Task_1_(Beginner_level)_Iris_Flowers_Classification_ML_Project.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [],
      "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/MohitPatil602/DATA-SCIENCE-TASKS/blob/main/Task_1_(Beginner_level)_Iris_Flowers_Classification_ML_Project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Lets Grow More***\n",
        "\n",
        "**Name: MOHIT HEMANTRAO PATIL**\n",
        "\n",
        "\n",
        "**Data Science Intern**\n",
        "\n",
        "**Task-1: Iris Flowers Classification ML Project**\n",
        "\n",
        "**Description Of Task:**\n",
        "\n",
        "This particular ML project is usually referred to as the “Hello World” of Machine Learning. The iris flowers dataset contains numeric attributes, and it is perfect for beginners to learn about supervised ML algorithms, mainly how to load and handle data. Also, since this is a small dataset, it can easily fit in memory without requiring special transformations or scaling capabilities.\n",
        "\n",
        "**Dataset: http://archive.ics.uci.edu/ml/datasets/Iris**"
      ],
      "metadata": {
        "id": "KXq8VFADa14W"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "nE0QKRsyexog"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Importing libraries**"
      ],
      "metadata": {
        "id": "lYD9DeRXdfGf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "!pip install scikit-plot #Manually downloading scikitplot library\n",
        "import scikitplot as skplt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aaqsddXTad3f",
        "outputId": "0bcaeb66-8d65-44fe-c964-d953f09437c6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting scikit-plot\n",
            "  Downloading scikit_plot-0.3.7-py3-none-any.whl (33 kB)\n",
            "Requirement already satisfied: scipy>=0.9 in /usr/local/lib/python3.7/dist-packages (from scikit-plot) (1.7.3)\n",
            "Requirement already satisfied: matplotlib>=1.4.0 in /usr/local/lib/python3.7/dist-packages (from scikit-plot) (3.2.2)\n",
            "Requirement already satisfied: scikit-learn>=0.18 in /usr/local/lib/python3.7/dist-packages (from scikit-plot) (1.0.2)\n",
            "Requirement already satisfied: joblib>=0.10 in /usr/local/lib/python3.7/dist-packages (from scikit-plot) (1.1.0)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=1.4.0->scikit-plot) (2.8.2)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=1.4.0->scikit-plot) (3.0.9)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=1.4.0->scikit-plot) (1.4.4)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=1.4.0->scikit-plot) (0.11.0)\n",
            "Requirement already satisfied: numpy>=1.11 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=1.4.0->scikit-plot) (1.21.6)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib>=1.4.0->scikit-plot) (4.1.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib>=1.4.0->scikit-plot) (1.15.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.18->scikit-plot) (3.1.0)\n",
            "Installing collected packages: scikit-plot\n",
            "Successfully installed scikit-plot-0.3.7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Importing dataset**"
      ],
      "metadata": {
        "id": "cAlAijxPdmu0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df= pd.read_csv('/Iris (2).csv')"
      ],
      "metadata": {
        "id": "sOrdYT-DVm9u"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head() #top 5 values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "roMpIzrsV241",
        "outputId": "b81f77b7-7f14-4bdb-9d0f-c43d16cb249f"
      },
      "execution_count": null,
      "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-81180b5a-e45b-4f1e-b485-95fcfe45b7bb\">\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-81180b5a-e45b-4f1e-b485-95fcfe45b7bb')\"\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-81180b5a-e45b-4f1e-b485-95fcfe45b7bb 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-81180b5a-e45b-4f1e-b485-95fcfe45b7bb');\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": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.tail() # last 5 values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "9Wg0WmTpV8f8",
        "outputId": "729359d8-5bd1-4730-bcc8-570d5488673d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\n",
              "145  146            6.7           3.0            5.2           2.3   \n",
              "146  147            6.3           2.5            5.0           1.9   \n",
              "147  148            6.5           3.0            5.2           2.0   \n",
              "148  149            6.2           3.4            5.4           2.3   \n",
              "149  150            5.9           3.0            5.1           1.8   \n",
              "\n",
              "            Species  \n",
              "145  Iris-virginica  \n",
              "146  Iris-virginica  \n",
              "147  Iris-virginica  \n",
              "148  Iris-virginica  \n",
              "149  Iris-virginica  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-140c33ca-784b-47ec-9c4a-486fafb1f5ef\">\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>145</th>\n",
              "      <td>146</td>\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>147</td>\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>148</td>\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>149</td>\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>150</td>\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",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-140c33ca-784b-47ec-9c4a-486fafb1f5ef')\"\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-140c33ca-784b-47ec-9c4a-486fafb1f5ef 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-140c33ca-784b-47ec-9c4a-486fafb1f5ef');\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": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape # no. of rows and colums"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eZHLweC0WCpE",
        "outputId": "7060662c-2f9f-4233-f248-93e4d6035d78"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(150, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull() # returns a DataFrame object where all the values are replaced with a Boolean,True for NULL values,otherwise False."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "HEC1GbePWGpa",
        "outputId": "7ffd849f-daf9-4adb-d20b-ece9a2ca212c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  Species\n",
              "0    False          False         False          False         False    False\n",
              "1    False          False         False          False         False    False\n",
              "2    False          False         False          False         False    False\n",
              "3    False          False         False          False         False    False\n",
              "4    False          False         False          False         False    False\n",
              "..     ...            ...           ...            ...           ...      ...\n",
              "145  False          False         False          False         False    False\n",
              "146  False          False         False          False         False    False\n",
              "147  False          False         False          False         False    False\n",
              "148  False          False         False          False         False    False\n",
              "149  False          False         False          False         False    False\n",
              "\n",
              "[150 rows x 6 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-48950d1c-9afe-49f8-b1a2-f905db75750f\">\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>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\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>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 6 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-48950d1c-9afe-49f8-b1a2-f905db75750f')\"\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-48950d1c-9afe-49f8-b1a2-f905db75750f 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-48950d1c-9afe-49f8-b1a2-f905db75750f');\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": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum() #returns the number of missing values in the data set."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kqzfkVabWO6n",
        "outputId": "061aeca0-de98-46b4-840f-059267b8d9b3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Id               0\n",
              "SepalLengthCm    0\n",
              "SepalWidthCm     0\n",
              "PetalLengthCm    0\n",
              "PetalWidthCm     0\n",
              "Species          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe() # used to view some basic statistical details like percentile, mean, std"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "wC8Uo1fxWVDM",
        "outputId": "bcd5cce5-76d8-4b57-c3b3-ccfd9dad9800"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
              "count  150.000000     150.000000    150.000000     150.000000    150.000000\n",
              "mean    75.500000       5.843333      3.054000       3.758667      1.198667\n",
              "std     43.445368       0.828066      0.433594       1.764420      0.763161\n",
              "min      1.000000       4.300000      2.000000       1.000000      0.100000\n",
              "25%     38.250000       5.100000      2.800000       1.600000      0.300000\n",
              "50%     75.500000       5.800000      3.000000       4.350000      1.300000\n",
              "75%    112.750000       6.400000      3.300000       5.100000      1.800000\n",
              "max    150.000000       7.900000      4.400000       6.900000      2.500000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7a85a97a-60a8-49d0-a368-499536ac7f9d\">\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",
              "    </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",
              "      <td>150.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>75.500000</td>\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>43.445368</td>\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>1.000000</td>\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>38.250000</td>\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>75.500000</td>\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>112.750000</td>\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>150.000000</td>\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-7a85a97a-60a8-49d0-a368-499536ac7f9d')\"\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-7a85a97a-60a8-49d0-a368-499536ac7f9d 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-7a85a97a-60a8-49d0-a368-499536ac7f9d');\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": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LD2DdVPIWW99",
        "outputId": "d9acff91-69eb-4dae-a463-713861f9f2fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm',\n",
              "       'Species'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.nunique() # return number of unique elements in the object"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "10wxaK8XWj0o",
        "outputId": "548b95fc-1cff-4e1a-da24-31dca9924e28"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Id               150\n",
              "SepalLengthCm     35\n",
              "SepalWidthCm      23\n",
              "PetalLengthCm     43\n",
              "PetalWidthCm      22\n",
              "Species            3\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.Species.nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YYoyJliTWmsA",
        "outputId": "f9d7370c-6099-496f-cb7a-1a460f4ad7f7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.Species.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "098HsCuxWm0e",
        "outputId": "9311892f-f160-4611-fc70-be62ceecd4d2"
      },
      "execution_count": null,
      "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": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.max()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2HHC05_sWuLA",
        "outputId": "d1898e87-f7cc-4205-ddd1-1463d29b3455"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Id                          150\n",
              "SepalLengthCm               7.9\n",
              "SepalWidthCm                4.4\n",
              "PetalLengthCm               6.9\n",
              "PetalWidthCm                2.5\n",
              "Species          Iris-virginica\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.min()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RSnJk4GsWySx",
        "outputId": "07cf8158-9524-4566-ed6d-e8b1a3563c1e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Id                         1\n",
              "SepalLengthCm            4.3\n",
              "SepalWidthCm             2.0\n",
              "PetalLengthCm            1.0\n",
              "PetalWidthCm             0.1\n",
              "Species          Iris-setosa\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#drop the value of id form dataset\n",
        "df.drop('Id',axis=1,inplace=True)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "wm2fYYQ2W5Bf",
        "outputId": "7284c37d-3b36-4316-eb59-f2942f505900"
      },
      "execution_count": null,
      "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-cb0b83fd-72ff-4b4b-9593-065f8c6645d8\">\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-cb0b83fd-72ff-4b4b-9593-065f8c6645d8')\"\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-cb0b83fd-72ff-4b4b-9593-065f8c6645d8 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-cb0b83fd-72ff-4b4b-9593-065f8c6645d8');\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": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Visualization**"
      ],
      "metadata": {
        "id": "vw5AFxAKduzt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#The boxplot plot is reated with the boxplot() method. The example below loads the iris flower data set.(a box-and-whisker plot)\n",
        "#Then the presented boxplot shows the minimum, maximum,median, 1st quartile and 3rd quartile\n",
        "sns.boxplot(x=\"Species\", y='PetalLengthCm', data=df ) \n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "mDxySQb4W-7c",
        "outputId": "84913565-5163-4bc6-e4cc-dfca3947a771"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAEGCAYAAABvtY4XAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAY7klEQVR4nO3df3yddX338de7SbGhBREanRIxYAqKEyoGlZuBIBYJczzcQyYiaGT31t27NwLD/XJ2TnnUbeiYEtTNyoTsHnI7VG4RVx7t1K5sbEBKS2krthkWDFNJUaAtbU3bz/3HdWWk4STnSnKuXOdceT8fjzxynevnJ+fqefd7vuc630sRgZmZlc+cogswM7N8OODNzErKAW9mVlIOeDOzknLAm5mVVHPRBYy2cOHCaG9vL7oMM7OGsW7duh0R0VppWV0FfHt7O/39/UWXYWbWMCQ9Nt4yd9GYmZWUA97MrKRyC3hJJ0naMOrnWUlX53U8MzM7VG598BHxfWAxgKQm4AngjryOZ2Zmh5qpLprzgP+MiHE/DDAzs9qaqYB/L3BbpQWSlkrql9Q/NDQ0Q+WYmZVf7gEv6TDgIuD2SssjYkVEdEZEZ2trxUs5zcxsCmbiOvgu4MGI+MkMHMvMGlBvby8DAwO57HtwcBCAtra2mu+7o6ODnp6emu+3VmYi4C9lnO4ZM7O87dmzp+gSCpNrwEuaDywBfivP45hZY8uzFTyy797e3tyOUa9yDfiI2A0ck+cxzMysMn+T1cyspBzwZmYl5YA3MyspB7yZWUk54M3MSsoBb2ZWUg54M7OScsCbmZWUA97MrKQc8GZmJeWANzMrKQe8mVlJOeDNzErKAW9mVlIOeDOzknLAm5mVlAPezKykHPBmZiXlgDczKykHvJlZSTngzcxKygFvZlZSDngzs5JywJuZlVSuAS/pKElflfSIpO9JOiPP45mZ2fOac97/DcDdEXGxpMOAw3M+npmZpXILeEkvBs4GPggQET8Hfp7X8czM7FB5dtEcDwwBN0taL+kmSfPHriRpqaR+Sf1DQ0M5lmNmNrvkGfDNwGnA30TEG4DdwB+PXSkiVkREZ0R0tra25liOmdnskmfADwKDEXFf+virJIFvZmYzILeAj4gfAz+UdFI66zxgS17HMzOzQ+V9Fc2VwK3pFTSPAlfkfDwzy1Fvby8DAwNFlzEp27ZtA6Cnp6fgSrLr6OioSb25BnxEbAA68zyGmc2cgYEB1m9eD0cVXckkHEx+rX9ifbF1ZPV07XaVdwvezMrmKDh4zsGiqyitOWtq13PuoQrMzErKAW9mVlIOeDOzknLAm5mVlAPezKykHPBmZiXlgDczKylfB29mmQ0ODsIztb1W28Z4GgZjsCa78lkyMyspt+DNLLO2tjaGNORvsuZozpo5tB3bVpt91WQvZmZWdxzwZmYl5YA3MyspB7yZWUk54M3MSsoBb2ZWUg54M7OScsCbmZWUA97MrKQc8GZmJeWANzMrKQe8mVlJOeDNzErKo0laafT29jIwMFDz/Q4OJmNzt7XVZoS/0To6Oujp6an5fnP1dIONB78r/b2g0Cqyexo4tja7yjXgJW0HdgIHgP0R0Znn8czysGfPnqJLqBsdHR1FlzBp27ZtA2DRsYsKriSjY2v3PCsiarKjijtPAr4zInZkWb+zszP6+/tzq8dsKkZa2L29vQVXYlNR9vMnad14jecGep9lZmaTkXfAB7BK0jpJSyutIGmppH5J/UNDQzmXY2Y2e2Tqg5fUCXwEeFW6jYCIiFOqbPpLEfGEpJcCqyU9EhFrR68QESuAFZB00Uz2DzAzs8qyfsh6K/AHwMNA5psxRsQT6e8nJd0BvAlYO/FWZmZWC1kDfigi7pzMjiXNB+ZExM50+nzg2skWaGZmU5M14P9M0k3At4F9IzMj4usTbPMy4A5JI8f5ckTcPdVCzcxscrIG/BXAa4C5PN9FE8C4AR8RjwKnTqs6MzObsqwBf3pEnJRrJWZmVlNZL5O8V9LJuVZiZmY1lbUF/xZgg6QfkPTBZ71M0szMCpI14C/ItQozM6u5CQNe0unAwohYOWZ+F/Ak8FiOtZmZ2TRU64O/DthSYf4W4FO1L8fMzGqlWhfNERHxglZ6RDwmaWFONVmJ5TVme55GhptttHHbG3KseaupagH/kgmWHV7LQmx2GBgYYOumBzluwYGiS8nssOHkje7e7Q8UXEl2j+9qKroEqwPVAv6fJX0CWBbpwPFKvpr6ceA7eRdn5XTcggMs69xVfUWbsuX9jXL7IstTtYD/EHATMCBpQzrvVKAf+I08CzMzs+mZMOAjYjdwqaQTgNelszenwxCYmdVEnp/N5PkZSr1/zpHpOviIeFTSPpLx4NsktaXzPfSvmdW1lpaWoksoTNYbflwHXAJs5tDBxhzwZjZt9dwKbmRZv8n6LuCkiNhXdU0zM6sLWQcbe5RkqGAzM2sQ1YYquJGkK+Y5ksHGxt7ww++rbFIGBwfZvbPJl/Hl7LGdTcwfHCy6DCtYtS6a/vT3OmDsLft8g2wzszpW7TLJPgBJV0XEDaOXSboqz8KsnNra2ti7/0f+olPOlvcvYF5bW9FlWMGy9sF3V5j3wRrWYWZmNVatD/5S4H3A8ZJGd9EcAfw0z8LMzGx6qvXB3wv8CFgIXD9q/k5gY15FmZnZ9FXrg3+M5KYeZ8xMOWZmVitZv8m6kxdeNfMMyVU2H/LYNGZm9SfrN1k/AwwCXya54fZ7gVcDDwJfAs7JozgzM5u6rFfRXBQRX4iInRHxbESsAN4REV9h4puCIKlJ0npJd027WjMzyyxrwD8n6T2S5qQ/7wH2psuqfeHpKuB7U67QzMymJGsXzWXADcDnSQL9P4DLJbUAvzveRumwwr8MfAK4ZnqlWlk8vquxhir4yXNJO+hlhx+ssmb9eHxXEycWXYQVLvN48MCvjLP4XyfY9DPAH5JcN1+RpKXAUoDjjjsuSznWwDo6OoouYdJ+nt4wYl77ooIrye5EGvO5ttrKehVNK/CbQPvobSLi1yfY5p3AkxGxTtI5462X9uevAOjs7PT4NiXXiON+j9Tc29tbcCVmk5O1i+YbwD3APwMHMm5zJnCRpAuBecCRkv4hIi6ffJlmZjZZWQP+8Ij4o8nsOCI+DHwYIG3B/77D3cxs5mS9iuautCVuZmYNImvAX0US8nslPStpp6Rnsx4kItZExDunVqKZmU1F1qtoxr0KxszM6lOmFrwSl0v60/TxKyW9Kd/SzMxsOrJ20XyeZETJ96WPdwGfy6UiMzOriaxX0bw5Ik6TtB4gIn4m6bAc6zIzs2nK2oIfltREOu5M+sWnxvnetpnZLJQ14HuBO4CXSvoEyfAEf55bVWZmNm1Zr6K5VdI64DyS8eDfRXLDDzMzq1NZ++CJiEeAR0YeS3oc8OhgZmZ1KmsXTSWqWRVmZlZz0wl4j/xoZlbHJuyikXQjlYNcwFG5VGRmZjVRrQ++f4rLzMysYBMGfET0zVQhZmZWW9W6aL7JBH3tEXFRzSsyM7OaqNZF81czUoWZmdVctS6af5mpQszMrLay3nR7EfAXwMkk91cFICJOyKkuMzObpqzfZL0Z+DPg08C5wBVM7xp6s5rr7e1lYGCg5vvdtm0bAD09PTXfd0dHRy77NYPsId0SEd8GFBGPRcTHgF/Oryyz+tHS0kJLS0vRZZhNWtYW/D5Jc4Btkn4XeAJYkF9ZZpPnlrDZoSZz0+3DgR7gjcDlwAfyKsrMzKYva8C3R8SuiBiMiCsi4t14JEkzs7qWNeA/nHGemZnViWrfZO0CLgSOldQ7atGRwP48CzMzs+mp1oL/L5JBxfYC60b93Am8I9/SzOrDjh07uPLKK3nqqaeKLsVsUiYM+Ih4KB1wrAP4R+A/IqIvIr4eET+baFtJ8yTdL+khSZslfbyGdZvNmL6+PjZu3Ehfn8fes8aStQ/+AmADcDeApMWS7qyyzT7gbRFxKrAYuEDSW6ZcqVkBduzYwcqVK4kIVq5c6Va8NZSsAf8x4E3A0wARsQE4fqINIrErfTg3/fFdoKyh9PX1EZH8sz148KBb8dZQsgb8cEQ8M2Ze1bCW1CRpA/AksDoi7quwzlJJ/ZL6h4aGMpZjNjNWr17N8PAwAMPDw6xatargisyyyxrwmyW9D2iStCi9ld+91TaKiAMRsRhoA94k6RcrrLMiIjojorO1tXVSxZvlbcmSJcydOxeAuXPncv755xdckVl2WQP+SuB1JP3qXwaeAa7OepCIeBr4LklfvlnD6O7uRhIAc+bMobu7u+CKzLKbMODTK2GuBj4JPA6cERGnR8SyiNhbZdtWSUel0y3AEuCRGtVtNiMWLlxIV1cXkujq6uKYY44puiSzzKoNNtYHDAP3AF3Aa8necn850CepieQ/kn+MiLumWqhZUbq7u9m+fbtb79ZwNHKFQMWF0sMR8fp0uhm4PyJOy6uYzs7O6O/vz2v3ZmalI2ldRHRWWlatD354ZCIiPDSBmVkDqdZFc6qkZ9NpAS3pY5Fc6n5krtWZmdmUVbvpdtNMFWJmZrXl+6qamZWUA97MrKQc8GZmJeWANzMrKQe8mVlJOeDNzErKAW9mVlIOeDOzknLAm5mVlAPezKykHPBmZiXlgDczKykHvJlZSTngzcxKygFvZlZSDngzs5JywJuZlZQD3syspBzwZmYl5YA3MyspB7yZWUnlFvCSXinpu5K2SNos6aq8jmVmZi/UnOO+9wMfiogHJR0BrJO0OiK25HhMMzNL5daCj4gfRcSD6fRO4HvAsXkdz8zMDjUjffCS2oE3APdVWLZUUr+k/qGhoZkoZ8Zt3bqVrq4uBgYGii7FzGaR3ANe0gLga8DVEfHs2OURsSIiOiOis7W1Ne9yCrF8+XJ2797NtddeW3QpZjaL5BrwkuaShPutEfH1PI9Vr7Zu3cr27dsB2L59u1vxZjZj8ryKRsDfAd+LiL/O6zj1bvny5Yc8divezGZKni34M4H3A2+TtCH9uTDH49Wlkdb7eI/NzPKS22WSEfGvgPLaf6Nob28/JNTb29sLq8XMZhd/kzVny5YtO+TxRz/60YIqMbPZxgGfsxNPPPG/W+3t7e10dHQUW5CZzRoO+BmwbNky5s+f79a7mc2oPIcqaDi9vb25XMY4ODhIS0sLvb29Nd83QEdHBz09Pbns28walwN+BuzZs6foEsxsFnLAj5JXK3hkv3m14M3MKnEfvJlZSTngzcxKqiG7aPL6MDQv27ZtA/LrAsqLP7w1a2wNGfADAwOsf3gLBw8/uuhSMtHPA4B1//njgivJbs5zPy26BDObpoYM+MHBQSCKLiOzmHdk0SVMQaTPs5k1KvfBm5mVVEO24Nva2vjJvmb2nvzOoksprXlb7qKt7ReKLsPMpsEteDOzkmrIFjwkHwLO23JX0WVkor3JnQobqS8++ZDVLXizRtaQAd9oIzJu27YTgEWvbqTA/IWGe57N7FANGfCNdm22hyowsyK4D97MrKQc8GZmJeWANzMrqYbsg89LXmPc5D0WjceMMbNK3IKfAS960YvYt28fw8PDRZdiZrOIW/Cj5NUKvv7667nzzjtZtGgR11xzTS7HMDMbyy34nO3YsYOVK1cSEaxcuZKnnnqq6JLMbJZwwOesr6+PgwcPAnDgwAH6+voKrsjMZovcAl7SlyQ9KWlTXsdoBKtXr2b//v0A7N+/n1WrVhVckZnNFnm24G8BLshx/w3hrLPOOuTx2WefXVAlZjbb5BbwEbEW8G2BzMwKUngfvKSlkvol9Q8NDRVdTs3dc889hzxeu3ZtQZWY2WxTeMBHxIqI6IyIztbW1qLLqbklS5bQ3Jxcjdrc3Mz5559fcEVmNlsUHvBl193dzZw5ydPc1NREd3d3wRWZ2WzhgM/ZwoUL6erqQhJdXV0cc8wxRZdkZrNEnpdJ3gb8O3CSpEFJ/zOvY9W77u5uTjnlFLfezWxGKSKKruG/dXZ2Rn9/f9FlmJk1DEnrIqKz0jJ30ZiZlZQD3syspBzwZmYl5YA3MyupuvqQVdIQ8FjRdeRkIbCj6CJsynz+GluZz9+rIqLit0TrKuDLTFL/eJ90W/3z+Wtss/X8uYvGzKykHPBmZiXlgJ85K4ouwKbF56+xzcrz5z54M7OScgvezKykHPBmZiXlgE9J2jXBsntzPO6f5LXvsijq3GQh6RWSvjrFbddImlWX7uV9LiVdK+ntk9zmIkl/XGWdKZ/nIrkPPiVpV0QsGDOvOSL2z/Rx7VBFnZu8jydpDfD7EZFpCFVJTRFxoJY1zLQCX2cN/9xNhVvwY0g6R9I9ku4EtqTzdqW/Xy5praQNkjZJOqvC9q+TdH+6zkZJi9L5l4+a/wVJTZL+EmhJ592arndNuu9Nkq5O582X9C1JD6XzL0nnf1TSA+m8FZI0M89SMaZzbiS9WNJjkuakj+dL+qGkuZJeLeluSevS/b8mXecWSX8r6T7gk5Lemu5/g6T1ko6Q1C5pU7p+k6S/So+/UdKV6fzz0vUflvQlSS+q8Lddmi7fJOm6UfN3Sbpe0kPAGbk8sQXI8VzeIunidP52SddJehD4NUkXSnokPc+9ku5K1/ugpM+m07eky+6V9OiofWU5z/X3eowI/yTvYnalv88BdgPHV1j2IeAj6XQTcESF/dwIXJZOHwa0AK8FvgnMTed/HvjA6H2n028EHgbmAwuAzcAbgHcDXxy13ovT30ePmvd/gF8p+nms83PzDeDcdPoS4KZ0+tvAonT6zcB30ulbgLuApvTxN4Ez0+kFQDPQDmxK5/028FWgeeT8APOAHwInpvP+Hrg6nV4DdAKvAB4HWtN9fgd4V7pOAO8p+hw00Lm8Bbg4nd4O/GE6PXIejk8f3wbclU5/EPjsqO1vJ2n8ngwMpPMnPM+jf6fTdfF6dAu+svsj4gcV5j8AXCHpY8DrI2JnhXX+HfgTSX9EMkbEHuA8kvB+QNKG9PEJFbb9JeCOiNgdEbuArwNnkYT+krQ1clZEPJOuf66k+yQ9DLwNeN2U/+LGMZ1z8xWSMAB4L/AVSQuA/wHcnp6bLwAvH7XN7fH8W/t/A/5aUg9wVLywW+HtwBdG5kfET4GTgB9ExNZ0nT7g7DHbnQ6siYihdNtbR61zAPhapSeiBGp6Lsc5xsj81wCPjjrebRPU9f8i4mBEbAFeVmF5pfMMdfh6dMBXtrvSzIhYS/LCewK4RdIHJP3qqLftnRHxZeAiYA/wT5LeBgjoi4jF6c9JEfGxrMWk4XAaSdAvT98KziN5J3BxRLwe+CJJK6XspnxugDuBCyQdTfIf7ndIXgNPjzo3iyPitZWOFxF/CfwGybuyfxvpysnZ3ihv33Gtz2XmY1Sxb9R0pm6Wen09OuAnQdKrgJ9ExBeBm4DTIuKOUcHQL+kEkpZCL8nbyFNIugAulvTSdD9Hp/sCGJY0N52+B3iXpMMlzQd+FbhH0iuA5yLiH4BPkYT9yD+eHWkr9OLcn4A6luXcpO+KHgBuIHl7fiAingV+IOnX0v1I0qnjHOPVEfFwRFyX7mdswK8GfktSc7r+0cD3gXZJHek67wf+Zcx29wNvlbRQUhNwaYV1Zo2pnssqu/0+cIKk9vTxJeOvWlWl81yXr8fmogtoMOcAfyBpGNgFfKDCOu8B3p+u82PgzyPip5KWAavSD4aGgd8hGRp5BbBR0oMRcZmkW0he8JD0K66X9A7gU5IOptv+dkQ8LemLwKb0OA/k9Dc3inOofm4gect+e7r+iMuAv0nP0Vzg/wIPVdj2aknnAgdJPh9ZyaHdOTcBJ5Kcz2GSz00+K+kKki6gZpLz9LejdxoRP1Jymd53SVqM34qIb2T9w0voHKZ+LiuKiD2S/jdwt6TdTO/1Mt55rrvXoy+TNLNZQdKCiNiVXt3yOWBbRHy66Lry5C4aM5stfjP9IH0z8GKSD9RLzS14M7OScgvezKykHPBmZiXlgDczKykHvJWapI9I2pyOGbJB0ptruO9/knRUrfZnVmu+Dt5KS9IZwDtJviizT9JCkvGBaiIiLqzVvszy4Ba8ldnLgR0RsQ8gInZExH8pGWXwk0pGb7x/5FumklolfS0dEfABSWem8xdIujldf6Okd6fzt6f/aYw3WmiTktEJN6Xb/l5Bz4PNUg54K7NVwCslbZX0eUlvHbXsmXTMkM8Cn0nn3QB8OiJOJxnB86Z0/p+OrB8RpzBm3BNJryX56vuZEbGYZICwy4DFwLER8YvpsW7O5880q8xdNFZa6bcW30gyIue5JKNHjty557ZRv0e+zfh24GQ9P4z3kem4Im8nGbFwZL8/G3Oo0aOFQjIY2ZMkwwufIOlG4Fsk/+GYzRgHvJVaOgjVGmBNOoxr98ii0aulv+cAb4mIvaP3oer3bRgZLfTDL1iQDFz2DuB/kYxT9OuT/BPMpsxdNFZakk5Seket1GKSAd7g+dEELyEZwx+SFvaVo7ZfnE6uJhkcbmT+S8YcquJooWn//JyI+BqwjGQUULMZ4xa8ldkC4Mb0Usb9wACwlOTKmpdI2kgy9vel6fo9wOfS+c3AWpKW9/J0/iaS/vWPk9yMBYCI2DLOaKF7gJvTeQAvaOGb5clj0disI2k70BkRO4quxSxP7qIxMyspt+DNzErKLXgzs5JywJuZlZQD3syspBzwZmYl5YA3Myup/w9WA7KZ+ct8FgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(x = \"Species\", y = \"SepalWidthCm\", data = df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "shEYDqaIXKdK",
        "outputId": "fe606f0f-602f-41bb-bc86-4d33b805c2d6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f98e52fa190>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEJCAYAAACZjSCSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAahElEQVR4nO3de5hcVZnv8e+vQzSBcBESFWhiC01UdBgurYgoch+DDM4ccMBHIXiZeJwjAWXGOSgHlSdzHtEZcBp0IMKQeDmI3EaIZg48QhRQLgmXEIImfTBIMygJSCCQMAl5zx979VDpVKeqO7Wrunr9Ps9TT+9ae+213+7d3W+tfVlLEYGZmeWro9UBmJlZazkRmJllzonAzCxzTgRmZplzIjAzy5wTgZlZ5kpPBJLGSXpA0vwq686QtErSg+n1qbLjMTOzzW3XhH2cBTwK7DTE+msi4rNNiMPMzKooNRFI6gQ+CPwD8PlGtDl58uTo6upqRFNmZtlYvHjx6oiYUm1d2T2CbwJfAHbcSp2TJB0OLAc+FxFPbK3Brq4uFi1a1MAQzczGPkmPD7WutGsEkk4Ano6IxVupdjPQFRH7A7cC84Zoa6akRZIWrVq1qoRozczyVebF4sOAEyWtBH4IHCXp+5UVIuKZiHg5vb0COLhaQxExJyJ6IqJnypSqPRszMxuh0hJBRJwbEZ0R0QWcCtwWER+rrCNp94q3J1JcVDYzsyZqxl1Dm5F0AbAoIm4CZkk6EdgIPAuc0ex4zMxyp3Ybhrqnpyd8sdjMbHgkLY6Inmrr/GSxmVnmnAjMzDLX9GsEY0Fvby99fX0Nb7e/vx+Azs7Ohrfd3d3NrFmzGt6umbU/J4JRZN26da0Owcwy5EQwAmV9sh5ot7e3t5T2zcyq8TUCM7PMORGYmWXOicDMLHNOBGZmmXMiMDPLnBOBmVnmnAjMzDLnRGBmljknAjOzzDkRmJllzonAzCxzTgRmZplzIjAzy5wTgZlZ5pwIzMwy50RgZpY5JwIzs8w5EZiZZc6JwMwsc04EZmaZcyIwM8ucE4GZWeacCMzMMudEYGaWOScCM7PMORGYmWXOicDMLHOlJwJJ4yQ9IGl+lXWvlXSNpD5J90jqKjseMzPbXDN6BGcBjw6x7pPAHyOiG7gYuLAJ8ZiZWYVSE4GkTuCDwBVDVPkQMC8tXwccLUllxmRmZpsru0fwTeALwKYh1u8JPAEQERuBNcBuJcdkZmYVSksEkk4Ano6IxQ1oa6akRZIWrVq1qgHRmZnZgDJ7BIcBJ0paCfwQOErS9wfVeRLYC0DSdsDOwDODG4qIORHRExE9U6ZMKTFkM7P8lJYIIuLciOiMiC7gVOC2iPjYoGo3ATPS8smpTpQVk5mZbWm7Zu9Q0gXAooi4CbgS+J6kPuBZioRhZmZN1JREEBELgYVp+fyK8vXAh5sRg5mZVecni83MMudEYGaWOScCM7PMORGYmWXOicDMLHNOBGZmmXMiMDPLnBOBmVnmnAjMLHvLly9n+vTp9PX1tTqUlnAiMLPszZ49mxdffJELLrig1aG0hBOBmWVt+fLlrFy5EoCVK1dm2StwIjCzrM2ePXuz9zn2CpwIzCxrA72Bod7nwInAzLLW1dW11fc5cCIws6ydd955m70///zzh6g5djkRmFnWpk2b9l+9gK6uLrq7u1sbUAs4EZhZ9s477zx22GGHLHsD0IKpKs3MRptp06axYMGCVofRMu4RmJllzonAzCxzTgRmZpkbs9cIent72+5R8RUrVgAwa9asFkcyPN3d3W0Xs5m9aswmgr6+Ph54eBmbtt+11aHUTf8ZACz+f79vcST163jp2VaHYGbbaMwmAoBN2+/K+v1OaHUYY9qEZfNbHYKZbSNfIzAzy5wTgZlZ5pwIzMwyV9c1AknjgA8CXZXbRMRF5YRlZralsu4G7O/vB6Czs7PhbcPov7Ou3ovFNwPrgYeBTeWFY2bWfOvWrWt1CC1VbyLojIj9S43EzKyGsj5VD7Tb29tbSvujXb3XCBZIOq7USMzMrCXq7RHcDdwoqQPYAAiIiNiptMjMzKwp6k0EFwGHAg9HRJQYj5mZNVm9p4aeAJYOJwlImiDpXkkPSXpE0ler1DlD0ipJD6bXp+pt38zMGqPeHsFjwEJJC4CXBwpr3D76MnBURKyVNB64U9KCiLh7UL1rIuKzw4razMwapt5E8Nv0ek161ZR6D2vT2/Hp5dNKZmajzFYTgaQJwI4R8dVB5a8Hnq/VeHoQbTHQDXwrIu6pUu0kSYcDy4HPRcQTVdqZCcwEmDp1aq3dmpnZMNS6RtALvK9K+WHAxbUaj4hXIuIAoBN4l6R3DKpyM9CVnlG4FZg3RDtzIqInInqmTJlSa7dmZjYMtRLBwRFxw+DCiLgROLzenUTEc8DtwAcGlT8TEQPXHK4ADq63TTMza4xaiWD7kW4raYqkXdLyROBY4NeD6uxe8fZE4NEa8ZiZWYPVulj8tKR3RcS9lYWS3gmsqrHt7sC8dJ2gA/hRRMyXdAGwKCJuAmZJOhHYCDwLnDGSb8LMzEauViL4O+BHkuZSXPQF6AFOB07d2oYRsQQ4sEr5+RXL5wLnDiNeMzNrsK2e3kk9gXdRDClxRnoJOGSIO4DMzKzN1HyOICKeBr7chFjMzKwF6p2Y5jCKZNCVthkYdG7v8kLbNv39/XS8tMaTq5es46Vn6O/f2Oow6lbWxCZQ7uQmo31iE2tv9T5ZfCXwOYrrBK+UF45Z+8p9chNrX/UmgjURsaDUSBqss7OTP7y8Hev3O6HVoYxpE5bNp7Pzja0Oo25lfqrOfXITa1+1hpg4KC3eLukbwA1sPujc/SXGZmZmTVCrR/BPg973VCwHcFRjwzEzs2bbaiKIiCMBJO0dEY9VrpM0ai8Um5lZ/eqdmOa6KmXXNjIQMzNrjVrXCN4KvB3YWdJ/q1i1EzChzMDMzKw5al0jeAtwArAL8OcV5S8Af11WUGZm1jy1rhH8GPixpEMj4ldNisnMzJqo1qmhS0jTS0r6yOD1EeFHHc3M2lyti8WLKJ4mngAcBKxIrwOoc+5iMzMb3WqdGpoHIOkzwHsjYmN6fxlwR/nhmZlZ2eq9ffR1FHcKDZiUyszMrM3VO9bQ14AHJN1OMfLo4cBXygrKzMyap65EEBFXSVoAHJKK/j4ifl9eWGZm1iy1JqB/a/p6ELAH8ER67VExIJ2ZmbWxWj2CcygeHBs8+Bx40DkzszGhViL4Arw6+JyZWT3KnAmuDCtWrADKna+iDI2aua5WIviNpNXAXcAvgbsiYvk279XMxrS+vj4eeOSBYnCadrCp+PLAkw+0No7heK5xTdV6juD1kqYB70mvcyRNAe6mSApfb1woZjam7AKbjtjU6ijGrI6F9d79X1vNu4ZSD2A5MFfSPsDxwFnAcYATgZlZm6s11tBAT+BQYC/gMYrewMeAUT9NZcdLzzJh2fxWh1E3rX8egJiwU42ao0fHS88C7TNnsZltqVaP4E6Kf/gXAzdGxEvlh9QY3d3drQ5h2FaseAGAffdpp3+sb2zLn7WZvapWItiDV68PfFrSdhSJ4VfArwZPXzmatNvVf3g15t7e3hZHYmY5qXWx+PfADemFpO2BTwBfBd4MjCs7QDMzK1etawQ7U1wfGOgVHEgxDPXNFLeUmplZm6t1aqiPdBoIuAC4LyLWlR6VmZk1Ta1TQ1OaFYiZmbVGrVNDN5OmqqwmIk5seERmZtZUtU4N/eNIG5Y0AfgF8Nq0n+si4suD6rwW+C5wMPAMcEpErBzpPs3MbPhqnRr6+Ta0/TJwVESslTQeuFPSgoi4u6LOJ4E/RkS3pFOBC4FTtmGfZmY2THUNViFpX0nXSVom6bGB19a2icLa9HZ8eg0+zfQhYF5avg44WpKGEb+ZmW2jeqeqvAr4MsUTxkcCH6eOJCJpHLAY6Aa+FRH3DKqyJ8VEN0TERklrgN2A1XXGZWNUuw1jDO05lHGjhjEerL+/H9Y0dmA0G+Q56I/+hjRVbyKYGBE/k6SIeBz4iqTFwPlb2ygiXgEOkLQLcKOkd0TE0uEGKWkmMBNg6tSpw93c2lBfXx/Ll97P1EmvtDqUur1mQ/FPb/3K+1ocSX1+t9bPg1qh3kTwsqQOYIWkzwJPApPq3UlEPJcmvv8AUJkInqQYzK4/DV+xM8VF48HbzwHmAPT09Ax5F5ONLVMnvcJ5PWtrV7QRmb2o7j/hYevs7GSVVnkY6hJ1LOygc8/OxrRVZ72zgO2BWRR3+JwGzNjaBpKmpJ4AkiYCxwK/HlTtpop2TgZuiwj/ozcza6K6egQRcR9A6hXMiogX6thsd2Beuk7QAfwoIuZLugBYFBE3AVcC35PUBzwLnDqSb8LMzEaurkQgqYfigvGO6f0a4BMRsXiobSJiCcXYRIPLz69YXg98eJgxm5lZA9V7jeBfgb+JiDsAJL2XIjHsX1ZgZmbWHPVeI3hlIAkARMSdwMZyQjIzs2aqt0fwc0mXA1dTPBR2CrBQ0kEAETHqp600M7Pq6k0Ef5q+fnlQ+YEUieGohkVkZmZNVe9dQ0eWHYiZmbVGvWMNvUHSlZIWpPf7SfpkuaGZmVkz1HuxeC7wfykmswdYDpxdRkBmZtZc9SaCyRHxI2ATFAPEAe0zCIyZmQ2p3kTwoqTdSMNIS3o3sKa0qMzMrGnqvWvo8xTjAu0j6S5gCsXYQGZm1uZqzVn8TuCJiLhf0vuBTwMnAbcAjRkI26yK/v5+XnxhXKkjZObu8RfGsUN/iX/Gz7XRfAQDg9y206/bcxQzujRArR7B5cAxafk9wJeAM4EDKIaFdq/AzLbQ3d3d6hCGZWBSoX333LfFkQzDno37OddKBOMi4tm0fAowJyKuB66X9GBDIjCrorOzk/Ubn/J8BCWavWgSEzobM579YO00Sxu8Gm9vb2+LI2mNWv22cWnCGICjgdsq1tV7fcHMzEaxWv/Mr6YYZ2g1sA4YGH20G981ZGY2Jmw1EUTEP0j6GcUkM7dUzB7WQXGtwMzM2lzN0zsRcXeVsuXlhGNmZs3WJvd2mZlZWZwIzMwy50RgZpY5JwIzs8w5EZiZZc6JwMwsc04EZmaZcyIwM8ucE4GZWeacCMzMMudEYGaWOScCM7PMORGYmWXOicDMLHNOBGZmmSstEUjaS9LtkpZJekTSWVXqHCFpjaQH0+v8suIxM7Pqypx3eCNwTkTcL2lHYLGkWyNi2aB6d0TECSXGYW3qd2vHMXvRpFaHUbc/vFR8rnrD9ptaHEl9frd2HNNaHYSNCqUlgoh4CngqLb8g6VFgT2BwIjDbQnd3d6tDGLb/XLECgAld+7Y4kvpMoz1/ztZ4ZfYI/oukLuBA4J4qqw+V9BDwH8DfRsQjzYjJRrdZs2a1OoRhG4i5t7e3xZGMXb29vfT19TW83RUpiZf1e9fd3T2qf6dLTwSSJgHXA2dHxPODVt8PvCki1ko6Hvg3YIuPU5JmAjMBpk6dWnLEZpabiRMntjqElio1EUgaT5EEfhARNwxeX5kYIuKnkr4taXJErB5Ubw4wB6CnpyfKjNnMRq/R/Km6nZV515CAK4FHI+KiIeq8MdVD0rtSPM+UFZOZmW2pzB7BYcBpwMOSHkxlXwSmAkTEZcDJwGckbQTWAadGhD/xm5k1UZl3Dd0JqEadS4FLy4rBzMxq85PFZmaZcyIwM8ucE4GZWeacCMzMMudEYGaWOScCM7PMORGYmWXOicDMLHNOBGZmmXMiMDPLXFPmIxhr2nFM9NE+HrqZtY4TwSiS+5joZtYaTgQj4E/WZjaW+BqBmVnmnAjMzDLnRGBm2Vu9ejVnnnkmzzyT5wSJTgRmlr158+axZMkS5s2b1+pQWsKJwMyytnr1ahYsWEBEsGDBgix7BU4EZpa1efPmMTBV+qZNm7LsFTgRmFnWbr31VjZs2ADAhg0buOWWW1ocUfM5EZhZ1o499ljGjx8PwPjx4znuuONaHFHzORGYWdZmzJiBJAA6OjqYMWNGiyNqPicCM8va5MmTmT59OpKYPn06u+22W6tDajoPMWFm2ZsxYwYrV67MsjcATgRmZkyePJlLLrmk1WG0jE8NmZllzonAzCxzTgRmZplzIjAzy5wTgZlZ5pwIzMwy50RgZpa50hKBpL0k3S5pmaRHJJ1VpY4k9Urqk7RE0kFlxWNmZtWV+UDZRuCciLhf0o7AYkm3RsSyijrTgX3T6xDgX9JXMzNrktJ6BBHxVETcn5ZfAB4F9hxU7UPAd6NwN7CLpN3LisnMzLbUlCEmJHUBBwL3DFq1J/BExfv+VPZUM+Ky/PT29tLX11dK2ytWrABg1qxZDW+7u7u7lHbNoAmJQNIk4Hrg7Ih4foRtzARmAkydOrWB0Zk1zsSJE1sdgtmIlJoIJI2nSAI/iIgbqlR5Etir4n1nKttMRMwB5gD09PRECaFaJvyp2mxLZd41JOBK4NGIuGiIajcBp6e7h94NrIkInxYyM2uiMnsEhwGnAQ9LejCVfRGYChARlwE/BY4H+oCXgI+XGI+ZmVVRWiKIiDsB1agTwP8oKwYzM6vNTxabmWXOicDMLHNOBGZmmXMiMDPLnBOBmVnmVNy40z4krQIeb3UcJZoMrG51EDZiPn7ta6wfuzdFxJRqK9ouEYx1khZFRE+r47CR8fFrXzkfO58aMjPLnBOBmVnmnAhGnzmtDsC2iY9f+8r22PkagZlZ5twjMDPLnBPBMEhau5V1vyxxv18sq+2xpFXHpx6S9pB03Qi3XSgpq7tZyj6Wki6QdMwwtzlR0v+sUWfEx7mVfGpoGCStjYhJg8q2i4iNzd6vbalVx6fs/UlaCPxtRCyqs/64iHilkTE0Wwv/1tr+ZzcS7hGMgKQjJN0h6SZgWSpbm77uLukXkh6UtFTS+6ps/3ZJ96Y6SyTtm8o/VlF+uaRxkr4GTExlP0j1Pp/aXirp7FS2g6SfSHoolZ+Sys+XdF8qm5MmDBrTtuX4SNpZ0uOSOtL7HSQ9IWm8pH0k/bukxan9t6Y6cyVdJuke4OuS3p/af1DSA5J2lNQlaWmqP07SP6b9L5F0Zio/OtV/WNK/Snptle/tI2n9UkkXVpSvlfRPkh4CDi3lB9sCJR7LuZJOTuUrJV0o6X7gw5KOl/TrdJx7Jc1P9c6QdGlanpvW/VLSYxVt1XOcR9/fZET4VecLWJu+HgG8CLy5yrpzgC+l5XHAjlXauQT4aFp+DTAReBtwMzA+lX8bOL2y7bR8MPAwsAMwCXgEOBA4CfhORb2d09ddK8q+B/x5q3+ObXB8fgwcmZZPAa5Iyz8D9k3LhwC3peW5wHxgXHp/M3BYWp5EMe9HF7A0lX0GuA7YbuAYAROAJ4Bpqey7FPN8AywEeoA9gN8BU1KbtwF/keoE8FetPgZtdCznAien5ZXAF9LywHF4c3p/NTA/LZ8BXFqx/bUUH6b3A/pS+VaPc+XXtDwq/ibdIxi5eyPit1XK7wM+LukrwJ9ExAtV6vwK+KKkv6d47HsdcDTFP/n7VMzodjSwd5Vt3wvcGBEvRsRa4AbgfRTJ4dj0yeZ9EbEm1T9S0j2SHgaOAt4+4u+4vWzL8bmG4p8GwKnANZImAe8Brk3H53Jg94ptro1XTyncBVwkaRawS2x5OuMY4PKB8oh4FngL8NuIWJ7qzAMOH7TdO4GFEbEqbfuDijqvUMwPPhY19FgOsY+B8rcCj1Xs7+qtxPVvEbEpIpYBb6iyvtpxhlH4N+lEMHIvViuMiF9Q/HE+CcyVdLqkv6w4VdATEf8HOBFYB/xU0lEUs7nNi4gD0ustEfGVeoNJ/0AOokgIs1P3cwJFz+LkiPgT4DsUn3hyMOLjQzGX9gck7UqRnG+j+Ft5ruL4HBARb6u2v4j4GvApip7eXQOnkEq2Psbuue1GH8u691HDyxXLdZ3eGa1/k04EDSbpTcAfIuI7wBXAQRFxY8U/j0WS9qb41NFL0XXdn+K0w8mSXp/a2TW1BbBB0vi0fAfwF5K2l7QD8JfAHZL2AF6KiO8D36BICgO/YKvTJ9qTS/8BjHL1HJ/U07oP+GeK0wKvRMTzwG8lfTi1I0l/OsQ+9omIhyPiwtTO4ERwK/BpSdul+rsCvwG6JHWnOqcBPx+03b3A+yVNljQO+EiVOtkY6bGs0exvgL0ldaX3pwxdtaZqx3lU/k2WOXl9ro4A/k7SBmAtcHqVOn8FnJbq/B743xHxrKTzgFvSxa0NFPM5P07xxOMSSfdHxEclzaX4pwDFOc8HJP0Z8A1Jm9K2n4mI5yR9B1ia9nNfSd9zOzmC2scHilMF16b6Az4K/Es6TuOBHwIPVdn2bElHApsoruEsYPPTSFcA0yiO6QaKazuXSvo4xamn7SiO1WWVjUbEUypuX7yd4hPoTyLix/V+42PQEYz8WFYVEesk/Q3w75JeZNv+ZoY6zqPub9K3j5qZVZA0KSLWprt5vgWsiIiLWx1XmXxqyMxsc3+dbgh4BNiZ4saAMc09AjOzzLlHYGaWOScCM7PMORGYmWXOicAMkPQlSY+kMWEelHRIA9v+qaRdGtWeWaP5OQLLnqRDgRMoHkh6WdJkijGgGiIijm9UW2ZlcI/ArHjYa3VEvAwQEasj4j9UjEr5dRWjfd478NSvpCmSrk8jSN4n6bBUPknSVan+EkknpfKVKbkMNcLsOBWjWS5N236uRT8Hy5QTgRncAuwlabmkb0t6f8W6NWlMmEuBb6ayfwYujoh3Uoz6ekUq/18D9SNifwaNayPpbRRDFhwWEQdQDBT3UeAAYM+IeEfa11XlfJtm1fnUkGUvPUV6MMUorkdSjDY6MBPV1RVfB54uPQbYT68OI79TGjfmGIoRLgfa/eOgXVWOMAvFoHRPUwxbvbekS4CfUCQms6ZxIjAD0mBkC4GFaXjgGQOrKqulrx3AuyNifWUbqj2/yMAIs+dusaIYwO7PgP9OMRbVJ4b5LZiNmE8NWfYkvUVplrjkAIrB/uDV0SdPoZhHAopP7GdWbH9AWryVYqDAgfLXDdpV1RFm0/WDjoi4HjiPYuRYs6Zxj8CsmEXsknSL50agD5hJcSfR6yQtoRh7/iOp/izgW6l8O+AXFJ/kZ6fypRTn/79KMXEQABGxbIgRZtcBV6UygC16DGZl8lhDZkOQtBLoiYjVrY7FrEw+NWRmljn3CMzMMucegZlZ5pwIzMwy50RgZpY5JwIzs8w5EZiZZc6JwMwsc/8fO5RrJpj/m3wAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(x = \"Species\", y = \"SepalLengthCm\", data = df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "b2XdRTh2XO9j",
        "outputId": "16f7607f-e705-4e3a-fa0f-9ae505c9f251"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f98e4e4f490>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "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": [
        "sns.boxplot(x = \"Species\", y = \"PetalWidthCm\", data = df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "43x0mHqqXR8j",
        "outputId": "def83d42-cfa2-410b-9539-3af1eec8c7e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f98e4c93fd0>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "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": [
        "sns.boxplot( y=\"SepalLengthCm\" , data=df);\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        },
        "id": "MXwT6nJfXZ2K",
        "outputId": "a4898f6d-93f0-4fdb-be6d-17c8377a4570"
      },
      "execution_count": null,
      "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": [
        "sns.boxplot( y=\"SepalWidthCm\" , data=df);\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        },
        "id": "tnZOA5wxXcGQ",
        "outputId": "ea78002b-33fa-46d6-8133-a30cd35fe115"
      },
      "execution_count": null,
      "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": [
        "sns.boxplot( y=\"PetalLengthCm\" , data=df);\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        },
        "id": "nAOB70F6XgBK",
        "outputId": "07c8c998-ce13-460e-b03a-cac51e2937ab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAADrCAYAAABjGI3/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAANv0lEQVR4nO3df4xl5V3H8fd3Z0FmKVjrTolZul3oNIu0CtRbLKkxbUkVaN000dSCVIs/Vv/odDD4ixjTpkk1xB/pdqKNG9qKKTRVLLESJNJapQ0GMwPbH8A2jqvoji07iLALuyy/vv5x74bZ3dm5Z+be5967z75fyWRnzrnnnA+E/eTwzHOeE5mJJKk+64YdQJJUhgUvSZWy4CWpUha8JFXKgpekSlnwklSp9cMOsNTGjRtzy5Ytw44hSSeNubm5xzNzYrl9I1XwW7ZsYXZ2dtgxJOmkERGPnmifQzSSVKliBR8RWyNi15Kv/RFxfanrSZKOVmyIJjO/DVwMEBFjwAJwR6nrSZKONqghmsuBf8/ME44VSZL6a1AF/z7gc8vtiIjtETEbEbOLi4sDiiNJ9Ste8BFxOrAN+Ovl9mfmzsxsZWZrYmLZmT6SpDUYxB38lcADmfnYAK4lSeoYxDz4qznB8IxG18zMDPPz88OOMRIWFhYA2LRp05CTjIbJyUmmpqaGHUMNFC34iDgTeCfwqyWvI5V06NChYUeQ1qRowWfmM8D3l7yGyvAO7WXT09MA7NixY8hJpNXxSVZJqpQFL0mVsuAlqVIWvCRVyoKXpEpZ8JJUKQtekiplwUtSpSx4SaqUBS9JlbLgJalSFrwkVcqCl6RKWfCSVCkLXpIqZcFLUqUseEmqlAUvSZWy4CWpUha8JFXKgpekSlnwklQpC16SKmXBS1KlihZ8RLwyIm6PiN0R8UhEXFbyepKkl60vfP4dwN2Z+TMRcTqwofD1JEkdxQo+Ir4X+HHgAwCZ+RzwXKnrSZKOVnKI5jxgEfhMRDwYETdHxJkFrydJWqJkwa8H3gR8MjMvAZ4BfufYD0XE9oiYjYjZxcXFgnEk6dRSsuD3Ansz8/7Oz7fTLvyjZObOzGxlZmtiYqJgHEk6tRQr+Mz8LvDfEbG1s+ly4OFS15MkHa30LJop4NbODJo9wHWFrydJ6iha8Jm5C2iVvIYkaXk+ySpJlbLgJalSFrwkVcqCl6RKWfCSVCkLXpIqZcFLUqUseEmqlAUvSZWy4CWpUha8JFXKgpekSlnwklQpC16SKmXBS1KlLHhJqpQFL0mVsuAlqVIWvCRVyoKXpEpZ8JJUKQtekiplwUtSpSx4SarU+mEHGCUzMzPMz88PO4ZGzJH/Jqanp4ecRKNmcnKSqampYcc4oaIFHxH/CRwAXgReyMxWyev1an5+nl3feoQXN7xq2FE0QtY9lwDM7XlsyEk0SsYOPjHsCF0N4g7+7Zn5+ACu0xcvbngVhy64atgxJI248d13DTtCV47BS1KlShd8Av8QEXMRsX25D0TE9oiYjYjZxcXFwnEk6dTRaIgmIlrA7wKv7RwTQGbmD3c59McycyEiXg3cExG7M/PepR/IzJ3AToBWq5Wr/QeQJC2v6Rj8rcBvAt8EXmp68sxc6Py5LyLuAC4F7l35KElSPzQt+MXM/OJqThwRZwLrMvNA5/ufAD662oCSpLVpWvAfjoibgS8Dh49szMwvrHDMOcAdEXHkOrdl5t1rDSpJWp2mBX8dcAFwGi8P0SRwwoLPzD3ART2lkyStWdOCf3Nmbi2aRJLUV02nSd4XERcWTSJJ6qumd/BvAXZFxH/QHoNvOk1SkjQkTQv+iqIpJEl9t2LBR8SbgY2Z+ffHbL8S2Ac8WjCbJKkH3cbgbwIeXmb7w8Af9j+OJKlfuhX8WZl53F16Z9vGMpEkSf3QreC/b4V9G/oZRJLUX90K/ksR8bHoPI4KEG0fBf6xbDRJUi+6zaK5AbgZmI+IXZ1tFwGzwC+XDCZJ6s2KBZ+ZzwBXR8T5wBs6mx/qLEMgSRphjebBZ+aeiDhMez34cyPi3M52l/6VpBHV9IUfNwE/CzzE0YuNWfCSNKKaPsn6HmBrZh7u+klJ0khoutjYHtpLBUuSThLdliqYoT0Uc5D2YmPHvvDjQ2XjDdbCwgJjB59ifPddw44iacSNHfxfFhZeGHaMFXUbopnt/DkHHPvKPl+QLUkjrNs0yVsAImI6M3cs3RcR0yWDDcOmTZv47uH1HLrgqmFHkTTixnffxaZN5ww7xoqajsH/wjLbPtDHHJKkPus2Bn81cA1wXkQsHaI5C3iiZDBJUm+6jcHfB3yH9sqRf7xk+wHgG6VCSZJ6120M/lHaL/W4bDBxJEn90vRJ1gMcP2vmKdqzbG5wbRpJGj1Nn2T9OLAXuI32C7ffB7wOeAD4NPC2EuEkSWvXdBbNtsz888w8kJn7M3Mn8JOZ+XlWfikIETEWEQ9GxJ09p5UkNda04A9GxHsjYl3n673As5193R54mgYeWXNCSdKaNC34nwPeD+wDHut8f21EjAMfPNFBnWWF30X7pSGSpAFqvB488FMn2P21FQ79OPBbtOfNS5IGqOksmgngV4AtS4/JzF9c4Zh3A/sycy4i3rbC57YD2wE2b97cKLQkqbums2j+Fvgq8CXgxYbHvBXYFhFXAWcAZ0fEZzPz2qUf6vzCdidAq9VyATNJ6pOmBb8hM397NSfOzBuBGwE6d/C/cWy5S5LKafpL1js7d+KSpJNE04Kfpl3yz0bE/og4EBH7m14kM/8pM9+9toiSpLVoOovGWTCSdJJpdAcfbddGxO91fn5NRFxaNpokqRdNh2j+jPaKktd0fn4a+NMiiSRJfdF0Fs2PZuabIuJBgMz8v4g4vWAuSVKPmt7BPx8RY3TWnek8+PRSsVSSpJ41LfhPAHcAr46Ij9FenuD3i6WSJPWs6SyaWyNiDric9nrw76H9wg9J0ohqOgZPZu4Gdh/5OSL+C3DxGEkaUU2HaJYTfUshSeq7XgrehcEkaYStOEQTETMsX+QBvLJIIklSX3Qbg59d4z5J0pCtWPCZecuggkiS+qvbEM3fscJYe2Zu63siSVJfdBui+aOBpJAk9V23IZp/HlQQSVJ/NX3p9uuBPwAupP1+VQAy8/xCuSRJPWo6D/4zwCeBF4C3A38JfLZUKElS75oW/HhmfhmIzHw0Mz8CvKtcLElSr5quRXM4ItYB/xYRHwQWgFeUiyVJ6tVqXrq9AfgQ8CPAtcDPlwolSepd04LfkplPZ+bezLwuM38aV5KUpJHWtOBvbLhNkjQiuj3JeiVwFbApIj6xZNfZtGfUSJJGVLdfsv4P7UXFtgFzS7YfAH69VChJUu+6Pcn6deDrEXFb57ObM/PbTU4cEWcA9wLf0zn29sz8cI95JUkNNR2DvwLYBdwNEBEXR8QXuxxzGHhHZl4EXAxcERFvWXNSSdKqNC34jwCXAk8CZOYu4LyVDsi2pzs/ntb58i1QkjQgTQv++cx86phtXcs6IsYiYhewD7gnM+9f5jPbI2I2ImYXFxcbxpEkddO04B+KiGuAsYh4fedVfvd1OygzX8zMi4FzgUsj4o3LfGZnZrYyszUxMbGq8JKkE2ta8FPAG2iPq98GPAVc3/Qimfkk8BXaY/mSpAHoNg/+DODXgEngm8Blmdlo/ntETNAe2nkyIsaBdwI39ZhXktRQt3nwtwDPA18FrgR+kOZ37j8A3BIRY7T/T+GvMvPOtQaVJK1Ot4K/MDN/CCAiPgX8a9MTZ+Y3gEt6yCZJ6kG3Mfjnj3zTdGhGkjQaut3BXxQR+zvfBzDe+TloT3U/u2g6SdKadVuqYGxQQSRJ/dV0mqQk6SRjwUtSpSx4SaqUBS9JlbLgJalSFrwkVcqCl6RKWfCSVCkLXpIqZcFLUqUseEmqlAUvSZWy4CWpUha8JFXKgpekSlnwklQpC16SKmXBS1KlLHhJqpQFL0mVsuAlqVLFCj4iXhMRX4mIhyPioYiYLnUtSdLx1hc89wvADZn5QEScBcxFxD2Z+XDBa0qSOordwWfmdzLzgc73B4BHgE2lridJOtpAxuAjYgtwCXD/IK4nSRpAwUfEK4C/Aa7PzP3L7N8eEbMRMbu4uFg6jiSdMooWfEScRrvcb83MLyz3mczcmZmtzGxNTEyUjCNJp5SSs2gC+BTwSGb+SanrSJKWV/IO/q3A+4F3RMSuztdVBa8nSVqi2DTJzPwaEKXOL0lamU+ySlKlLHhJqpQFL0mVsuAlqVIWvCRVyoKXpEpZ8JJUqZLLBZ+Uxg4+wfjuu4YdQyNk3bPtJZReOuPsISfRKBk7+ARwzrBjrMiCX2JycnLYETSC5ucPADB5/mj/ZdagnTPynWHBLzE1NTXsCBpB09Ptl5Ht2LFjyEmk1XEMXpIqZcFLUqUseEmqlAUvSZWy4CWpUha8JFXKgpekSlnwklQpC16SKmXBS1KlLHhJqpQFL0mVsuAlqVIWvCRVyoKXpEoVK/iI+HRE7IuIb5W6hiTpxErewf8FcEXB80uSVlDsjU6ZeW9EbCl1fpU1MzPD/Pz8sGOMhCP/Ho682elUNzk56dvPThJDf2VfRGwHtgNs3rx5yGmk442Pjw87grQmkZnlTt6+g78zM9/Y5POtVitnZ2eL5ZGk2kTEXGa2ltvnLBpJqpQFL0mVKjlN8nPAvwBbI2JvRPxSqWtJko5XchbN1aXOLUnqziEaSaqUBS9JlbLgJalSFrwkVarog06rFRGLwKPDziEtYyPw+LBDSMt4bWZOLLdjpApeGlURMXuipwWlUeUQjSRVyoKXpEpZ8FIzO4cdQFotx+AlqVLewUtSpSx4SaqUBS9JlbLgJalSFrwkVer/AV6WKOOKTRnJAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot( y=\"PetalWidthCm\" , data=df);\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        },
        "id": "XGHH2oNnXkFD",
        "outputId": "0036c1e2-f8b5-4cb0-f93d-98ed3c420802"
      },
      "execution_count": null,
      "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": [
        "sns.pairplot(df,hue = 'Species') # A pairplot plot a pairwise relationships in a dataset."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 685
        },
        "id": "izOAp76EXnya",
        "outputId": "91438406-178a-4c91-e5b9-a1eb8642ab60"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7f98e4a17a10>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 823.25x720 with 20 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Data PreProcessing or Correlation Matrix** "
      ],
      "metadata": {
        "id": "jdF57quNd1Qm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# heatmap uses to show 2D data in graphical format.Each data value represents in a matrix and it has a special color. \n",
        "#‘True‘ value to annot then the value will show on each cell of the heatmap\n",
        "# we change the color of seaborn heatmap but center parameter will change cmap according to a given value by the creator.\n",
        "plt.figure(figsize=(10,7))\n",
        "sns.heatmap(df.corr(),annot=True,cmap=\"seismic\") \n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        },
        "id": "Tbgm0-z5XvTB",
        "outputId": "19f38fac-4ee6-4331-930a-a75e39ec0c7d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Label Encoder**"
      ],
      "metadata": {
        "id": "yHGqvDP_d6TP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "le = LabelEncoder() # LabelEncoder can be used to normalize labels."
      ],
      "metadata": {
        "id": "fmtY6tC8X1RF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Species'] = le.fit_transform(df['Species'])  # fit_transform: Fit label encoder and return encoded labels.\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Z8y4coQ-X36S",
        "outputId": "4bb0e524-5077-4f85-cbe3-831b759fada1"
      },
      "execution_count": null,
      "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-e6683b34-4f5a-4788-8af4-bd6bd74e27f8\">\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-e6683b34-4f5a-4788-8af4-bd6bd74e27f8')\"\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-e6683b34-4f5a-4788-8af4-bd6bd74e27f8 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-e6683b34-4f5a-4788-8af4-bd6bd74e27f8');\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": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.drop(columns=['Species']) # Drop column\n",
        "y = df['Species'] \n",
        "X[:5] # # Return list from beginning until index 5"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "a_-EWY3OX7PJ",
        "outputId": "97c8874e-cc21-4ebd-94ba-3dd60f0e1d76"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
              "0            5.1           3.5            1.4           0.2\n",
              "1            4.9           3.0            1.4           0.2\n",
              "2            4.7           3.2            1.3           0.2\n",
              "3            4.6           3.1            1.5           0.2\n",
              "4            5.0           3.6            1.4           0.2"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-953ad640-b285-4d4d-8eef-4410c067eb92\">\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>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-953ad640-b285-4d4d-8eef-4410c067eb92')\"\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-953ad640-b285-4d4d-8eef-4410c067eb92 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-953ad640-b285-4d4d-8eef-4410c067eb92');\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": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-69uSUY9YAuF",
        "outputId": "dbe9ae70-bf16-42d4-ef5e-1e2489f83e89"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0\n",
              "1    0\n",
              "2    0\n",
              "3    0\n",
              "4    0\n",
              "Name: Species, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Splitting the Dataset into the Training set and Test set**"
      ],
      "metadata": {
        "id": "_dET5gt5d_wc"
      }
    },
    {
      "cell_type": "code",
      "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 = 1)"
      ],
      "metadata": {
        "id": "Ta1g5AyMYE4a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Selecting the Models and Metrics(Supervised Machine Learning Models)**"
      ],
      "metadata": {
        "id": "BFJZK-useFdn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import accuracy_score"
      ],
      "metadata": {
        "id": "D2MO8dVzYH1p"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lr = LogisticRegression()\n",
        "knn = KNeighborsClassifier()\n",
        "svm = SVC()\n",
        "nb = GaussianNB()\n",
        "dt = DecisionTreeClassifier()\n",
        "rf = RandomForestClassifier()"
      ],
      "metadata": {
        "id": "MM-23DyxYMdx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Training and Evaluating the Models**"
      ],
      "metadata": {
        "id": "ylxqvRMteK_3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "models = [lr, knn, svm, nb, dt, rf]\n",
        "scores = []\n",
        "\n",
        "for model in models:\n",
        "  model.fit(X_train, y_train) # LogisticRegression.fit(X_train, y_train) # Fitting Support Vector Classifer to the Training set\n",
        "  y_pred = model.predict(X_test) #LogisticRegression.predict(X_test) # Predicting the Test set results\n",
        "  scores.append(accuracy_score(y_test, y_pred)) # Accuracy on the Test set results  \n",
        "  print(\"Accuracy of \" + type(model).__name__ + \" is\", accuracy_score(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lnznRmLeYPt7",
        "outputId": "0415b81b-024a-4385-f8d2-742edccbcd0e"
      },
      "execution_count": null,
      "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": "stream",
          "name": "stdout",
          "text": [
            "Accuracy of LogisticRegression is 0.9777777777777777\n",
            "Accuracy of KNeighborsClassifier is 0.9777777777777777\n",
            "Accuracy of SVC is 0.9777777777777777\n",
            "Accuracy of GaussianNB is 0.9333333333333333\n",
            "Accuracy of DecisionTreeClassifier is 0.9555555555555556\n",
            "Accuracy of RandomForestClassifier is 0.9555555555555556\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "results = pd.DataFrame({\n",
        "    'Models': ['Logistic Regression', 'K-Nearest Neighbors', 'Support Vector Machine', 'Naive Bayes', 'Decision Tree', \n",
        "               'Random Forest'],'Accuracy': scores})\n",
        "\n",
        "results = results.sort_values(by='Accuracy', ascending=False)\n",
        "print(results)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m06CDhuPYUaf",
        "outputId": "07f66b48-fbe9-4817-92c6-13f596f87903"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   Models  Accuracy\n",
            "0     Logistic Regression  0.977778\n",
            "1     K-Nearest Neighbors  0.977778\n",
            "2  Support Vector Machine  0.977778\n",
            "4           Decision Tree  0.955556\n",
            "5           Random Forest  0.955556\n",
            "3             Naive Bayes  0.933333\n"
          ]
        }
      ]
    }
  ]
}