Iris_Recog.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "1Oy_8Tg_VxkVQS5-TFHknCa97h2DQ7uJM",
      "authorship_tag": "ABX9TyN8AUddyPxw/AT4MW1NcgtO",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mrUnknown69Lkt/CG-MINIPROJ/blob/main/Iris_Recog.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Imports:"
      ],
      "metadata": {
        "id": "b0--jJkqDtOj"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "metadata": {
        "id": "GpV59ejXCXcq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "98200eb5-d093-4006-ae68-adfe628a4f9c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Colab only includes TensorFlow 2.x; %tensorflow_version has no effect.\n"
          ]
        }
      ],
      "source": [
        "%tensorflow_version 2.x"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Import Libraries"
      ],
      "metadata": {
        "id": "dVCs3xxTgp25"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import os\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "ql0gueCzDpxS"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Import Dataset"
      ],
      "metadata": {
        "id": "jnO2k_eUEOmh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"/content/drive/MyDrive/Datasets/Iris.csv\")\n",
        "df.head()"
      ],
      "metadata": {
        "id": "1tCE-UjeEQgT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "8b5d4866-eff1-4cc4-a824-9d3ba54cb0c6"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
              "0   1            5.1           3.5            1.4           0.2  Iris-setosa\n",
              "1   2            4.9           3.0            1.4           0.2  Iris-setosa\n",
              "2   3            4.7           3.2            1.3           0.2  Iris-setosa\n",
              "3   4            4.6           3.1            1.5           0.2  Iris-setosa\n",
              "4   5            5.0           3.6            1.4           0.2  Iris-setosa"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3f6f5500-695c-49ae-bd18-6575e6f1c859\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Id</th>\n",
              "      <th>SepalLengthCm</th>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <th>PetalLengthCm</th>\n",
              "      <th>PetalWidthCm</th>\n",
              "      <th>Species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\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>2</td>\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>3</td>\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</td>\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</td>\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",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3f6f5500-695c-49ae-bd18-6575e6f1c859')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3f6f5500-695c-49ae-bd18-6575e6f1c859 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3f6f5500-695c-49ae-bd18-6575e6f1c859');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "To delete the colums 'Id'"
      ],
      "metadata": {
        "id": "ZI3MBJgOgclO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.drop(columns = ['Id'])\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "p06PgbqhgIsk",
        "outputId": "d22593de-d54f-4877-cb96-46927b6eabb4"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\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"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-afa8d8c4-c8f5-4147-8f4d-4e8ccbcaf8ca\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>SepalLengthCm</th>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <th>PetalLengthCm</th>\n",
              "      <th>PetalWidthCm</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>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",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-afa8d8c4-c8f5-4147-8f4d-4e8ccbcaf8ca')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-afa8d8c4-c8f5-4147-8f4d-4e8ccbcaf8ca button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-afa8d8c4-c8f5-4147-8f4d-4e8ccbcaf8ca');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "To display statistics about the data"
      ],
      "metadata": {
        "id": "SX-_9FnHg12z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "yIKajK9Ig6kE",
        "outputId": "c59f9241-6908-4d0d-a4ab-acd6aba0460b"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\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"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3e965a2f-b77d-49dd-b91a-59f3d9b09c01\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>SepalLengthCm</th>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <th>PetalLengthCm</th>\n",
              "      <th>PetalWidthCm</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>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3e965a2f-b77d-49dd-b91a-59f3d9b09c01')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3e965a2f-b77d-49dd-b91a-59f3d9b09c01 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3e965a2f-b77d-49dd-b91a-59f3d9b09c01');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "To display basic info about the datatype"
      ],
      "metadata": {
        "id": "FOr72haQhR8n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HkHzjQtThG7K",
        "outputId": "ff5d9e05-f085-42d8-df30-8d9df8b13a1c"
      },
      "execution_count": 80,
      "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   SepalLengthCm  150 non-null    float64\n",
            " 1   SepalWidthCm   150 non-null    float64\n",
            " 2   PetalLengthCm  150 non-null    float64\n",
            " 3   PetalWidthCm   150 non-null    float64\n",
            " 4   Species        150 non-null    object \n",
            "dtypes: float64(4), object(1)\n",
            "memory usage: 6.0+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "To display number of samples on each class"
      ],
      "metadata": {
        "id": "6ePSBM5XhYpW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['Species'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NCaZKRkGhJmI",
        "outputId": "0a42315f-fd81-4687-f8bf-94778aa37f92"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Iris-setosa        50\n",
              "Iris-versicolor    50\n",
              "Iris-virginica     50\n",
              "Name: Species, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Preprocessing the dataset"
      ],
      "metadata": {
        "id": "3WEMltPiiDHX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Checking for NULL values"
      ],
      "metadata": {
        "id": "98ojgP5kiSFr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2YrwdeMohtqm",
        "outputId": "8174efc6-2857-4ac4-c973-ade392108fee"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SepalLengthCm    0\n",
              "SepalWidthCm     0\n",
              "PetalLengthCm    0\n",
              "PetalWidthCm     0\n",
              "Species          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Exploratory data analysis"
      ],
      "metadata": {
        "id": "diF7nNRTizmY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['SepalLengthCm'].hist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "RdQuemgYilRo",
        "outputId": "404e1207-e2a8-45ff-f3fe-51a27f15f6a4"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff4043bfa10>"
            ]
          },
          "metadata": {},
          "execution_count": 83
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['SepalWidthCm'].hist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "TB1OgGgEjBUL",
        "outputId": "ae357f95-476a-4dd5-facd-26ac18ca433c"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff404309bd0>"
            ]
          },
          "metadata": {},
          "execution_count": 84
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['PetalLengthCm'].hist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "UZcjcGfCjeIO",
        "outputId": "e3027b53-bb92-4626-ffed-852af3e4cefc"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff404268b90>"
            ]
          },
          "metadata": {},
          "execution_count": 85
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['PetalWidthCm'].hist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "8jbC5Xf_jfjR",
        "outputId": "42b77721-6a6b-42a1-b4f8-396c58218b57"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff4042228d0>"
            ]
          },
          "metadata": {},
          "execution_count": 86
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Scatterplot"
      ],
      "metadata": {
        "id": "RCc21dzukLBs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "colors = ['red' , 'orange' , 'blue']\n",
        "species = ['Iris-virginica' , 'Iris-versicolor' , 'Iris-setosa']"
      ],
      "metadata": {
        "id": "6vnZCyyPjovt"
      },
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(3):\n",
        "  x = df[df['Species'] == species[i]]\n",
        "  plt.scatter(x['SepalLengthCm'] , x['SepalWidthCm'] , c = colors[i] , label = species[i])\n",
        "  plt.xlabel(\"Sepal Length\")\n",
        "  plt.ylabel(\"Sepal Width\")\n",
        "  plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "-4LrhQHQlTxc",
        "outputId": "700b7da4-8ed9-4b0c-f99a-595d411da06e"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAEJCAYAAAB2T0usAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3de5gU1Z3/8feXmYk4jEqiPNGIXIzGBAcY5OItXhBijBKMURcTdCUbQUFXYxKjifm5hl/Imp/ZxDURklmNURkvkY0u8ckmqyJeotEAQRFv8QJGZaOiIoggDN/fH1UDc+nuqp6prq6e/ryep5/uPl1d/e0zRR+qzvmeY+6OiIhUtz7lDkBERMpPjYGIiKgxEBERNQYiIoIaAxERQY2BiIiQQmNgZjVm9hczuyvHa9PM7A0zWx7ezip1PCIi0lVtCp9xAfA0sGue129z9/NSiENERPIoaWNgZgOBE4A5wNeT2Ocee+zhQ4YMSWJXIiJVY+nSpW+6+4B8r5f6zOAq4FvALgW2OdnMjgSeAy50978V2uGQIUNYsmRJgiGKiPR+Zra60Osl6zMws0nA6+6+tMBmvwWGuPsI4G7ghjz7mmFmS8xsyRtvvFGCaEVEqlspO5APByab2SrgVuAYM5vffgN3X+vum8On1wKjc+3I3ZvdfYy7jxkwIO9ZjoiIdFPJGgN3/7a7D3T3IcBpwCJ3P739Nma2V7unkwk6mkVEJGVpjCbqwMxmA0vcfSFwvplNBrYCbwHT0o5HRLpny5YtvPLKK2zatKncoUg7ffv2ZeDAgdTV1RX1Pqu0KazHjBnj6kAWKb+XXnqJXXbZhd133x0zK3c4Arg7a9euZf369QwdOrTDa2a21N3H5HuvMpClorW0wJAh0KdPcN/SUu6IqsemTZvUEGSMmbH77rt362wt9ctEIklpaYEZM2DjxuD56tXBc4CpU8sXVzVRQ5A93f2b6MxAKtall+5oCNps3BiUi0hx1BhIxXr55eLKpfdpaGjI+9phhx3W4/1fdtll3HPPPUW9Z+HChVxxxRUFt3nttdc45ZRTehJa4tSBLBVryJDg0lBngwfDqlVpR1N9nn76aT71qU+VNYaGhgY2bNjQoWzr1q3U1pb2Cnhrays1NTUl/YyeyPW3UQey9Fpz5kB9fcey+vqgXDKohL39ixcv5ogjjmDy5MkMGzYM2HHWsGbNGo488kiamppobGzkwQcf7PDedevWMXjwYLZt2wbAe++9xz777MOWLVuYNm0aCxYsAIKpcC6++GIOOuggbr/9dn73u9/xyU9+ktGjR3P++eczadIkAH71q19x3nnB3JvTpk3j/PPP57DDDmPffffdvq9Vq1bR2NgIBA3LN7/5TRobGxkxYgQ//elPAZg9ezZjx46lsbGRGTNmUOr/uKsDWSpWWyfxpZcGl4YGDQoaAnUeZ1AKvf3Lli3jySef7DKk8uabb+azn/0sl156Ka2trWzs1NG022670dTUxP3338/48eO56667+OxnP5tznP7uu+/OsmXL2LRpE/vvvz8PPPAAQ4cO5Utf+lLeuNasWcNDDz3EM888w+TJk7tcHmpubmbVqlUsX76c2tpa3nrrLQDOO+88LrvsMgDOOOMM7rrrLj7/+c93q27i0JmBVLSpU4NLQtu2BfdqCDIqhd7+cePGdWkIAMaOHcv111/P5ZdfzooVK9hll67zZk6ZMoXbbrsNgFtvvZUpU6bk/Iy28meeeYZ99913++cVagy+8IUv0KdPH4YNG8bf//73Lq/fc889nH322dsvbX3kIx8B4L777uPggw9m+PDhLFq0iJUrVxb6+j2mxkBESi+F3v5+/frlLD/yyCN54IEH2HvvvZk2bRo33ngjd9xxB01NTTQ1NbFkyRImT57M73//e9566y2WLl3KMcccU9RnFLLTTjttfxz3Us+mTZuYNWsWCxYsYMWKFUyfPr3kmd5qDESk9AYNKq48QatXr+ajH/0o06dP56yzzmLZsmWcdNJJLF++nOXLlzNmzBgaGhoYO3YsF1xwAZMmTYrsHD7ggAN48cUXWRWOVGg7q+iOz3zmM/ziF79g69atALz11lvbf/j32GMPNmzYsL2voZTUZyAipTdnTsc+A0itt3/x4sVceeWV1NXV0dDQwI033phzuylTpnDqqaeyePHiyH3uvPPOzJ07l+OOO45+/foxduzYbsd31lln8dxzzzFixAjq6uqYPn065513HtOnT6exsZE999yzR/uPS0NLRaRbih5a2tLSq3r7N2zYQENDA+7Oueeey/7778+FF15Y7rAADS0VkSzrZb39//Ef/0FTUxMHHngg69at4+yzzy53SD2iy0QiIt1w4YUXZuZMIAk6MxARETUGIiKixkBERFBjIGWkhWlEskONgZRF21Q1q1eD+46patQgSDFKPYV1T/Rkmuqjjz6atIfQqzGQstDCNFIqbZm8Dz/8cKqf19nHPvaxVDKHIZj5tKfUGEhZaGGaKvRSC9w5BG7uE9y/lP0prF944QWOO+44Ro8ezRFHHMEzzzwDBFNTn3POORx88MF861vf4v77798+19GoUaNYv359rGmq7733XkaNGsXw4cP5p3/6JzZv3tzlu91yyy0MHz6cxsZGLr744u3lDQ0NfOMb32DkyJE88sgjPa9Ed6+o2+jRo10q3+DB7sEFoo63wYPLHZnE9dRTT8Xf+MX57rfWu7ew43ZrfVDeA/369XN39/vuu8/r6+v9xRdf7PLaj370I//+97/v7u5bt271d999t8t+Jk+e7IsWLXJ391tvvdW/+tWvurv7Mccc488995y7u//pT3/y8ePHu7v7mWee6SeccIJv3brV3d0nTZrkDz30kLu7r1+/3rds2eIvvfSSH3jgge7uPnfuXD/55JN9y5Yt7u6+du1af//9933gwIH+7LPPurv7GWec4T/5yU/c3f2oo47yP//5z/7qq6/6Pvvs46+//rpv2bLFx48f73fccYe7uwN+22235ayXXH8bYIkX+G3VmYGUhRamqTKPXwqtna4Ltm4MyhOS9BTWGzZs4OGHH+bUU0+lqamJs88+mzVr1mx/z6mnnrp9QrvDDz+cr3/961x99dW88847XVZayzVN9bPPPsvQoUP5xCc+AcCZZ57JAw880OF9f/7znzn66KMZMGAAtbW1TJ06dfs2NTU1nHzyyd2tri7UGEhZTJ0Kzc3BEpVmwX1zc8XPUCD5bMxz/S9feTckPYX1tm3b6N+///bZTZcvX87TTz+d8/MuueQSrr32Wt5//30OP/zw7ZeTSqlv376JLr2pxkDKppdNVSOF1OeZqjpfeYK6O4X1rrvuytChQ7n99tuB4JL6448/nvMzXnjhBYYPH87FF1/M2LFjuzQGuaapPuCAA1i1ahXPP/88ADfddBNHHXVUh/eNGzeO+++/nzfffJPW1lZuueWWLtskRY2B5KQcAEnUyDlQ0+m6YE19UF5iixcvZuTIkYwaNYrbbruNCy64IOd2U6ZMYf78+R1WOWtpaeG6665j5MiRHHjggfzXf/1XzvdeddVV2zuH6+rq+NznPtfh9bPOOotBgwYxYsQIRo4cyc0330zfvn25/vrrOfXUUxk+fDh9+vThnHPO6fC+vfbaiyuuuILx48czcuRIRo8ezYknntjDGslNU1hLF52Xq4Xger4u40h7RU9h/VJL0Eew8eXgjGDkHBiqA6oUujOFtWYtlS4K5QCoMZBuGzpVP/4ZpstE0oVyAESqjxoD6aKMy9WKSJmoMZAulAMgUn3UGEgXygEQqT4l70A2sxpgCfCqu0/q9NpOwI3AaGAtMMXdV5U6Jok2dap+/EWqSRpnBhcAT+d57avA2+6+H/AT4IcpxCNVRPkSvVu5prD+wQ9+ULJ9l0tJGwMzGwicAFybZ5MTgRvCxwuACWZmpYxJqofWTKhOaUxhrcageFcB3wK25Xl9b+BvAO6+FVgH7F7imKRKaM2EbCnlWVpPprAGWLlyJePGjaOpqYkRI0bw17/+FYD58+dvLz/77LNpbW3lkksu4f3336epqYmp4bXUH//4xzQ2NtLY2MhVV10FBFNhn3DCCYwcOZLGxsbtE+HNnj2bsWPH0tjYyIwZM8hM4m+hKU17cgMmAXPDx0cDd+XY5klgYLvnLwB75NhuBkG/w5JBgwblnLJVpDOz3NNkm5U7st6hmCms5893r6/v+Heorw/KeyKpKazPO+88nx8Gs3nzZt+4caM/9dRTPmnSJP/ggw/c3X3mzJl+ww03dNi3u/uSJUu8sbHRN2zY4OvXr/dhw4b5smXLfMGCBX7WWWdt3+6dd95x92D66jann366L1y4sGeVkEPWprA+HJhsZquAW4FjzGx+p21eBfYBMLNaYDeCjuQO3L3Z3ce4+5gBAwaUMGTpTZQvkR1pnKX1ZArrQw89lB/84Af88Ic/ZPXq1ey8887ce++9LF26lLFjx9LU1MS9997Liy++2OW9Dz30ECeddBL9+vWjoaGBL37xizz44IMMHz6cu+++m4svvpgHH3yQ3XbbDYD77ruPgw8+mOHDh7No0SJWrlyZXCX0QMkaA3f/trsPdPchwGnAInc/vdNmC4Ezw8enhNtk5JxJKp3yJbIjjaz2nkxh/eUvf5mFCxey8847c/zxx7No0SLcnTPPPHP77KbPPvssl19+eex4PvGJT7Bs2TKGDx/Od7/7XWbPns2mTZuYNWsWCxYsYMWKFUyfPp1NmzYlVAM9k3qegZnNNrPJ4dPrgN3N7Hng68AlaccjvZfyJbKjnGdpcaawfvHFF9l33305//zzOfHEE3niiSeYMGECCxYs4PXXXweCaadXr14NQF1dHVu2bAHgiCOO4M4772Tjxo2899573HHHHRxxxBG89tpr1NfXc/rpp3PRRRexbNmy7T/8e+yxBxs2bEhtjeQ4Upmozt0XA4vDx5e1K98EnJpGDFKdlC+RDXPm5J4JN42ztMWLF3PllVdSV1dHQ0MDN954Y5dtfv3rX3PTTTdRV1fHnnvuyXe+8x0+8pGP8P3vf59jjz2Wbdu2UVdXxzXXXMPgwYOZMWMGI0aM4KCDDqKlpYVp06Yxbtw4IJiuetSoUfzhD3/goosuok+fPtTV1TFv3jz69+/P9OnTaWxsZM8992Ts2LGlr4CYNIW1lMSsWcH/wltboaYm+CGYO7fcUUmSip3CuqUl6CN4+eXgjGDOHDXUpaIprCUTZs2CefN2PG9t3fFcDUL10llatmluIklcc3Nx5SJSfmoMJHGtrcWVS+WqtMvM1aC7fxM1BpK4mpriyqUy9e3bl7Vr16pByBB3Z+3atfTt27fo96rPQBI3Y0bHPoP25dJ7DBw4kFdeeYU33nij3KFIO3379mXgwIFFv0+NgSSurZNYo4l6t7q6upwZv1KZ1BhIScydqx9/kUqiPgMREVFjUI0mTgymZ2i7TZxY7oi6T4vXSOYlcZCmcaAXmtI0i7fRo0cXNZWrdDRhQu5pnSdMKHdkxSvVtMgiiUniIE3oQCdiCmtNR1FlCq0jV2GHAkOGBKuXdTZ4MKxalXY0IjkkcZAmdKBHTUehy0RSsdKYFlmkR5I4SFM60NUYSMXS4jWSeUkcpCkd6GoMqsyECcWVZ5kWr5HMS+IgTelAV2NQZe65p+sP/4QJQXml0eI1knlJHKQpHejqQBYRqQLqQJYu0hj2rPH/IpVF01FUmZaWjssPrl69YwK5uGedUftI4jNEJF26TFRl0hj2rPH/Itmjy0TSQRrDnjX+X6TyqDGoMmkMe9b4f5HKo8agyqQx7Fnj/0UqjxqDKpPGsGeN/xepPOpAFhGpAupATlFWxtZnJQ6RktKBnijlGSQkK2PrsxKHSEnpQE+cLhMlJCtj67MSh0hJ6UAvmi4TpSQrY+uzEodISelAT5wag4RkZWx9VuIQKSkd6IlTY5CQrIytz0ocIiWlAz1xagwSkpWx9VmJQ6SkdKAnTh3IIiJVoMcdyGb2RTP7q5mtM7N3zWy9mb0b4319zewxM3vczFaa2fdybDPNzN4ws+Xh7azoryRRZs2C2trgP0y1tcHzYl6HdIZwa5i4SIa4e8Eb8DzwqajtcrzPgIbwcR3wKHBIp22mAT8rZr+jR492yW/mTHfoeps5M97r7u7z57vX13d8vb4+KE9KGp8hIjsAS7zAb2vkZSIz+6O7H96TBsfM6oGHgJnu/mi78mnAGHc/L+6+dJmosNpaaG3tWl5TA1u3Rr8O6Qzh1jBxkXRFXSbKm4FsZl8MHy4xs9uAO4HNba+7+29ifHgNsBTYD7imfUPQzslmdiTwHHChu/8tx35mADMABmnoWEG5fujbl0e9DukM4dYwcZFsKdRn8PnwtiuwETi2XdmkODt391Z3bwIGAuPMrLHTJr8Fhrj7COBu4IY8+2l29zHuPmbAgAFxPrpq1dQULo96HdIZwq1h4iLZkrcxcPevuPtXgGvbHrcru66YD3H3d4D7gOM6la9197azjWuB0cWFL521Tc+SrzzqdUhnCLeGiYtkTKEOhbA/YVmcshzbDAD6h493Bh4EJnXaZq92j08C/hS1X3UgR5s5072mJuiUranp2Dkc53X3oCN38GB3s+C+FB27aXyGiATobgeymR0KHAZ8DfhJu5d2BU5y95GFGhkzG0Fw2aeG4Azk1+4+28xmh0EtNLN/BSYDW4G3CDqYnym0X3Ugi4gUr9sdyMCHgIZwm13alb8LnBL1we7+BDAqR/ll7R5/G/h21L5ERKS0CvUZ3O/u3yPIDfheu9uP3f2vKcZYMZJIooqTENbTfcSJs6ffJYnvkRkvtcCdQ+DmPsH9S934w6ZR6SI9ke/6EcFIn4X5boWuPZXyltU+gySSqOIkhPV0H3Hi7Ol3SeJ7ZMaL891vrXdvYcft1vqgPK40Kl0kAj3oMzgqfPhFYE9gfvj8S8Df3f3CUjVQhWS1zyCJJKo4CWE93UecOHv6XZL4Hplx5xDYmKMy6gfDF1bF20calS4SIarPIE4G8pLOO8hVlpasNgZ9+gT/nevMDLZti7cPs/yvRfyZYu8jTpw9/S5JfI/MuLkPkCtogy/H/MOmUekiEZJY6ayfme3bbodDgX5JBNebJJFEFSchrKf7iBNnT79LEt8jM+rzfOl85bmkUekiPRSnMbgQWGxmi83sfoLksa+VNqzKk0QSVZyEsJ7uI06cPf0uSXyPzBg5B2o6VUZNfVAeVxqVLtJThToU2m7ATsDI8LZTnPeU6pbVDmT3ZJKo4iSE9XQfceLs6XdJ4ntkxovz3e8Y7N5iwX0xncdt0qh0kQLoQQfyMe6+qN2EdZ0bkciJ6kohq30GIiJZ1pM+g7bRRJ/PcYs1UZ2UR9RwdQ1nz6iWWXBNLbRYeF+G5IxelSAixSiUgXyHmZkHE9NJhWhpCa7Nb9wYPF+9ese1+qlTo1+XMmmZBR/Mgw+Hzz/cCpvnQQswdW46McyaBfPm7Xje2rrj+dyUYpCyKXSZaAmwL8F6BA8DfwQecff16YXXlS4TFRY1XF3D2TPqmtqgAejs7Ro4N6XkjF6VICKddfsyUfimgcAcgkVtzgeeD9c01n8TMipq0RgtKpNR/fOsOrRbnvJSiLPykfRaBYeWuvtGd18M/DvBzKXXEOQYHFfofVI+UcPVNZw9o97Jk4SxLsXkjF6VICLFytsYmNmXzexnZvYQwXxEnwFWAJ92933zvU/KK2q4uoazZ1T/Ge0WlQ1tDsvT0qsSRKRYhTqQfwE8C/wceMDdn0snJOmJtk7gSy8NLv0MGhT80LeVR70uZTJ1btBZ/HZzcGloXU3QEKTVeQw7Oombm4NLQzU1QUOgzuOqUKgDuYYgyeyw8HYAsAZ4hKAjeVFaQbanDmQRkeL1pAO51d2XufvP3P3LwPHA74GvECxe36skMfY+ah9pDeFWHkGRklivIA1ReQhp/eGTSGRJa40IiS9fajIwAjgHuBF4HngZuBW4ABhTKK25lLdSTEeRxFTyUftIa45/TYtfpCTWK0jD/Jnuv6RjnL8kKHdP7w8f9Tlx4khrjQjpgB5MR7EMeIjgstAf3T0Tgw9LcZkoibH3UftIawi38giKlMR6BWmIykNI6w+fRCJLWmtESAc9Xs8ga0rRGCQxlXzUPtKa41/T4hcpifUK0tBikOsY2gacHnOhiiREfU6cONJaI0I6SGI9g14vibH3UftIawi38giKlMR6BWmIykNI6w+fRCJLWmtESFHUGJDM2PuofaQ1hFt5BEVKYr2CNETlIaT1h08ikSWtNSKkOIU6FLJ4K9V6BklMJR+1j7Tm+Ne0+EVKYr2CNMyf6f6zGvebCO7nd2OhikTiiPicOHGktUaEbEcPOpB/S+4Le22NyOQStU8FKc9ARKR4Pekz+BHwbwVu0kkauQoTJwZ9ZG23iRN7HrdUiDTG1bdMhJss6LC+yYLnxbpsIlwd7uNqC55L5mk0UUI6rxMAwSXM5ub4Uz1E7WPiRLj33q7vmzAB7rmnZ/FLxiVxgEV+xkTg3o6jlhxgAkyNeYBdNhGG3hsslNtmM/DSBJitg7Scejy01Mz2B/4VGAb0bSv3Mk1Wl9XGII1chbSGp0oGpTGu/iaDXIOWWoEzYh5gVxvskaP8TeB8HaTllMTQ0uuBecBWYDxBRvL8ZMLrPZJYJ0BrDUheaRwc+X4NihlzuHue8o8UGYukLs6feWd3v5fgLGK1u18OnFDasCpPGrkKUsXSODjy5WoVk8O1Nk/5W0XGIqmL0xhsNrM+wF/N7DwzOwloKHFcFSeNXIUJE3K/L1+59CJpjKvvM6Hr+EEPy+N6c0LufIg3dZBmXZzG4AKgnmDZy9HAGcCZpQyqEk2dGvTlDR4cXNsfPLj4vr2ofdxzT9cffnUeV4kkDrDIz7gHmBD0ETjBfTGdxxB0Er80Iegj2EZwr87jihB7NJGZ7Qq4u68vbUiFZbUDWUQky3rcgWxmY8xsBfAEsMLMHjez0THe19fMHgu3X2lm38uxzU5mdpuZPW9mj5rZkKj9iohI8uJcJvolMMvdh7j7EOBcghFGUTYDx7j7SKAJOM7MDum0zVeBt919P+AnwA9jR16EOLk6WVknI2oBnIr5LkksXvLYLLilFm624P6xHKsBJbJISgKLxkTtIw1xMhKj6ivO90gl+a1CDvRKiTOOQnNVhJeQ/pKjbFnU+zptXw8sAw7uVP4H4NDwcS3BFUYrtK9i5yaKswZGVtbJiFoAp2K+SxKLlzw6s+P7226PtpuPJ5FFUhJYNCZqH2mYMCH3wTNhwo5touorzvdI4wCrlAO9UuIM0d25idqY2VXAzsAtBN1KU4BNhLkG7r6swHtrgKXAfsA17n5xp9efBI5z91fC5y+EDcab+fZZbJ9BnFydrKyTEbUATsV8lyQWL7mlFjxHZVgNfGlrcp+TxKIxUftIQ5yMxKj6ivM90jjAKuVAr5Q4Q1F9BrUx9jEyvP+XTuWjCBqHY/K90d1bgSYz6w/cYWaN7v5kjM/swMxmADMABhU5rjpOrk5Wkr1yNQTtyyvmu2zM82H5ynPJ1RB0Lk/ic/rn+Zzdiqj0qH1kRVR9xfkeaRxglXKgV0qcMUX2Gbj7+AK3vA1Bp328A9wHHNfppVeBfQDMrBbYjRxpK+7e7O5j3H3MgAED4nzkdnFydbKS7BW1AE7FfJckFi+xPJXRvjyJz0li0ZiofWRFVH3F+R5pHGCVcqBXSpwxxRlN9FEzu87M/jt8PszMvhrjfQPCMwLMbGfgM8AznTZbyI6chVOARR513apIcXJ1srJORtQCOBXzXZJYvOTjeSqjfXkSn5PEojFR+0hDnIzEqPqK8z3SOMAq5UCvlDjjKtShEP4u/zfwD8DjvqOjd0WM940A/kIwJPVJ4LKwfDYwOXzcF7gdeB54DNg3ar/dWdwmzhoYWVknI2oBnIr5LkksXvLoTPeba4KOzJtrOnYeJ/k5SSwaE7WPNHTuRG7fedwmqr7ifI80DrBKOdArJU5PpgP5z+4+1sz+4u6jwrLl7t6UbLMUj5LORESKl8Sspe+Z2e6Es5aEuQLrEoovMyplKHBViZNDkESeQRpxxNlH1EGYxHdNo76yQv+oixJnNNHXCa7tf9zM/ggMILi+32t0Xjdk9eod1+mTnPpFivBSCzw2A1rDP8rG1cFzgKFT42+ThTji7CPqIEziu6ZRX1mhf9RFizU3UTjS5wCCNZCedfctpQ4sn1JcJsrQUGBpEyeHIIk8gzTiiLOPqIMwie+aRn1lhf5Rd9Hty0RmNtbM9gRw960EM5bOAf7NzHrVUhUVNBS4esTJIUgizyCNOOLsI+ogTOK7plFfWaF/1EUr1GfwC+ADADM7EriCYJWzdUBz6UNLTwUNBa4ecXIIksgzSCOOOPuIOgiT+K5p1FdW6B910Qo1BjXu3rY+0RSg2d3/093/D8H0Er1GJQ0FrhpxcgiSyDNII444+4g6CJP4rmnUV1boH3Xx8o05JcgNqA0fPwMc2f61QuNVS3nrTp5BHBkZCiztxckhSCLPII044uwj6iBM4rumUV9ZoX/UHdDdPAMzuxQ4nmAm0UHAQe7uZrYfcIO7H17ylioH5RmIiBSv2x3I7j4H+AbwK+DTvqPV6AP8c5JBiuQUZ5x41JoHaY01TyKOqG2iFrtIK4egN+UqZGVthiwodNqQxVupLhNJxsSZBz5qzYO05pJPIo6obaIWu0hibYc40vqcNGRlbYaU0NPpKLJGl4mqRJxx4lFrHqQ11jyJOKK2iVrsIq0cgt6Uq5CVtRlSksR0FCLpizNOPGrNg7TGmicRR9Q2UYtdpJVD0JtyFbKyNkNGqDGQbIozTjxqzYO0xponEUfUNlGLXaSVQ9CbchWysjZDRqgxkGyKM048as2DtMaaJxFH1DZRi12klUPQm3IVsrI2Q1YU6lDI4k0dyFUkzjjxqDUP0hprnkQcUdtELXaRVg5Bb8pVyMraDClAHcgiIqIOZOmeLIwlTyKGKw+EmwxaLLi/8sDyxBFH1Hj0ShmvLhUpznoGUm2yMO99EjFceSB87Klg4nWAGoLnVx4IF61ML444oubf1/z8UmK6TCRdZWEseSWPzvgAAA32SURBVBIx3GRBA9BZK3BGzOM+rbqIGo+eofHqUpl0mUiKl4Wx5EnEkO/oLuaoT6suosajV9B4dalMagykqyyMJU8ihm1FlpcqjjiixqNX0Hh1qUxqDKSrLIwlTyKG/x0Gna8GeVieZhxxRI1Hr6Tx6lKR1BhIV0Onwrjm4Lo4FtyPa0530fQkYrhoJbw2LOgjcIL714bF7zxOKo44pk6F5uagD8AsuG9u3tE5HPW6SA+pA1lEpAqoA1myK4nx+1H7yEqOgFSvCjk2lGcg5ZHE+P2ofWQlR0CqVwUdG7pMJOWRxPj9qH1kJUdAqleGjg1dJpJsSmL8ftQ+spIjINWrgo4NNQZSHkmM34/aR1ZyBKR6VdCxocZAyiOJ8ftR+8hKjoBUrwo6NtQYSHkkMX4/ah9ZyRGQ6lVBx4Y6kEVEqkDZOpDNbB8zu8/MnjKzlWZ2QY5tjjazdWa2PLxdVqp4REQkv1JeJtoKfMPdhwGHAOeaWa5JYR5096bwNruE8fQOSSSwZGHhmjhxxImzQhJ6YmmZBdfUBgvxXFMbPE89hl5Un1KUkiWdufsaYE34eL2ZPQ3sDTxVqs/s9ZJIYMnCwjVx4ogTZwUl9ERqmQUfzIMPh88/3Aqb50ELMHVuSjH0ovqUoqXSZ2BmQ4AHgEZ3f7dd+dHAfwKvAK8B33T3grOIVXWfQRIJLFlYuCZOHHHizFBCT49dUxs0AJ29XQPnbk0nht5Un9JFVJ9ByaejMLMGgh/8r7VvCELLgMHuvsHMjgfuBPbPsY8ZwAyAQRkcn5uaJBJYsrBwTZw44sRZQQk9kfrnaAgAdstTXgq9qT6laCUdWmpmdQQNQYu7/6bz6+7+rrtvCB//Dqgzsz1ybNfs7mPcfcyAAQNKGXK2JZHAkoWFa+LEESfOCkroifROrvU5gXV5ykuhN9WnFK2Uo4kMuA542t1/nGebPcPtMLNxYTxrSxVTxUsigSULC9fEiSNOnBWU0BOp/wzY3Klsc1ielt5Un1K0Up4ZHA6cARzTbujo8WZ2jpmdE25zCvCkmT0OXA2c5pWW+JCmJBJYsrBwTZw44sRZQQk9kabOhQ/NDPoIthHcf2hmep3H0LvqU4qmpDMRkSqgWUt7m6zkCCThsVlwSy3cbMH9Y2UYVy8igBa3qSxZyRFIwmOz4Pl5O557647n41K8NCIigM4MKsvjl+5oCNq0bgzKK80LzcWVi0hJqTGoJFnJEUiC5xk/n69cREpKjUElyUqOQBIsz/j5fOUiUlJqDCpJVnIEkvDxPOPn85WLSEmpMagkWckRSMK4ubDfzB1nAlYTPFfnsUhZKM9ARKQKKM8gIRU1zXul5CJUSpxpUX1IGSnPIIaKmua9UnIRKiXOtKg+pMx0mSiGiprmPSvrFUSplDjTovqQEtNlogRU1DTvlZKLUClxpkX1IWWmxiCGiprmvVJyESolzrSoPqTM1BjEUFHTvFdKLkKlxJkW1YeUmRqDGCpqmvdKyUWolDjTovqQMlMHsohIFVAHskhPtcyCa2qhxcL7bqy7oBwCyTg1BiKFtMyCD+bBh1vBCO4/mFdcg9CWQ7BxNeA7cgjUIEiGqDEQKeSdZtipU9lOYXlcvWkdCum11BiIFNI/z/oKuxWx7oJyCKQCqDEQKeSdPOsrrCti3QXlEEgFUGMgUkj/GbC5U9nmsDwu5RBIBVBjIFLI1LnwoZnwdg1sI7j/0MygPC7lEEgFUJ6BiEgVUJ6BiIhEUmMgIiJqDERERI2BiIigxkBERFBjICIiqDEQERHUGIiICCVsDMxsHzO7z8yeMrOVZnZBjm3MzK42s+fN7AkzO6hU8YiISH6lPDPYCnzD3YcBhwDnmtmwTtt8Dtg/vM0A5pUwnuqhhVREpEglawzcfY27LwsfrweeBvbutNmJwI0e+BPQ38z2KlVMVUELqYhIN6TSZ2BmQ4BRwKOdXtob+Fu756/QtcGQYmghFRHphpI3BmbWAPwn8DV3f7eb+5hhZkvMbMkbb7yRbIC9jRZSEZFuKGljYGZ1BA1Bi7v/JscmrwL7tHs+MCzrwN2b3X2Mu48ZMGBAaYLtLbSQioh0QylHExlwHfC0u/84z2YLgX8MRxUdAqxz9zWliqkqaCEVEemG2hLu+3DgDGCFmS0Py74DDAJw958DvwOOB54HNgJfKWE81aFtwZTHLw0uDdUPChoCLaQiIgWUrDFw94cAi9jGgXNLFUPVGjpVP/4iUhRlIIuIiBoDERFRYyAiIqgxEBER1BiIiAhgwYCeymFmbwCryxjCHsCbZfz8YlRKrIozWZUSJ1ROrL0hzsHunjdrt+Iag3IzsyXuPqbcccRRKbEqzmRVSpxQObFWQ5y6TCQiImoMREREjUF3NJc7gCJUSqyKM1mVEidUTqy9Pk71GYiIiM4MREREjUFBZlZjZn8xs7tyvDbNzN4ws+Xh7awyxbjKzFaEMSzJ8bqZ2dVm9ryZPWFmB5UjzjCWqFiPNrN17er0sjLF2d/MFpjZM2b2tJkd2un1TNRpjDizUp8HtIthuZm9a2Zf67RN2es0ZpxZqdMLzWylmT1pZreYWd9Or+9kZreF9flouNpkQaWcwro3uIBg7eZd87x+m7ufl2I8+Yx393xjiz8H7B/eDgbmhfflUihWgAfdfVJq0eT278Dv3f0UM/sQ0GmBiMzUaVSckIH6dPdngSYI/oNFsIDVHZ02K3udxowTylynZrY3cD4wzN3fN7NfA6cBv2q32VeBt919PzM7DfghMKXQfnVmkIeZDQROAK4tdyw9dCJwowf+BPQ3s73KHVRWmdluwJEECzPh7h+4+zudNit7ncaMM4smAC+4e+fE0bLXaSf54syKWmBnM6sl+E/Aa51ePxG4IXy8AJgQLjiWlxqD/K4CvgVsK7DNyeEp7QIz26fAdqXkwP+Y2VIzm5Hj9b2Bv7V7/kpYVg5RsQIcamaPm9l/m9mBaQYXGgq8AVwfXiK81sz6ddomC3UaJ04of312dhpwS47yLNRpe/nihDLXqbu/CvwIeBlYQ7BC5P902mx7fbr7VmAdsHuh/aoxyMHMJgGvu/vSApv9Fhji7iOAu9nRCqft0+5+EMFp9rlmdmSZ4ogjKtZlBCnzI4GfAnemHSDB/7gOAua5+yjgPeCSMsQRJU6cWajP7cJLWZOB28sZR5SIOMtep2b2YYL/+Q8FPgb0M7PTe7pfNQa5HQ5MNrNVwK3AMWY2v/0G7r7W3TeHT68FRqcb4vY4Xg3vXye4vjmu0yavAu3PWgaGZamLitXd33X3DeHj3wF1ZrZHymG+Arzi7o+GzxcQ/Oi2l4U6jYwzI/XZ3ueAZe7+9xyvZaFO2+SNMyN1OhF4yd3fcPctwG+Awzpts70+w0tJuwFrC+1UjUEO7v5tdx/o7kMIThcXuXuHlrfT9czJBB3NqTKzfma2S9tj4FjgyU6bLQT+MRytcQjBKeWalEONFauZ7dl2XdPMxhEcnwUP4KS5+/8CfzOzA8KiCcBTnTYre53GiTML9dnJl8h/6aXsddpO3jgzUqcvA4eYWX0YywS6/v4sBM4MH59C8BtWMKlMo4mKYGazgSXuvhA438wmA1uBt4BpZQjpo8Ad4bFZC9zs7r83s3MA3P3nwO+A44HngY3AV8oQZ9xYTwFmmtlW4H3gtKgDuET+GWgJLxe8CHwlo3UaFWdW6rPtPwCfAc5uV5a5Oo0RZ9nr1N0fNbMFBJestgJ/AZo7/T5dB9xkZs8T/D6dFrVfZSCLiIguE4mIiBoDERFBjYGIiKDGQEREUGMgIiKoMZBexswuDWdzfCKcVTLRyc4smLUy1yy2OcsT/Nz+ZjYrrc+T6qM8A+k1LJjCeRJwkLtvDjNDP1TmsJLSH5gFzC13INI76cxAepO9gDfbpglx9zfd/TUAMxttZveHk+T9oS2D3MwWm9m/h2cRT4ZZpZjZODN7JJwE7uF2mb5FMbNjw/0sM7PbzawhLF9lZt8Ly1eY2SfD8gFmdnd4dnOtma0OG7UrgI+HcV4Z7r7Bdqxn0NKWGSvSHWoMpDf5H2AfM3vOzOaa2VEAZlZHMKnYKe4+GvglMKfd++rdvYngf96/DMueAY4IJ4G7DPhBscGEP+LfBSaGE/QtAb7ebpM3w/J5wDfDsn8hmDrgQIL5hgaF5ZcQTKnc5O4XhWWjgK8Bw4B9CebUEukWXSaSXsPdN5jZaOAIYDxwm5ldQvAj3AjcHf7nuYZg6t82t4Tvf8DMdjWz/sAuwA1mtj/B1Nt13QjpEIIf6j+Gn/sh4JF2r/8mvF8KfDF8/GngpDCe35vZ2wX2/5i7vwJgZsuBIcBD3YhTRI2B9C7u3gosBhab2QqCybqWAivd/dB8b8vx/P8C97n7SRYsGbi4G+EYcLe7fynP622z3rbSvX+Lm9s97u4+RABdJpJexII1bPdvV9QErAaeBQaEHcyYWZ11XJRkSlj+aYLZMtcRTPnbNoXytG6G9CfgcDPbL9x/PzP7RMR7/gj8Q7j9scCHw/L1BGcrIiWhxkB6kwaCSztPmdkTBJdoLnf3Dwhmm/yhmT0OLKfj/O+bzOwvwM8J1o4F+H/Av4blcf/HPcHMXmm7AfsRNCS3hPE8AnwyYh/fA441syeBU4H/Bda7+1qCy01PtutAFkmMZi2VqmZmi4FvuvuScscCYGY7Aa3uvjU8k5kXdm6LlJSuMYpkyyDg12bWB/gAmF7meKRK6MxARETUZyAiImoMREQENQYiIoIaAxERQY2BiIigxkBERID/D1tZsI4/KeP4AAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(3):\n",
        "  x = df[df['Species'] == species[i]]\n",
        "  plt.scatter(x['PetalLengthCm'] , x['PetalWidthCm'] , c = colors[i] , label = species[i])\n",
        "  plt.xlabel(\"Petal Length\")\n",
        "  plt.ylabel(\"Petal Width\")\n",
        "  plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "S-TtvtARl_D-",
        "outputId": "366f798c-3c95-40ae-ca47-8bff7dd654d0"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(3):\n",
        "  x = df[df['Species'] == species[i]]\n",
        "  plt.scatter(x['SepalLengthCm'] , x['PetalLengthCm'] , c = colors[i] , label = species[i])\n",
        "  plt.xlabel(\"Sepal Length\")\n",
        "  plt.ylabel(\"Petal Length\")\n",
        "  plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "tTTA_Ge9nYhm",
        "outputId": "a24dc642-020a-4184-847b-fb858071a845"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(3):\n",
        "  x = df[df['Species'] == species[i]]\n",
        "  plt.scatter(x['SepalWidthCm'] , x['PetalWidthCm'] , c = colors[i] , label = species[i])\n",
        "  plt.xlabel(\"Sepal Width\")\n",
        "  plt.ylabel(\"Petal Width\")\n",
        "  plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "LMgJVfIMnhqY",
        "outputId": "510a5e4d-e00a-40b4-d435-846e19f4927b"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Correlation Matrix"
      ],
      "metadata": {
        "id": "8gYyvyy2qc0r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "ADUNAkCgqcKo",
        "outputId": "bf06fcf7-cdaf-441d-e1c0-fbebdbd8aac3"
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
              "SepalLengthCm       1.000000     -0.109369       0.871754      0.817954\n",
              "SepalWidthCm       -0.109369      1.000000      -0.420516     -0.356544\n",
              "PetalLengthCm       0.871754     -0.420516       1.000000      0.962757\n",
              "PetalWidthCm        0.817954     -0.356544       0.962757      1.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d44d65a7-8ba3-4e0b-8476-93ef15d518d0\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>SepalLengthCm</th>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <th>PetalLengthCm</th>\n",
              "      <th>PetalWidthCm</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>SepalLengthCm</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.109369</td>\n",
              "      <td>0.871754</td>\n",
              "      <td>0.817954</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <td>-0.109369</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.420516</td>\n",
              "      <td>-0.356544</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PetalLengthCm</th>\n",
              "      <td>0.871754</td>\n",
              "      <td>-0.420516</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.962757</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PetalWidthCm</th>\n",
              "      <td>0.817954</td>\n",
              "      <td>-0.356544</td>\n",
              "      <td>0.962757</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d44d65a7-8ba3-4e0b-8476-93ef15d518d0')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d44d65a7-8ba3-4e0b-8476-93ef15d518d0 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d44d65a7-8ba3-4e0b-8476-93ef15d518d0');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "corr = df.corr()\n",
        "fig, ax = plt.subplots(figsize=(5,4))\n",
        "sns.heatmap(corr , annot = True , ax = ax , cmap = 'coolwarm')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "id": "Dx_ZG9GfqhuW",
        "outputId": "bccc9eec-6a64-4306-8db0-9da56a4f20e0"
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff403f70dd0>"
            ]
          },
          "metadata": {},
          "execution_count": 93
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Label Encoder"
      ],
      "metadata": {
        "id": "N21EXwELrrsn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "le = LabelEncoder()"
      ],
      "metadata": {
        "id": "d0hc8UugrCBW"
      },
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Species'] = le.fit_transform(df['Species'])\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "WRalIutAsHCf",
        "outputId": "a1f6daf0-c615-44f6-d557-2bf029043843"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  Species\n",
              "0            5.1           3.5            1.4           0.2        0\n",
              "1            4.9           3.0            1.4           0.2        0\n",
              "2            4.7           3.2            1.3           0.2        0\n",
              "3            4.6           3.1            1.5           0.2        0\n",
              "4            5.0           3.6            1.4           0.2        0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-29400e1f-9202-4f81-99f6-51e90ac61037\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>SepalLengthCm</th>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <th>PetalLengthCm</th>\n",
              "      <th>PetalWidthCm</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>0</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>0</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>0</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>0</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>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-29400e1f-9202-4f81-99f6-51e90ac61037')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-29400e1f-9202-4f81-99f6-51e90ac61037 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-29400e1f-9202-4f81-99f6-51e90ac61037');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Model Training"
      ],
      "metadata": {
        "id": "y0G0-0kGsk4u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "#train - 70\n",
        "#test - 30\n",
        "X = df.drop(columns = ['Species'])\n",
        "Y = df['Species']\n",
        "x_train , x_test , y_train , y_test = train_test_split(X , Y , test_size = 0.30)"
      ],
      "metadata": {
        "id": "xFSdPisHsnGk"
      },
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Logistic Regression"
      ],
      "metadata": {
        "id": "317goP-_t4Cm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "model = LogisticRegression()"
      ],
      "metadata": {
        "id": "gebHjGyjt0QS"
      },
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model Training"
      ],
      "metadata": {
        "id": "JYsTIGSruhB3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(x_train , y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k_Sx5oVWuJTa",
        "outputId": "53aaeca3-7d12-4cb1-f92e-b7a73a6f17da"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Print Metric to get performance"
      ],
      "metadata": {
        "id": "jRun8mHguboT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Accuracy: \",model.score(x_test , y_test) * 100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "98kmAyYcuV3j",
        "outputId": "5815028a-fe21-4071-e604-496ba485920c"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy:  100.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "KNN - K-nearest neighbors"
      ],
      "metadata": {
        "id": "BkDu_-1yu7Yo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#from sklearn.neighbors import KNeighborsClassifier\n",
        "#model = KNeighborsClassifier()"
      ],
      "metadata": {
        "id": "EdQF4bkXuwVP"
      },
      "execution_count": 100,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#model.fit(x_train , y_train)"
      ],
      "metadata": {
        "id": "BVGz9JmqvXOo"
      },
      "execution_count": 101,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#print(\"Accuracy: \",model.score(x_test , y_test)*100)"
      ],
      "metadata": {
        "id": "Fjy_LGu9vmgW"
      },
      "execution_count": 102,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Descision tree"
      ],
      "metadata": {
        "id": "vPXU_ypywX5m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#from sklearn.tree import DecisionTreeClassifier\n",
        "#model = DecisionTreeClassifier()"
      ],
      "metadata": {
        "id": "cG_saIcGwXSg"
      },
      "execution_count": 103,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#model.fit(x_train , y_train)"
      ],
      "metadata": {
        "id": "drbt33UIwnzf"
      },
      "execution_count": 104,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#print(\"Accuracy: \",model.score(x_test , y_test)*100)"
      ],
      "metadata": {
        "id": "lpfcWSSXwqOS"
      },
      "execution_count": 105,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Predictions"
      ],
      "metadata": {
        "id": "0cSYwWADy5NV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "expected = Y\n",
        "predicted = model.predict(X)\n",
        "predicted"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dk3IxRuDwsIE",
        "outputId": "69a6b9a2-e37b-426f-884a-977ec7949d6d"
      },
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import metrics"
      ],
      "metadata": {
        "id": "Tzn-5NLjy4vI"
      },
      "execution_count": 107,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Report for Predictions"
      ],
      "metadata": {
        "id": "bO1IiFS60YEq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(metrics.classification_report(expected , predicted))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s1XMAt_kzBTG",
        "outputId": "82473fd5-d13e-46bf-83d4-6038fff9be24"
      },
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        50\n",
            "           1       0.98      0.94      0.96        50\n",
            "           2       0.94      0.98      0.96        50\n",
            "\n",
            "    accuracy                           0.97       150\n",
            "   macro avg       0.97      0.97      0.97       150\n",
            "weighted avg       0.97      0.97      0.97       150\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(metrics.confusion_matrix(expected , predicted))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i4oBmiTUzIZg",
        "outputId": "0eada127-a571-4daf-f210-73075ce5e337"
      },
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[50  0  0]\n",
            " [ 0 47  3]\n",
            " [ 0  1 49]]\n"
          ]
        }
      ]
    }
  ]
}