_TASK4.ipynb

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    "colab": {
      "name": "#TASK4.ipynb",
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      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMoSiHz36bW5+tcJL5yPp/v",
      "include_colab_link": true
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      "name": "python3",
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Anishpatil-108/LGMVIP-DataScience/blob/main/_TASK4.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QHcPhNFihl9q"
      },
      "source": [
        "#DATA SCIENCE INTERN @Lets Grow More LGMVIP October\n",
        "##Name: Anish Patil\n",
        "###TASK 4 (INTERMEDIATE LEVEL): Prediction using Decision Tree  Algorithm\n",
        "Create the Decision Tree classifier and visualize it graphically. The purpose of creating this model is that if we feed any new data to this classifier, it would be able to predict the right class accordingly​."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V-yJvy7eiAlP"
      },
      "source": [
        "#IMPORT LIBRARIES"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HkvV6ZUtOGkg"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xWw1UGdxOIZB"
      },
      "source": [
        "url = 'https://raw.githubusercontent.com/jbrownlee/Datasets/master/iris.csv'"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EVW32CpSOKSq"
      },
      "source": [
        "col_name = ['Sepal_Length','Sepal_Width','Petal_Length','Petal_Width','Class']"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cso_I8KlOMOj"
      },
      "source": [
        "dataset = pd.read_csv(url, names = col_name)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6pIEK4_YiDWc"
      },
      "source": [
        "#DATA PREPARATION"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "n5rPORL8ON-I",
        "outputId": "43013d81-e4c7-418b-a4c1-5a7213e4c798"
      },
      "source": [
        "dataset"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
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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",
              "      <th>Class</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>Iris-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>Iris-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>Iris-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>Iris-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>Iris-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>145</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.3</td>\n",
              "      <td>Iris-virginica</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>Iris-virginica</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>Iris-virginica</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>Iris-virginica</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>Iris-virginica</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Sepal_Length  Sepal_Width  Petal_Length  Petal_Width           Class\n",
              "0             5.1          3.5           1.4          0.2     Iris-setosa\n",
              "1             4.9          3.0           1.4          0.2     Iris-setosa\n",
              "2             4.7          3.2           1.3          0.2     Iris-setosa\n",
              "3             4.6          3.1           1.5          0.2     Iris-setosa\n",
              "4             5.0          3.6           1.4          0.2     Iris-setosa\n",
              "..            ...          ...           ...          ...             ...\n",
              "145           6.7          3.0           5.2          2.3  Iris-virginica\n",
              "146           6.3          2.5           5.0          1.9  Iris-virginica\n",
              "147           6.5          3.0           5.2          2.0  Iris-virginica\n",
              "148           6.2          3.4           5.4          2.3  Iris-virginica\n",
              "149           5.9          3.0           5.1          1.8  Iris-virginica\n",
              "\n",
              "[150 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
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      "cell_type": "code",
      "metadata": {
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        },
        "id": "9UNvN7BlORcU",
        "outputId": "7ee8c48e-72db-49ff-c458-197c6113b22a"
      },
      "source": [
        "dataset.shape"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(150, 5)"
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          "metadata": {},
          "execution_count": 6
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      "metadata": {
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        "id": "M4VHaKSiORY6",
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      },
      "source": [
        "dataset.size"
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      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "750"
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          "metadata": {},
          "execution_count": 7
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        "outputId": "f3683323-5eaa-400e-823a-f3b849363713"
      },
      "source": [
        "dataset.info()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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   Class         150 non-null    object \n",
            "dtypes: float64(4), object(1)\n",
            "memory usage: 6.0+ KB\n"
          ]
        }
      ]
    },
    {
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        "id": "2r51mUyyOXdt",
        "outputId": "127f7c7f-1c75-487a-d3fd-2fcd78ff49ac"
      },
      "source": [
        "dataset.head()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>Petal_Length</th>\n",
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              "  </thead>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
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              "      <td>Iris-setosa</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
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              "      <td>Iris-setosa</td>\n",
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              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
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              "      <td>Iris-setosa</td>\n",
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              "    <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>Iris-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>Iris-setosa</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "   Sepal_Length  Sepal_Width  Petal_Length  Petal_Width        Class\n",
              "0           5.1          3.5           1.4          0.2  Iris-setosa\n",
              "1           4.9          3.0           1.4          0.2  Iris-setosa\n",
              "2           4.7          3.2           1.3          0.2  Iris-setosa\n",
              "3           4.6          3.1           1.5          0.2  Iris-setosa\n",
              "4           5.0          3.6           1.4          0.2  Iris-setosa"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
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          "height": 203
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        "id": "Kjg5eFatORS7",
        "outputId": "29717e95-3146-433f-89de-4b47b042d00c"
      },
      "source": [
        "dataset.tail()"
      ],
      "execution_count": 10,
      "outputs": [
        {
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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",
              "      <th>Class</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.3</td>\n",
              "      <td>Iris-virginica</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>Iris-virginica</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",
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              "      <td>Iris-virginica</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>Iris-virginica</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>Iris-virginica</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "     Sepal_Length  Sepal_Width  Petal_Length  Petal_Width           Class\n",
              "145           6.7          3.0           5.2          2.3  Iris-virginica\n",
              "146           6.3          2.5           5.0          1.9  Iris-virginica\n",
              "147           6.5          3.0           5.2          2.0  Iris-virginica\n",
              "148           6.2          3.4           5.4          2.3  Iris-virginica\n",
              "149           5.9          3.0           5.1          1.8  Iris-virginica"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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          "height": 295
        },
        "id": "JhGHzJ7KORQY",
        "outputId": "ca3e54dd-114d-4453-dee2-313e328ba123"
      },
      "source": [
        "dataset.describe()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<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>count</th>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>5.843333</td>\n",
              "      <td>3.054000</td>\n",
              "      <td>3.758667</td>\n",
              "      <td>1.198667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.828066</td>\n",
              "      <td>0.433594</td>\n",
              "      <td>1.764420</td>\n",
              "      <td>0.763161</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>4.300000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5.100000</td>\n",
              "      <td>2.800000</td>\n",
              "      <td>1.600000</td>\n",
              "      <td>0.300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>5.800000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.350000</td>\n",
              "      <td>1.300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6.400000</td>\n",
              "      <td>3.300000</td>\n",
              "      <td>5.100000</td>\n",
              "      <td>1.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>7.900000</td>\n",
              "      <td>4.400000</td>\n",
              "      <td>6.900000</td>\n",
              "      <td>2.500000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Sepal_Length  Sepal_Width  Petal_Length  Petal_Width\n",
              "count    150.000000   150.000000    150.000000   150.000000\n",
              "mean       5.843333     3.054000      3.758667     1.198667\n",
              "std        0.828066     0.433594      1.764420     0.763161\n",
              "min        4.300000     2.000000      1.000000     0.100000\n",
              "25%        5.100000     2.800000      1.600000     0.300000\n",
              "50%        5.800000     3.000000      4.350000     1.300000\n",
              "75%        6.400000     3.300000      5.100000     1.800000\n",
              "max        7.900000     4.400000      6.900000     2.500000"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "WFFqAvIDORNP",
        "outputId": "7a2f82c0-d48b-4fbb-9f8b-ec1f27361115"
      },
      "source": [
        "pd.crosstab(dataset.Class, [dataset.Sepal_Length, dataset.Sepal_Width])"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th>Sepal_Length</th>\n",
              "      <th>4.3</th>\n",
              "      <th colspan=\"3\" halign=\"left\">4.4</th>\n",
              "      <th>4.5</th>\n",
              "      <th colspan=\"4\" halign=\"left\">4.6</th>\n",
              "      <th>4.7</th>\n",
              "      <th colspan=\"3\" halign=\"left\">4.8</th>\n",
              "      <th colspan=\"4\" halign=\"left\">4.9</th>\n",
              "      <th colspan=\"8\" halign=\"left\">5.0</th>\n",
              "      <th colspan=\"6\" halign=\"left\">5.1</th>\n",
              "      <th colspan=\"4\" halign=\"left\">5.2</th>\n",
              "      <th>5.3</th>\n",
              "      <th colspan=\"4\" halign=\"left\">5.4</th>\n",
              "      <th>...</th>\n",
              "      <th>6.2</th>\n",
              "      <th colspan=\"7\" halign=\"left\">6.3</th>\n",
              "      <th colspan=\"5\" halign=\"left\">6.4</th>\n",
              "      <th colspan=\"3\" halign=\"left\">6.5</th>\n",
              "      <th colspan=\"2\" halign=\"left\">6.6</th>\n",
              "      <th colspan=\"4\" halign=\"left\">6.7</th>\n",
              "      <th colspan=\"3\" halign=\"left\">6.8</th>\n",
              "      <th colspan=\"2\" halign=\"left\">6.9</th>\n",
              "      <th>7.0</th>\n",
              "      <th>7.1</th>\n",
              "      <th colspan=\"3\" halign=\"left\">7.2</th>\n",
              "      <th>7.3</th>\n",
              "      <th>7.4</th>\n",
              "      <th>7.6</th>\n",
              "      <th colspan=\"4\" halign=\"left\">7.7</th>\n",
              "      <th>7.9</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sepal_Width</th>\n",
              "      <th>3.0</th>\n",
              "      <th>2.9</th>\n",
              "      <th>3.0</th>\n",
              "      <th>3.2</th>\n",
              "      <th>2.3</th>\n",
              "      <th>3.1</th>\n",
              "      <th>3.2</th>\n",
              "      <th>3.4</th>\n",
              "      <th>3.6</th>\n",
              "      <th>3.2</th>\n",
              "      <th>3.0</th>\n",
              "      <th>3.1</th>\n",
              "      <th>3.4</th>\n",
              "      <th>2.4</th>\n",
              "      <th>2.5</th>\n",
              "      <th>3.0</th>\n",
              "      <th>3.1</th>\n",
              "      <th>2.0</th>\n",
              "      <th>2.3</th>\n",
              "      <th>3.0</th>\n",
              "      <th>3.2</th>\n",
              "      <th>3.3</th>\n",
              "      <th>3.4</th>\n",
              "      <th>3.5</th>\n",
              "      <th>3.6</th>\n",
              "      <th>2.5</th>\n",
              "      <th>3.3</th>\n",
              "      <th>3.4</th>\n",
              "      <th>3.5</th>\n",
              "      <th>3.7</th>\n",
              "      <th>3.8</th>\n",
              "      <th>2.7</th>\n",
              "      <th>3.4</th>\n",
              "      <th>3.5</th>\n",
              "      <th>4.1</th>\n",
              "      <th>3.7</th>\n",
              "      <th>3.0</th>\n",
              "      <th>3.4</th>\n",
              "      <th>3.7</th>\n",
              "      <th>3.9</th>\n",
              "      <th>...</th>\n",
              "      <th>3.4</th>\n",
              "      <th>2.3</th>\n",
              "      <th>2.5</th>\n",
              "      <th>2.7</th>\n",
              "      <th>2.8</th>\n",
              "      <th>2.9</th>\n",
              "      <th>3.3</th>\n",
              "      <th>3.4</th>\n",
              "      <th>2.7</th>\n",
              "      <th>2.8</th>\n",
              "      <th>2.9</th>\n",
              "      <th>3.1</th>\n",
              "      <th>3.2</th>\n",
              "      <th>2.8</th>\n",
              "      <th>3.0</th>\n",
              "      <th>3.2</th>\n",
              "      <th>2.9</th>\n",
              "      <th>3.0</th>\n",
              "      <th>2.5</th>\n",
              "      <th>3.0</th>\n",
              "      <th>3.1</th>\n",
              "      <th>3.3</th>\n",
              "      <th>2.8</th>\n",
              "      <th>3.0</th>\n",
              "      <th>3.2</th>\n",
              "      <th>3.1</th>\n",
              "      <th>3.2</th>\n",
              "      <th>3.2</th>\n",
              "      <th>3.0</th>\n",
              "      <th>3.0</th>\n",
              "      <th>3.2</th>\n",
              "      <th>3.6</th>\n",
              "      <th>2.9</th>\n",
              "      <th>2.8</th>\n",
              "      <th>3.0</th>\n",
              "      <th>2.6</th>\n",
              "      <th>2.8</th>\n",
              "      <th>3.0</th>\n",
              "      <th>3.8</th>\n",
              "      <th>3.8</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Class</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Iris-setosa</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>2</td>\n",
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              "      <td>2</td>\n",
              "      <td>...</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Iris-versicolor</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Iris-virginica</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3 rows × 116 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "Sepal_Length    4.3 4.4         4.5 4.6          ... 7.3 7.4 7.6 7.7             7.9\n",
              "Sepal_Width     3.0 2.9 3.0 3.2 2.3 3.1 3.2 3.4  ... 2.9 2.8 3.0 2.6 2.8 3.0 3.8 3.8\n",
              "Class                                            ...                                \n",
              "Iris-setosa       1   1   1   1   1   1   1   1  ...   0   0   0   0   0   0   0   0\n",
              "Iris-versicolor   0   0   0   0   0   0   0   0  ...   0   0   0   0   0   0   0   0\n",
              "Iris-virginica    0   0   0   0   0   0   0   0  ...   1   1   1   1   1   1   1   1\n",
              "\n",
              "[3 rows x 116 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gFH7lDPoiNDv"
      },
      "source": [
        "#DATA VISUALIZATION"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "IHMkgq4PORKi",
        "outputId": "f37abf56-ca2b-4c48-babf-c46b7a86ed4c"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "sns.set_style('white')\n",
        "import seaborn as sns\n",
        "plt.figure(figsize=(21,7))\n",
        "sns.color_palette(\"tab10\")\n",
        "sns.heatmap(pd.crosstab([dataset.Class], [dataset.Sepal_Length]),cmap =\"rocket\", annot = True, cbar = True)\n",
        "plt.xlabel('Sepal Length', size = 15)\n",
        "plt.ylabel('Species', size = 15)\n",
        "plt.title('Sepal Length in Species', size = 18)\n",
        "plt.show()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1512x504 with 2 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "urw2KRo6iR9V"
      },
      "source": [
        "#TRAIN-TEST SPLIT MODEL"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s3RMHxiFORHh"
      },
      "source": [
        "x = dataset.iloc[:,:4].values\n",
        "y = dataset.iloc[:,4:5]\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=0.20,random_state=0)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y9xvKh2nOREh",
        "outputId": "fe8a0b84-7841-4d44-d27f-7fc20ee74529"
      },
      "source": [
        "print(x_train.shape)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(120, 4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u2CiONEaORBi",
        "outputId": "c2dbf55e-c941-48aa-c470-1feaf27ee367"
      },
      "source": [
        "print(x_test.shape)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(30, 4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "odFTuRxkOQ-Y",
        "outputId": "1873e702-49bd-4fad-d0bc-c8b4dfd44c1c"
      },
      "source": [
        "print(y_train.shape)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(120, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wxWT3lMROQ5-",
        "outputId": "0b9dcca1-7fd9-4441-dc7b-9765b4ceae42"
      },
      "source": [
        "print(y_test.shape)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(30, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6W-SAs5OiaAr"
      },
      "source": [
        "#DECISION TREE MODEL"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fYHzzDzVOQ0D"
      },
      "source": [
        "from sklearn import metrics\n",
        "from sklearn.tree import DecisionTreeClassifier"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QTYZ5XEOU0_m"
      },
      "source": [
        "from sklearn.model_selection import train_test_split  \n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y,test_size=0.3, random_state=0)"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B6BKzW09Uf23"
      },
      "source": [
        "x = dataset.drop(\"Class\",axis=1)\n",
        "y = dataset[\"Class\"]"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lsMHLa_-OQpj",
        "outputId": "8591e7b9-ed00-4382-9cf2-37d99066fba0"
      },
      "source": [
        "model = DecisionTreeClassifier()\n",
        "model.fit(x_train, y_train)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
              "                       max_depth=None, max_features=None, max_leaf_nodes=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
              "                       random_state=None, splitter='best')"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M_99jK_fidyl"
      },
      "source": [
        "MODEL ACCURACY"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DC6_ypDyVAAF",
        "outputId": "2aa8876d-7f86-4a59-f227-bc6184386809"
      },
      "source": [
        "model.score(x_test,y_test)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9777777777777777"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "93VbHCmTOQdm",
        "outputId": "7f202ef9-5537-43c5-ce02-f88d5aaa4829"
      },
      "source": [
        "y_pred = model.predict(x_test)\n",
        "print(y_pred)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Iris-virginica' 'Iris-versicolor' 'Iris-setosa' 'Iris-virginica'\n",
            " 'Iris-setosa' 'Iris-virginica' 'Iris-setosa' 'Iris-versicolor'\n",
            " 'Iris-versicolor' 'Iris-versicolor' 'Iris-virginica' 'Iris-versicolor'\n",
            " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-setosa'\n",
            " 'Iris-versicolor' 'Iris-versicolor' 'Iris-setosa' 'Iris-setosa'\n",
            " 'Iris-virginica' 'Iris-versicolor' 'Iris-setosa' 'Iris-setosa'\n",
            " 'Iris-virginica' 'Iris-setosa' 'Iris-setosa' 'Iris-versicolor'\n",
            " 'Iris-versicolor' 'Iris-setosa' 'Iris-virginica' 'Iris-versicolor'\n",
            " 'Iris-setosa' 'Iris-virginica' 'Iris-virginica' 'Iris-versicolor'\n",
            " 'Iris-setosa' 'Iris-virginica' 'Iris-versicolor' 'Iris-versicolor'\n",
            " 'Iris-virginica' 'Iris-setosa' 'Iris-virginica' 'Iris-setosa'\n",
            " 'Iris-setosa']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3zD7iOenih0n"
      },
      "source": [
        "PREDICTION OF MODEL"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MVy2rxi5VQYz",
        "outputId": "8ad9c2bf-89e8-4371-c103-231cec170b34"
      },
      "source": [
        "print(\"Predicted score for the given values is :\",model.predict([[5.1,5.7,5.3,0.1]]))"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted score for the given values is : ['Iris-setosa']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qyfkN_onim_6"
      },
      "source": [
        "#DECISION TREE"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qS7H3sKtH1nN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "662441fd-f9a2-4d47-9986-4cbdd1c7a665"
      },
      "source": [
        "from sklearn import tree\n",
        "import matplotlib.pyplot as plt\n",
        "plt.figure(figsize = (20,30))\n",
        "tree.plot_tree(model, filled=True, rounded=True)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1440x2160 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}