Classification_Task3_20b030018.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7C5rAxwPGDQf"
      },
      "source": [
        "#Importing useful libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "collapsed": true,
        "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": 54,
      "metadata": {
        "collapsed": true,
        "id": "_hccks2pF4Mq"
      },
      "outputs": [],
      "source": [
        "df = pd.read_csv(\"iris.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "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": 55,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "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": 57,
      "metadata": {
        "collapsed": true,
        "id": "HKFsVhubF4Mx"
      },
      "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": 57,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.isna().sum()\n",
        "df = df.dropna(how = \"any\", axis=0)\n",
        "df = df.drop_duplicates()\n",
        "df = df.reset_index(drop = True)\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L1cnZS5XF4M2"
      },
      "source": [
        "### EDA (Exploratory Data Analysis) with Iris"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "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": 59,
      "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",
        "\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()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "id": "EIh_yKQAF4M6",
        "outputId": "91706df0-4dd1-4a8b-92e7-41e04e6e602d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x1282b3fbc70>"
            ]
          },
          "execution_count": 60,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1088x360 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "g1 = sns.FacetGrid(df, col = \"species\", height = 5)\n",
        "g1.map(plt.scatter, \"petal_length\", \"petal_width\")\n",
        "g1.add_legend()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x1282c272e80>"
            ]
          },
          "execution_count": 61,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1088x360 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "g2 = sns.FacetGrid(df, col = \"species\", height = 5)\n",
        "g2.map(plt.scatter, \"sepal_length\", \"sepal_width\")\n",
        "g2.add_legend()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "id": "yadZQxoKF4M8",
        "outputId": "639d4b96-87a7-45d0-f358-ef061d08d230"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 0 Axes>"
            ]
          },
          "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": 63,
      "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": 64,
      "metadata": {
        "id": "8LizCSuWF4NA"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(147, 5)"
            ]
          },
          "execution_count": 64,
          "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": 65,
      "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": 66,
      "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": 67,
      "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": 68,
      "metadata": {
        "id": "yR9D2qgQF4NG"
      },
      "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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>82</th>\n",
              "      <td>5.4</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127</th>\n",
              "      <td>7.2</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.8</td>\n",
              "      <td>1.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>5.5</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>5.7</td>\n",
              "      <td>2.9</td>\n",
              "      <td>4.2</td>\n",
              "      <td>1.3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     sepal_length  sepal_width  petal_length  petal_width\n",
              "82            5.4          3.0           4.5          1.5\n",
              "127           7.2          3.0           5.8          1.6\n",
              "35            5.5          3.5           1.3          0.2\n",
              "2             4.7          3.2           1.3          0.2\n",
              "94            5.7          2.9           4.2          1.3"
            ]
          },
          "execution_count": 68,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_X.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "id": "KcGbNGkcF4NH"
      },
      "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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>84</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.1</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1.5</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",
              "    </tr>\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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.8</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.3</td>\n",
              "      <td>1.7</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     sepal_length  sepal_width  petal_length  petal_width\n",
              "84            6.7          3.1           4.7          1.5\n",
              "143           6.3          2.5           5.0          1.9\n",
              "0             5.1          3.5           1.4          0.2\n",
              "42            5.1          3.8           1.9          0.4\n",
              "23            5.1          3.3           1.7          0.5"
            ]
          },
          "execution_count": 69,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "test_X.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "metadata": {
        "id": "5sFmts-IF4NI"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "82     versicolor\n",
              "127     virginica\n",
              "35         setosa\n",
              "2          setosa\n",
              "94     versicolor\n",
              "Name: species, dtype: object"
            ]
          },
          "execution_count": 70,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_y.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S_w4Me2bF4NL"
      },
      "source": [
        "## Logistic Regression "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {
        "id": "gOQ5JrqrF4NL"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The accuracy of Logistic Regression is:  0.9555555555555556\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": 72,
      "metadata": {
        "id": "zSJmVzqnF4NK"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The accuracy of Support Vector Machine is:  0.9111111111111111\n"
          ]
        }
      ],
      "source": [
        "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": 73,
      "metadata": {
        "id": "iRXy3EZIF4NN",
        "outputId": "f470e075-fc92-4f3f-c343-7a8257e2c5d2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The accuracy of Decision Tree is:  0.9333333333333333\n"
          ]
        }
      ],
      "source": [
        "model = DecisionTreeClassifier()\n",
        "model.fit(train_X, train_y)\n",
        "prediction = model.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": 74,
      "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": 75,
      "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": 76,
      "metadata": {
        "id": "6hVj5MW3F4NU"
      },
      "outputs": [],
      "source": [
        "train_s,test_s = train_test_split(sepal, test_size=0.3, random_state=0) #sepals\n",
        "train_x_s = train_s[['sepal_width','sepal_length']]  # taking the training data's Petal 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 Petal 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": 77,
      "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 = svm.SVC()\n",
        "model.fit(train_x_p, train_y_p)\n",
        "prediction = model.predict(test_x_p)\n",
        "print('The accuracy of the SVM using Petals is:',metrics.accuracy_score(prediction,test_y_p))\n",
        "\n",
        "model = svm.SVC()\n",
        "model.fit(train_x_s, train_y_s)\n",
        "prediction = model.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": 78,
      "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": 79,
      "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, Petals data has much better accuracy due to the fact that all the three models are able to successfully separate all points in the space properly. In short we can say that all types of flowers have their petal size somewhat different from other types, whereas is case of sepal size, some sepals of different types of flower have similair size. Hence this leads to better training of petal data."
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "Classification_Task3.ipynb",
      "provenance": []
    },
    "interpreter": {
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    "kernelspec": {
      "display_name": "Python 3.9.7 64-bit",
      "name": "python3"
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    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
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      "file_extension": ".py",
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