Classification_Task3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7C5rAxwPGDQf"
   },
   "source": [
    "# Importing useful libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": 3,
   "metadata": {
    "id": "_hccks2pF4Mq"
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"iris.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "um0L09IOF4Ms"
   },
   "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>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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": null,
   "metadata": {
    "collapsed": true,
    "id": "HKFsVhubF4Mx"
   },
   "outputs": [],
   "source": [
    "#code\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "L1cnZS5XF4M2"
   },
   "source": [
    "### EDA (Exploratory Data Analysis) with Iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "DjsnhAraF4M7",
    "outputId": "d8d58921-fa2f-4d75-c884-268986342894"
   },
   "outputs": [
    {
     "data": {
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udx+9Y8z6+npWrHicAw88iObmR/jVr+6nrm4G//M/zRx00ExaW5+no2MNkydP6faeRx/9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\nD1CTrky1S6oIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbd075c0>"
      ]
     },
     "metadata": {},
     "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 == 'Iris-setosa'].plot(kind='scatter', x='PetalLengthCm', y='PetalWidthCm', color='orange', label='Setosa')\n",
    "df[df.Species == 'Iris-versicolor'].plot(kind='scatter', x='PetalLengthCm', y='PetalWidthCm', color='blue', label='Versicolor', ax=fig)\n",
    "df[df.Species == 'Iris-virginica'].plot(kind='scatter', x='PetalLengthCm', y='PetalWidthCm', 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": null,
   "metadata": {
    "id": "O_eYByFvF4M5",
    "outputId": "b7404bc7-4879-482e-ca9d-8d9680a8f6a7"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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WxoYND3D00c+ms/M+fvzju8lmj+JnP+vk2c+eT2/vHxkY2MzMmbP2Wef++3/KS1+6iF//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\nK9EvAeA4ZL8WCy8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x456db70>"
      ]
     },
     "metadata": {},
     "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",
    "\n",
    "\n",
    "#code\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": null,
   "metadata": {
    "id": "EIh_yKQAF4M6",
    "outputId": "91706df0-4dd1-4a8b-92e7-41e04e6e602d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0xbd07748>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Tz0hNHRitiC2LcMMVr2JZV2fQH0uqEGQkVjo9xb03fYiXPDtCfH8euyLEb49O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\n0YmISFtTRyciIm3t/wNlY9GprCKBfAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbd07780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot the FacetGrid plot using the seaborn library\n",
    "\n",
    "#sns.FacetGrid(...)\\\n",
    "#   .map(...)\\\n",
    "#   .add_legend()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "yadZQxoKF4M8",
    "outputId": "639d4b96-87a7-45d0-f358-ef061d08d230"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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a9iCKb/UfB54cET8NUPYlu53iWzLAORGxufz56L9RDIb4KYoKdUdm/iVFN4f1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\n332nTTLa3AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc6bad30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the distritbution of the features using histgram\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(12,6)\n",
    "plt.show()"
   ]
  },
  {
   "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": null,
   "metadata": {
    "id": "cJVjbgAjF4M_"
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression # for Logistic Regression Algorithm\n",
    "from sklearn.cross_validation 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": null,
   "metadata": {
    "id": "8LizCSuWF4NA"
   },
   "outputs": [],
   "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": null,
   "metadata": {
    "id": "YABeXMklF4ND",
    "outputId": "a212e140-712f-4e6f-f8f4-b6664bf29ff1"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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a1Uzg7p5fZfF9dz8cyALWu/tggqGO/nWprKZgbeFacnJzKpej0Whl0u/x8z04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\nESV+ERGRNPL/AX2WAx3YBYkFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb86a0f0>"
      ]
     },
     "metadata": {},
     "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": null,
   "metadata": {
    "id": "ZqSRd9GzF4NF",
    "outputId": "9504d228-e1ff-4c00-a125-63dd9e0f7d67"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(105, 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": null,
   "metadata": {
    "id": "yO2J2FpjF4NG"
   },
   "outputs": [],
   "source": [
    "train_X = train[['SepalLengthCm','SepalWidthCm','PetalLengthCm','PetalWidthCm']] # taking the training data features\n",
    "train_y = train.Species # output of the training data\n",
    "\n",
    "test_X = test[['SepalLengthCm','SepalWidthCm','PetalLengthCm','PetalWidthCm']] # taking test data feature\n",
    "test_y = test.Species # output value of the test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "yR9D2qgQF4NG"
   },
   "outputs": [],
   "source": [
    "train_X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "KcGbNGkcF4NH"
   },
   "outputs": [],
   "source": [
    "test_X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "5sFmts-IF4NI"
   },
   "outputs": [],
   "source": [
    "train_y.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "S_w4Me2bF4NL"
   },
   "source": [
    "## Logistic Regression "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "gOQ5JrqrF4NL"
   },
   "outputs": [],
   "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": null,
   "metadata": {
    "id": "zSJmVzqnF4NK"
   },
   "outputs": [],
   "source": [
    "#implement using Support Vector Machine (SVM)\n",
    "#code\n",
    "\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": null,
   "metadata": {
    "id": "iRXy3EZIF4NN",
    "outputId": "f470e075-fc92-4f3f-c343-7a8257e2c5d2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('The accuracy of Decision Tree is: ', 0.93333333333333335)\n"
     ]
    }
   ],
   "source": [
    "#implementing using Decision Tree\n",
    "#code\n",
    "\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": null,
   "metadata": {
    "collapsed": true,
    "id": "e1Q-1b9YF4NQ"
   },
   "outputs": [],
   "source": [
    "petal = df[['PetalLengthCm','PetalWidthCm','Species']]\n",
    "sepal = df[['SepalLengthCm','SepalWidthCm','Species']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Lv-nab5oF4NQ"
   },
   "source": [
    "### For Iris Petal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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[['PetalWidthCm','PetalLengthCm']]  # 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[['PetalWidthCm','PetalLengthCm']]   # 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": null,
   "metadata": {
    "id": "6hVj5MW3F4NU"
   },
   "outputs": [],
   "source": [
    "#Similarly define the split for sepals\n",
    "#define the training and test data's Sepal features followed by the output of the training and test data\n",
    "\n",
    "#use naming- train_s,test_s ; train_x_s, train_y_s; test_x_s, test_y_s\n",
    "\n",
    "#code"
   ]
  },
  {
   "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": null,
   "metadata": {
    "id": "jhlutJ78F4NV"
   },
   "outputs": [],
   "source": [
    "#code\n",
    "print('The accuracy of the SVM using Petals is:',metrics.accuracy_score(prediction,test_y_p))\n",
    "\n",
    "#code\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": null,
   "metadata": {
    "id": "2DqK_dFCF4NV"
   },
   "outputs": [],
   "source": [
    "#code\n",
    "print('The accuracy of the Logistic Regression using Petals is:',metrics.accuracy_score(prediction,test_y_p))\n",
    "\n",
    "#code \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": null,
   "metadata": {
    "id": "S8tXp-gMF4NW"
   },
   "outputs": [],
   "source": [
    "#code\n",
    "print('The accuracy of the Decision Tree using Petals is:',metrics.accuracy_score(prediction,test_y_p))\n",
    "\n",
    "#code\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"
   ]
  }
 ],
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