistic Regression is:  0.9777777777777777\n"
     ]
    }
   ],
   "source": [
    "model = LogisticRegression()\n",
    "model.fit(train_X, train_y)\n",
    "prediction = model.predict(test_X)\n",
    "print('The accuracy of Logistic Regression is: ', metrics.accuracy_score(prediction, test_y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "e1NNX-EGF4NJ"
   },
   "source": [
    "## Support Vector Machine SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "id": "zSJmVzqnF4NK"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of Support Vector Machine is:  0.9777777777777777\n"
     ]
    }
   ],
   "source": [
    "#implement using Support Vector Machine (SVM)\n",
    "model = svm.SVC()\n",
    "model.fit(train_X, train_y)\n",
    "prediction = model.predict(test_X)\n",
    "print('The accuracy of Support Vector Machine is: ', metrics.accuracy_score(prediction, test_y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GWfemKzPF4NN"
   },
   "source": [
    "## Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "id": "iRXy3EZIF4NN",
    "outputId": "f470e075-fc92-4f3f-c343-7a8257e2c5d2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of Decision Tree is:  0.9555555555555556\n"
     ]
    }
   ],
   "source": [
    "#implementing using Decision Tree\n",
    "model_1 = DecisionTreeClassifier()\n",
    "model_1.fit(train_X, train_y)\n",
    "prediction = model_1.predict(test_X)\n",
    "print('The accuracy of Decision Tree is: ', metrics.accuracy_score(prediction, test_y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uB2Co6f_F4NQ"
   },
   "source": [
    "### We used all the features of iris in above models. Now we will use Petals and Sepals Seperately"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1_v6cAZMF4NQ"
   },
   "source": [
    "### Creating Petals And Sepals Training Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "collapsed": true,
    "id": "e1Q-1b9YF4NQ"
   },
   "outputs": [],
   "source": [
    "petal = df[['petal_length','petal_width','species']]\n",
    "sepal = df[['sepal_length','sepal_width','species']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Lv-nab5oF4NQ"
   },
   "source": [
    "### For Iris Petal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "collapsed": true,
    "id": "DuOqLUWZF4NQ"
   },
   "outputs": [],
   "source": [
    "train_p,test_p = train_test_split(petal, test_size=0.3, random_state=0) #petals\n",
    "train_x_p = train_p[['petal_width','petal_length']]  # taking the training data's Petal features\n",
    "train_y_p = train_p.species  # output of the  training data\n",
    "\n",
    "test_x_p = test_p[['petal_width','petal_length']]   # taking the test data's Petal features\n",
    "test_y_p = test_p.species    # output of the test data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bgNB8kaNF4NU"
   },
   "source": [
    "### For Iris Sepal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "id": "6hVj5MW3F4NU"
   },
   "outputs": [],
   "source": [
    "train_s,test_s = train_test_split(sepal, test_size=0.3, random_state=0) #setals\n",
    "train_x_s = train_s[['sepal_width','sepal_length']]  # taking the training data's Setal features\n",
    "train_y_s = train_s.species  # output of the  training data\n",
    "\n",
    "test_x_s = test_s[['sepal_width','sepal_length']]   # taking the test data's Setal features\n",
    "test_y_s = test_s.species    # output of the test data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "y08e1O6aU9mx"
   },
   "source": [
    "Implementing the algorithms just like we did on the complete dataset but separately on sepals and petals and calculating accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TeMWnQr6F4NV"
   },
   "source": [
    "## SVM Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "id": "jhlutJ78F4NV"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the SVM using Petals is: 1.0\n",
      "The accuracy of the SVM using Sepals is: 0.8\n"
     ]
    }
   ],
   "source": [
    "model_2 = svm.SVC()\n",
    "model_2.fit(train_x_p, train_y_p)\n",
    "prediction = model_2.predict(test_x_p)\n",
    "print('The accuracy of the SVM using Petals is:',metrics.accuracy_score(prediction,test_y_p))\n",
    "\n",
    "model_3 = svm.SVC()\n",
    "model_3.fit(train_x_s, train_y_s)\n",
    "prediction = model_3.predict(test_x_s)\n",
    "print('The accuracy of the SVM using Sepals is:',metrics.accuracy_score(prediction,test_y_s))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Mli7zcq_F4NV"
   },
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "id": "2DqK_dFCF4NV"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the Logistic Regression using Petals is: 1.0\n",
      "The accuracy of the Logistic Regression using Sepals is: 0.7777777777777778\n"
     ]
    }
   ],
   "source": [
    "model = LogisticRegression()\n",
    "model.fit(train_x_p, train_y_p)\n",
    "prediction = model.predict(test_x_p)\n",
    "print('The accuracy of the Logistic Regression using Petals is:',metrics.accuracy_score(prediction,test_y_p))\n",
    "\n",
    "model = LogisticRegression()\n",
    "model.fit(train_x_s, train_y_s)\n",
    "prediction = model.predict(test_x_s) \n",
    "print('The accuracy of the Logistic Regression using Sepals is:',metrics.accuracy_score(prediction,test_y_s))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aM-7Zx95F4NW"
   },
   "source": [
    "## Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "id": "S8tXp-gMF4NW"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the Decision Tree using Petals is: 1.0\n",
      "The accuracy of the Decision Tree using Sepals is: 0.7111111111111111\n"
     ]
    }
   ],
   "source": [
    "model = DecisionTreeClassifier()\n",
    "model.fit(train_x_p, train_y_p)\n",
    "prediction = model.predict(test_x_p)\n",
    "print('The accuracy of the Decision Tree using Petals is:',metrics.accuracy_score(prediction,test_y_p))\n",
    "\n",
    "model = DecisionTreeClassifier()\n",
    "model.fit(train_x_s, train_y_s)\n",
    "prediction = model.predict(test_x_s)\n",
    "print('The accuracy of the Decision Tree using Sepals is:',metrics.accuracy_score(prediction,test_y_s))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6ec0NUyJF4NW"
   },
   "source": [
    "\n",
    "\n",
    "\n",
    "### Question:\n",
    "Does Using Petals over Sepals for training the data give a much better accuracy? Why?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yes,training petals over sepals gives a much better accuracy because from the plots we can see that the petal plots for the three species have very less intermixing compared to the sepals plot."
   ]
  }
 ],
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