task_1_Iris_Flowers_Classification_ML_Project.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Devikasarwade/LGM_DATA-SCIENCE/blob/main/task_1_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": [
        "LetsGrowMore- Virtual Internship Program (VIP)- Data Science\n",
        "\n",
        "Beginner Level Task\n",
        "\n",
        "Name:- Devika Hanumant Sarwade\n",
        "\n",
        "Task 1 :- Iris Flower classification"
      ],
      "metadata": {
        "id": "kFOpuE3wU796"
      },
      "id": "kFOpuE3wU796"
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "tnIFoIkz9KDt"
      },
      "id": "tnIFoIkz9KDt",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "eabe9102",
      "metadata": {
        "id": "eabe9102"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "85b0b589",
      "metadata": {
        "id": "85b0b589",
        "outputId": "70666523-5895-4b60-e8ce-64712673c103"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>"
            ],
            "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"
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data1 = pd.read_csv(\"C:/Users/Admin/Downloads/Iris.csv\")\n",
        "data1.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0c6bfea7",
      "metadata": {
        "id": "0c6bfea7",
        "outputId": "057de648-1691-4d6c-e6d9-fca5bb82ac9c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Index(['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm',\n",
            "       'Species'],\n",
            "      dtype='object')\n"
          ]
        }
      ],
      "source": [
        "print(data1.columns)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8619f2dc",
      "metadata": {
        "id": "8619f2dc",
        "outputId": "279077e4-9456-49db-c37b-04c0cf1042c8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm', 'Species']\n"
          ]
        }
      ],
      "source": [
        "#anotherway\n",
        "column = list(data1)\n",
        "print(column)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fd0cb8de",
      "metadata": {
        "scrolled": true,
        "id": "fd0cb8de",
        "outputId": "207dc118-8cb4-4759-c91e-da123a53c4f3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 150 entries, 0 to 149\n",
            "Data columns (total 6 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   Id             150 non-null    int64  \n",
            " 1   SepalLengthCm  150 non-null    float64\n",
            " 2   SepalWidthCm   150 non-null    float64\n",
            " 3   PetalLengthCm  150 non-null    float64\n",
            " 4   PetalWidthCm   150 non-null    float64\n",
            " 5   Species        150 non-null    object \n",
            "dtypes: float64(4), int64(1), object(1)\n",
            "memory usage: 7.2+ KB\n"
          ]
        }
      ],
      "source": [
        "data1.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8379a046",
      "metadata": {
        "id": "8379a046",
        "outputId": "afa71b9e-d2c6-46af-a4e5-bb72ce1b5c86"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Id                 int64\n",
              "SepalLengthCm    float64\n",
              "SepalWidthCm     float64\n",
              "PetalLengthCm    float64\n",
              "PetalWidthCm     float64\n",
              "Species           object\n",
              "dtype: object"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data1.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1ae46afb",
      "metadata": {
        "id": "1ae46afb",
        "outputId": "9edd253b-385a-46b8-a64b-10c101e87099"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[<AxesSubplot:title={'center':'Id'}>,\n",
              "        <AxesSubplot:title={'center':'SepalLengthCm'}>],\n",
              "       [<AxesSubplot:title={'center':'SepalWidthCm'}>,\n",
              "        <AxesSubplot:title={'center':'PetalLengthCm'}>],\n",
              "       [<AxesSubplot:title={'center':'PetalWidthCm'}>, <AxesSubplot:>]],\n",
              "      dtype=object)"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXEAAAEICAYAAACpqsStAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAkUklEQVR4nO3de5hdZXn38e8PEIEMp5gwhBAZioggqSBRoRwMBSqgGPq+gCBS8FC0ryi0qTViq9hKjVeRQuuhTTlWKGcQFORYxogoQhAMGBDEQMAYIJDDRIodvd8/nmfDymTPzJ49s/faa/L7XNdcs9f5Xms/697PetZJEYGZmVXTBmUHYGZmzXMSNzOrMCdxM7MKcxI3M6swJ3EzswpzEjczqzAn8Q4kqUdSSNqo7FjMhiJpsaSDy46jSFKvpI+UHUe7OImXqBN3AKsuSftJulvSSkkvSPqBpLe1cflnSLqkXctrdpmSNs7TPSZpTd4PL5DU06IwW8pJ3GwckLQF8B3gX4GJwFTgC8DLZcbVoa4G3gu8H9gSeAuwADiozKCa5STeASRtKOksSc9LegJ4d9kxWeW8ESAiLouI30XESxFxa0T8FEDShyQtkvSipFsk7VCbMDfdfVLSE7kM/pOkDfKwnST9t6TledilkrYaaXCS9s5HCSskPShpZmFYr6R/yEcOqyXdKmlSYfifSXoyx/B3tSNYSYcCpwPvk9Qn6cHCIneoN7985HsIMCsi7o2I/ohYGRFfi4jzC/F8McfbJ+nbkl6X132VpHs7qdbuJN4Z/hx4D7AnMAM4qtxwrIJ+DvxO0sWSDpO0dW2ApCNJye7/AJOB7wOXDZj+T0ll763ALOBDtcmBLwHbAbsC04AzRhKYpKnAjcAXSUcJfw1cI2lyYbT3Ax8EtgE2zuMgaTfg68DxwBRSzXkqQETcDPwjcEVEdEXEW4abH3Aw8OOIWDJM2McCJ+Rl7QT8ELgwx78I+PxItkErOYl3hmOAcyJiSUS8QNppzBoWEauA/YAA/gN4TtINkrqBjwJfiohFEdFPSnx7FGvjwJcj4oWIeAo4Bzguz/fxiLgtIl6OiOeAs4F3jjC8DwA3RcRNEfH7iLgNuA84vDDOhRHx84h4CbgS2CP3Pwr4dkTcFRG/BT6X13E4g83vdcDSBqf/RUSsBL4L/CIibs/b7ypShasjOIl3hu2AYs3gybICserKSfqkiNge2J1Urs4BdgDOzU0ZK4AXSDXsqYXJB5a/7QAkbSPpcknPSFoFXAJMYmR2AI6uLT/HsB+pZl3z68Ln3wBd+fNa+0ZE/AZY3sAyB5vf8gHLHcyywueX6nR30SGcxDvDUtJhas3rywrExoeIeAS4iJTMlwAfjYitCn+bRsTdhUkGlr9f5c9fItV8/zAitiDVqjXCcJYA3xyw/AkRMbeBaZcC29c6JG1Kqk3XjPQxrLcDb5e0/bBjVoSTeGe4EvikpO1zW+acsgOyapH0Jkmza8lJ0jRSk8iPgH8DPiPpzXnYlpKOHjCLT0naOk93KnBF7r850AesyG3bnxomlA0kbVL4ey2p9n6EpHflk/ibSJrZYCK9Ok/7R5I2Jl1xU/wRWQb01E7EDicibgduA66TtJekjSRtLuljkj403PSdyEm8M/wHcAvwIHA/cG254VgFrQbeAdwjaQ0peT8EzI6I64AvA5fnJpGHgMMGTH896TK7B0gnIc/P/b9AOtm5MvcfrmweR2puqP39Ip9EnEU6ufocqWb+KRrIPxHxMPAJ4HJSrXw18CyvXjp5Vf6/XNL9w80vOwq4ifRDtZK0PWaQaumVI78Uwmz9JimAnSPi8bJjGY6kLmAFKd5flhxOR3BN3Mw6mqQjJG0maQJwFrAQWFxuVJ3DSdzMOt0s0onWXwE7A8eGmxBe4eYUM7MKc03czKzC2vqo00mTJkVPT886/desWcOECRPaGcqoON7WGyrmBQsWPB8Rk+sO7DCDlfl26rTvv9Pigc6PacgyHxFt+9trr72injvvvLNu/07leFtvqJiB+6KN5XY0f4OV+XbqtO+/0+KJ6PyYhirzbk4xM6uwjnlzTM+cG8sOoWGzp/dzkuNtqYsO7axD2/VRM/vk4rl+inK7uSZuZlZhHVMTN7P1k2v8o+OauJlZhTmJm5lVmJO4mVmFOYmbmVWYk7iZWYU5iZuZVZgvMTSrkGYux/ONU+Oba+JmZhXmJG5mVmFO4mYDSJom6U5JiyQ9LOnU3H+ipNskPZb/b112rGZO4mbr6ie9JX5XYG/g45J2A+YAd0TEzsAdudusVE7iZgNExNKIuD9/Xg0sAqaS3vV4cR7tYuDIUgI0K/DVKWZDkNQD7AncA3RHxFJIiV7SNoNMczJwMkB3dze9vb1jFs/s6f0jnqavr6+pGJpZViPLGRhPq5YzEs1uo1ZqNKZhk7ikC4D3AM9GxO6530TgCqAHWAwcExEvNh+uWeeR1AVcA5wWEaskNTRdRMwD5gHMmDEjZs6cOWYxNfNc+IsOnUAzMTSzrMXHD7+c3t7eteJp1XJGYmBMnaDRmBppTrkIOHRAP7cN2rgm6TWkBH5pRFybey+TNCUPnwI8W1Z8ZjXDJvGImA+8MKC32wZt3FKqcp8PLIqIswuDbgBOzJ9PBK5vd2xmAzXbJt5Q2yA01j7Y19fH7Om/azKU9uvetLl2vLJULV4ovY1yX+AEYKGkB3K/04G5wJWSPgw8BRxdTnjmF0m8quUnNhtpH+zt7eUrd61pdShjZvb0fr6ysDrnhKsWLzTfjjsWIuIuYLAG8IPaGYvZcJq9xNBtg2ZmHaDZJO62QTOzDjBsEpd0GfBDYBdJT+f2wLnAIZIeAw7J3WZm1mbDNpRGxHGDDHLboJmtpZETjrOn9zd1bbjV59vuzcwqzEnczKzCnMTNzCqsWhcPm9mILXxmpdugxzHXxM3MKsxJ3MyswpzEzcwqzEnczKzCnMTN6pB0gaRnJT1U6OcXJVvHcRI3q+8i/DIUqwAncbM6/DIUqwpfJ27WuEq+KLnTXgpSVjxDfQ/j+kXJZjYynfai5E57KUhZ8Qz1cuXx/qJkM0v8MhTrOJ3z82zW+WovQ5nLGLwMpZn3RJoN5Jq4WR1+GYpVhWviZnX4ZShWFa6Jm5lVmJO4mVmFOYmbmVWY28TNbL0w1NVAQ728efHcd4/psgbTzHLANXEzs0pzTdzMbAidfj2/a+JmZhXmJG5mVmFO4mZmFeYkbmZWYU7iZmYV5iRuZlZhTuJmZhU2qiQu6VBJj0p6XJJfGmvjnsu8dZqmk7ikDYGvAYcBuwHHSdptrAIz6zQu89aJRlMTfzvweEQ8ERG/BS4nvQ3cbLxymbeOM5rb7qcCSwrdTwPvGDhS8c3fQJ+kR+vMaxLw/ChiaatPOt6WO/DLQ8a8QztjKRjLMt82nfb9d1o80Bkx6cvr9CrGNGiZH00SV51+sU6Pwpu/B52RdF9EzBhFLG3leFuvQ2MeszLfTp22LTstHqh2TKNpTnkamFbo3h741SjmZ9bpXOat44wmid8L7CxpR0kbA8eS3gZugKTFkg5uctqHJc0cZNhMSU8PMW2PpJDkJ1SOPZd5QNIZki4pO44iSSdJuqvsOMrQdBKPiH7gFOAWYBFwZUQ83OTsWnroKWk/SXdLWinpBUk/kPS2Ucyy4XglTclJtbvQ77OD9LsZICLeHBG9Dc6/kR+LteKV9EZJV0l6Pm+Tn0r6q3z1RafomOaImjEu8y2Ry8NLkvokLZN0IXDxMNP0SvpIk8trptIwqu+22YqKpHdJmi9ptaTnJH1P0nvHIqYWaSimUV0nHhE3RcQbI2KniDhzFPNp2QaUtAXwHeBfgYmkk1NfAF5udp4jiTcilgKPAwcUeh8APFKn3/xmYxomhlfilbQTcA/pBN30iNgSOBqYAWzeiuU3o5VlYjTGqsy32BER0QW8FXgbqcx3jDK+W0lHAVcB/0lqBusGPgccUVZMw2k0pvXhjs03AkTEZRHxu4h4KSJujYifAkj6kKRFkl6UdIukV84C51/7T0p6Itda/0nSBnnYTpL+W9LyPOxSSVsNEsN8csLOtd09gXMH9Nsnj7dW7VrSppIuyvH9jLRT1uL7JvB64Nu55vU3hWUeL+mpHNtnC/2/ANwdEX+Vf2CIiEcj4v0RsaJQy/mgpCV5uR+T9LZcY18h6avNfBHWXhHxDPBdYHdJe+ej0RWSHqw110k6E9gf+GouQ1/N/c/N3/8qSQsk7T/S5UvaUtL5kpZKekbSF2tHe7XmD0ln5TL2S0mHFabdsVBrvl3S1wpNOLXKzooc8z6F6daZnyQBZwP/EBHnRcTKiPh9RHwvIv68EM8PJP1z3kZPSPqj3H+JpGclnTjSbdAWETGu/4AtgOWkQ8rDgK0Lw44k1ZJ3JV2p87ekBFcbHsCdpBr864GfAx/Jw94AHAK8FphMKljnFKZdDBycP58IPJg/z8jj7jyg30vAxnWmnQt8P8cwDXgIeLrecnJ3T477P4BNgbeQjjp2zcN/DXxwiO1Vm/7fgE2APwH+B/gWsA2pVvcs8M6yv1v/1f3+imVnGvBwLgvLgcNJFbdDcvfkPF5vrVwX5vMB4HV5v5idy80medgZwCUDystGdWL5FvDvwIRcdn4MfDQPOwn4X+DPgQ2BvyCdJFYe/kPgLGBjYD9g1VDLHGp+wJvy+DsOsd1OAvqBD+bpvwg8Rbq567V5P1gNdJX9Ha8Te8kF7lDgUVIindPC5ewKXES6uqCfdDKqm1RL+XBhvA2A3wA75O7IMS4GFpKaIFblYROB24DH8v/jgZ8MsjP1AL8Dtgb+Ejgz93+m0O/OQaZ9Aji0MOxk1k3iN5MS60OFAr57Ib6VtfXMBf2CvM0fBd41YFvVpp9a6LcceF+h+xrgtFF8HxfU4i30OyNvjwfy3+GFYZ8ZLF7/rbNtFwN9wIpc1pcDS4HlA8a7BTgxf+5lQBKvM98XgbcUvqshk3jev14GNi30O45UIbk6x/RbYJ88bLM8n21JFaZ+YLPCtJcMtUxSEn680F2c37758yZ11muXXN6eyvGuAk4DpudpugfsB3u04Tv8S9KP70PAZfXiLv6V1pyiNt7CHBGLIuKkiNielNy2A84hXUB/bj58WgG8QPrlLrYh1m7uOBD4GCnRAPw9qXaxGelw9ELSxfn1lr+Y9AOyH6kJ5ft50A8L/QZrD9+OtW8webLOOLeQfmyKTgTuiIidSYXvqNx/BXAw8OY8zdcHOaG5rPD5pTrdXYPE24iL6sQL8M8RsUf+uwkgl4ljG4jXXnVkRGxFKnO7ANcBE2rlPJf1/YApg81A0uzczLgyj78lg5TvQewAvAZYWljmv5OS482kH+Z7SSeIiYjf5Om6SGX+hUI/WHsfGMyvax8GzG95/rzO+kZqStyD1D5+L6kSdx2pjBMRY1nuhyVpKvBJYEZE7E46Kjh2qGnKbBMv5RbmiHiElER2JxWMj0bEVoW/TSPi7sIkxeuCX8+r1wV/gFRL/kNgR+A56t8MUvN9UrLeB7h7QL/9GDyJL60Tw1qrRDpKeGFA//fy6lUJvya1w0PasddExMsR8UtSDfftQ8Q95iJiPuvGO5hZwOVlxjsOLAG+OaCcT4iIuXn4Wjcs5fbvTwPHkJoftyIdzQ1Vvust82VgUm2ZpBOKLwLn15YbESvqTLsUmChps0K/4j6wzg1Ww3g0x/N/hxlvK+AXEVGvotROGwGb5qtvNmOYexHKTOL1bmEe87Pokt6UaxXb5+5ppMO6H5HafT8j6c152JaSjh4wi0+RCu+dwD/z6gbdjHRL7ArSduxmaPOBPwN+FRGrcr+7cr8tSbXyeq7MMW6d1+ETA4YvA/6gznTdkU9ckg5ba1eeLASm5ZO025K2+16SLhnixGy7nJJPnl4gaevcry3lZJwK4FZSeT9G6RK7DSVtonS/wfZ5vIFlaHNSc8ZzwEaSPkc6tzSU1+b5biJpkzzPW4GvSNpC6YKAA0jnVy4knWB/g6QJ6wSdkuh9wBmSNs4nLo8ojPIc8Hvql/t1N0Jqo/gr4O/yCfstJG2gdOlx8QqQyaTmi9JEOhl9Fql5ZymwMiJuHWqaMpN4Q7cwj4HVpOdb3CNpDSl5PwTMjojrgC8Dl0talfsfNmD66/P/7UhtuX8oqVYY30qqodxISpRD+R6p+aV4Q8IDpJOPCwYcOhZ9gdSE8kvSTvHNAcO/RDoh+1PSiajhrCa1afaQ2t1OAP6atNOsbmD6VvkGsBOwB6nwfiX3b1c5GY/2jYi3kprPniWV9edIP4qf4tX9/1zgqHxVx7+Qmue+SzqR/ySprA/XnNFHam6o/f0xqYKyMfAzUg38LNLVYt8APk86TzTY43yPJx21LiedZLyCfFlw3lfOBH6Qm2r2Hm5DRMTVwPuAD5EqYsvyfGv794ak81xXDTevVsqVl1mko/vtSM1gHxhyolY30g/ReL8PcEuh+zPAZ8qKZ5AYA3jDgH5nkJLeo8CU3G8K8GjJsfaw9onCuvEN3M6kHXafsuMdbFinxFv1v1q5LTmGbYHFhe79gRsbnPYK4AstjG0WcGsHfE9HA+cXuv8M+PpQ05RZE6/KLcybStocIB/6/Qmpxn4D6eQh+f/19ScvzWDx3QAcK+m1knYkXer44xLiW4uk4kmnPyVtY+jQeDudpAmDlNvSRMSvgSWSdsm9DiLV0tehdF/CTrnZ41BSkv1WC8M7jpKbUrKngL0lbZavbz+IfPJ3MKU9XyMi+iXVbmHeELggOuwW5mwScEnanmwE/FdE3CzpXuBKSR8mbfiBbeltI+kyYCYwSem5Kp8nXV++TnwR8bCkK0k7Tz/w8Yj4XQfEO1PSHqSjn8XARzsl3orqBq4bWG7LDQlI53QuzRW3J0jXZdezLXAtqYnwaeAvIuInrQgon0A9hFzmyhQR90i6GrifVN5/wnBPgc1VdjMzq6D14bZ7M7Nxq63NKZMmTYqenp66w9asWcOECetcbTQueN3G1oIFC56PiMltXWiThirzrVLF8uaYhzZUmW9rEu/p6eG+++6rO6y3t5eZM2e2M5y28bqNLUll34zRsKHKfKtUsbw55qENVebdnGJmVmF++0uL9My58ZXPs6f3c1KheyiL5767VSFZh+lpsEwUuXzYQK6Jm5lVmJO4mVmFOYmbmVWYk7iZWYU5iZuZVZiTuJlZhTmJm5lVmJO4mVmFOYmbmVWY79jsML6Lz8xGwjVxM7MKc03czCrHR6yvck3czKzCnMTNzCrMSdzMrMKcxM3MKswnNs2sVD1zbhzRi1Nsba6Jm5lVmJO4mVmFOYmbmVWY28Qb0MyNBWZm7eCauJlZhTmJmw0gaZqkOyUtkvSwpFNz/4mSbpP0WP6/ddmxmg3bnCJpGvCfwLbA74F5EXGupInAFUAPsBg4JiJebF2oY8NNI9aAfmB2RNwvaXNggaTbgJOAOyJirqQ5wBzg0yXGadZQTbxWoHcF9gY+Lmk3UgG+IyJ2Bu7I3WaVFxFLI+L+/Hk1sAiYCswCLs6jXQwcWUqAZgXD1sQjYimwNH9eLalYoGfm0S4GenGtxMYZST3AnsA9QHfeH4iIpZK2GWSak4GTAbq7u+nt7a0779nT+0ccz2DzKurr62tovE4xe3o/3Zs2tz1GYqy3SadsZ0VE4yOnAj0f2B14KiK2Kgx7MSLWaSMcUKD3uvzyy+vOu6+vj66urpHE3pSFz6xs+TIG6t4Ulr3UuvlPn7pl62Y+jHZ9b0UHHnjggoiY0erlSOoCvgecGRHXSlrRSJkvmjFjRtx33311h7Xqcaq9vb3MnDlzxPMuS+2Oza8sbO3FcmP9KNp2bmdJg5b5hrdaLtDXAKdFxCpJDU0XEfOAeZAK9GAr3a4NUsatva0uoIuPn9myeQ+nagmjUZJeQyrvl0bEtbn3MklTci18CvBseRGaJQ1lFhdoW58o1VDOBxZFxNmFQTcAJwJz8//rSwjPmtTsRQ2d/jKJYU9sNlCgwQXaxpd9gROAP5b0QP47nJS8D5H0GHBI7jYrVSM18VqBXijpgdzvdFIBvlLSh4GngKNbEqFZm0XEXcBg7YUHtTMWs+E0cnWKC7SZWYfyHZtmZhXmJG5mVmFO4mZmFeYkbmZWYX6euFmFNHKt88D3VXb6dc42Oq6Jm5lVmJO4mVmFuTnFzGwIgzVhDWy2KmpnE5aT+HqqVU/QM7P2cnOKmVmFOYmbmVWYm1PGAb831Gz95SRuZmPGFYr2c3OKmVmFVbom7l99M1vfuSZuZlZhTuJmZhVW6eYUM7NO1M6b6TomiS98ZuWgt7CamVl9bk4xM6uwjqmJm1ln8dVf1TCqJC7pUOBcYEPgvIiYOyZRWUeqt1MP9SS30ejUh225zFunabo5RdKGwNeAw4DdgOMk7TZWgZl1Gpd560SjaRN/O/B4RDwREb8FLgdmjU1YZh3JZd46jiKiuQmlo4BDI+IjufsE4B0RccqA8U4GTs6duwCPDjLLScDzTQXT+bxuY2uHiJjc5mW2osy3ShXLm2Me2qBlfjRt4qrTb51fhIiYB8wbdmbSfRExYxTxdCyv27gxpmW+Var4nTjm5o2mOeVpYFqhe3vgV6MLx6yjucxbxxlNEr8X2FnSjpI2Bo4FbhibsKpJ0hmSLmly2tMlnTfE8MWSDh5ieK+kjzSzbGuYy7x1nKaTeET0A6cAtwCLgCsj4uFRxNLSw8+cBF+S1CdpmaQLJXUNM03TiVHSLZL+JnfOkzRVUhT6Uei3bUT8Y62ttYF5j/jHQtLGebrHJK3J2+MCST0jmU8dpTUbtFsLynyrVPE7ccxNGtUdmxFxU0S8MSJ2iogzRzmvdmyQIyKiC3gr8Dbgb1u4rPnAO+GVdTsAeKTWLzsAeCwift3COGquBt4LvB/YEngLsAA4aDQzbdP31jHGssy3ShW/E8fcvPXytvuIeAb4LrC7pL0l3S1phaQHJc0EkHQmsD/w1Vx7/2ruf66kJZJWSVogaf9BFjMf2FdSbRvvD5wDzBjQb36e71q1a0knSHpS0nJJny30PxQ4HXhfjuvBwjJ3kPQDSasl3SppUp7mYOAQYFZE3BsR/RGxMiK+FhHn53F6JX0xb4s+Sd+W9DpJl+Z1vXcMau1mNsbWyyQuaRpwOLAUuBH4IjAR+GvgGkmTI+KzwPeBUyKiq3AZ2b3AHnn8/wKukrRJncX8GHgtqcYLqdZ9G/D4gH7z68S3G/AN4ARgO+B1pJNoRMTNwD8CV+S43lKY9P3AB4FtgI3z+gAcDPw4IpYMs2mOzcucCuwE/BC4MK/rIuDzw0xvZm1WahKXNE3SnZIWSXpY0qktXuS3JK0A7gK+R7ra4KZ8iPz7iLgNuI+U4OuKiEsiYnmuzX6FlKh3qTOqgP8FviPpEeD1EfEE6YfhAEkTSXf9fa/OtEcB34mI+RHxMvB3wO8bWL8LI+LnEfEScCXpxwbSj8DSBqf/RUSsJB2p/CIibs9twVcBe661gtKGkn4i6TsNzNtarIT9aUxUsRxJ2krS1ZIeydt7n7JiKbsm3g/Mjohdgb2Bj7f4NuYjI2KriNghIv4f0A0cnZtSVuQEvx8wZbAZSJqdv7SVefwtSRf9D/Qy6RbtH5GaP/5X0t6kH5ADSE0pSyLiyTrTbge8UmuOiDXA8gbWr9i2/hugduJ2+VDrVLCs8PmlOt0DTwSfSqqhW2do9/40VqpYjs4Fbo6IN5GOrEuLv9QkHhFLI+L+/Hk1aUNMbWMIS4Bv5sRe+5tQeKjRWjdy5PbvTwPHAFtHxFbASurcBBLpVtg7SMn6ncCaPL8fAPsySFNKtpTC9ciSNiPVpl+Z/QjX83bg7ZK2H+F0g8rzejcw6GWR1l4dsD+NWBXLkaQtSPvv+QAR8duIWFFWPGXXxF+RT5rtCdzTxsVeAhwh6V35kG4TSTMLyW4Z8AeF8Tcn1XaeAzaS9DlgiyHm/yNSLf0TwO0RcU9EvJin/wCDJ/GrgfdI2i9fj/z3rP1dLQN6CidIhxQRt5Pa46+TtJekjSRtLuljkj7UyDzqOAf4Gxpr5rE2K2l/asY5VK8c/QFpH74wNwOdJ2lCWcF0RBLP12tfA5wWEavatdx8om8WqbnjOVLN/FO8ul3OBY6S9KKkfyFdH/xd4OfAk8D/UGj2qDP/PtJOtBrYUdLuedD3SScf6ybxfO3xx0knTpcCL5La72uuyv+XS7q/wdU9CrgJuIJ09PAQMINUSx8RSe8Bno2IBSOd1lqvrP1ppCpcjjYiXab8jYjYk3SUPaesYJp+ANaYBSC9BvgOcEtEnF1qMC0k6fPAmog4q+xYRkvSl0hXsfQDm5CORq6NiA+UGphVan+qajmStC3wo4joyd37A3MiopSH4Jd9dYpI7UqLOr3AjZSkyZK2yp83JV3m90ipQY2RiPhMRGyfC/GxwH93+o63Pqja/lTVcpRvzlsiqXZV2kHAz8qKp+zXs+1L+iVeKOmB3O/0iLipvJDGzBTgYqUXCWxAukW7MpdQWSWN5/2p03wCuDSfs3qCdH9GKUpvTjEzs+Z1xIlNMzNrTlubUyZNmhSTJ09mwoTSrsZpqTVr1njd2mDBggXPl/FmH7NO1NYk3tPTw1lnncXMmTPbudi26e3t9bq1gaR6d7marZcabk4Z+HwDSRMl3ab0fOrbJG3dujDNzKyekdTEa883qN2hOAe4IyLmSpqTuz/dbCA9c24c8TSL55ZyWaaZWcdoqCY+yPMNZgEX588XA0eOaWRmZjasRmvi55Ceb7B5oV93RCyF9OAdSdvUm1DSycDJAN3d3fT19dHb27vOeLOn9zcedVZvPmUabN3Gg/G8bmZVNmwSLz7fQPmtNyORX2E0D2DGjBnR1dVV9wTZSc00pxw/4nBaqpNO/o218bxuZlXWSE18X+C9kg4nP99A6TViyyRNybXwKcCzrQzUzMzWNWyb+BDPN7gBODGPdiJwfcuiNDOzukZzx+Zc4BBJj5Fewjt3mPHNzGyMjehmn4joBXrz5+Wkp3eZmVlJ/OwUM7MKcxI3M6swJ3EzswpzEjczq7Cy3+xjA/gZMmY2Eq6Jm5lVmJO4mVmFOYmbmVWYk7iZWYU5iZuZVZiTuJlZhTmJm5lVmJO4mVmFOYmbmVWYk7iZWYU5iZuZVZiTuJlZhTmJm5lVmJ9iuJ4a6dMSZ0/vZ2ZrQjGzURi2Ji5pmqQ7JS2S9LCkU3P/iZJuk/RY/r9168M1M7OiRppT+oHZEbErsDfwcUm7AXOAOyJiZ+CO3G1mZm00bBKPiKURcX/+vBpYBEwFZgEX59EuBo5sUYxmZjYIRUTjI0s9wHxgd+CpiNiqMOzFiFinSUXSycDJAN3d3Xudd955dHV1rTPvhc+sHGnsTJ+65YinaaW+vr666zYS7doOI11O96awzcTO2N4HHnjggoiYUXYcZp2g4RObkrqAa4DTImKVpIami4h5wDyAGTNmRFdXFzNnzlxnvJOaeS3Z8evOp0y9vb11120k2rUdRrqc2dP7OWaU62ZmY6+hSwwlvYaUwC+NiGtz72WSpuThU4BnWxOimZkNppGrUwScDyyKiLMLg24ATsyfTwSuH/vwzMxsKI00p+wLnAAslPRA7nc6MBe4UtKHgaeAo1sSoZmZDWrYJB4RdwGDNYAfNLbhmJnZSPi2ezOzCnMSNzOrMD87xVpupM9pAVg8990tiMRs/HFN3MyswpzEzcwqzEnczKzCnMTNzCrMSdzMrMKcxM3MKsyXGFrDmrlU0MxayzVxM7MKq3RNvJNvInGt1czaodJJvF0aTcizp/c39VIHM7NmOYmPA671m62/3CZuZlZhTuJmZhXmJG5mVmFO4mZmFeYkbmZWYaNK4pIOlfSopMclzRmroMzMrDFNX2IoaUPga8AhwNPAvZJuiIifjVVwreDL8cxsPBlNTfztwOMR8URE/Ba4HJg1NmGZmVkjFBHNTSgdBRwaER/J3ScA74iIUwaMdzJwcu7cBVgOPN90xJ1tEl63dtghIiaXHYRZJxjNHZuq02+dX4SImAfMe2Ui6b6ImDGK5XYsr5uZtdtomlOeBqYVurcHfjW6cMzMbCRGk8TvBXaWtKOkjYFjgRvGJiwzM2tE080pEdEv6RTgFmBD4IKIeLiBSecNP0pled3MrK2aPrFpZmbl8x2bZmYV5iRuZlZhbUvi4/kWfUkXSHpW0kNlxzLWJE2TdKekRZIelnRq2TGZ2ava0iaeb9H/OYVb9IHjOv0W/UZJOgDoA/4zInYvO56xJGkKMCUi7pe0ObAAOHK8fHdmVdeumvi4vkU/IuYDL5QdRytExNKIuD9/Xg0sAqaWG5WZ1bQriU8FlhS6n8aJoHIk9QB7AveUHIqZZe1K4g3dom+dS1IXcA1wWkSsKjseM0valcR9i36FSXoNKYFfGhHXlh2Pmb2qXUnct+hXlCQB5wOLIuLssuMxs7W1JYlHRD9Qu0V/EXBlg7foV4Kky4AfArtIelrSh8uOaQztC5wA/LGkB/Lf4WUHZWaJb7s3M6sw37FpZlZhTuJmZhXmJG5mVmFO4mZmFeYkbmZWYU7iZmYV5iRuZlZh/x8aYcCO0dAQ0QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 6 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "data1.hist()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "13c709ba",
      "metadata": {
        "id": "13c709ba",
        "outputId": "4d10a71c-5ba7-41e3-c432-76f17d4dc435"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1152x576 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, axes = plt.subplots(2, 2, figsize=(16, 8))\n",
        "\n",
        "axes[0,0].set_title(\"Distribution of First Column\")\n",
        "axes[0,0].hist(data1[\"SepalLengthCm\"]);\n",
        "\n",
        "axes[0,1].set_title(\"Distribution of Second Column\")\n",
        "axes[0,1].hist(data1[\"SepalWidthCm\"]);\n",
        "\n",
        "axes[1,0].set_title(\"Distribution of Third Column\")\n",
        "axes[1,0].hist(data1[\"PetalLengthCm\"]);\n",
        "\n",
        "axes[1,1].set_title(\"Distribution of Fourth Column\")\n",
        "axes[1,1].hist(data1[\"PetalWidthCm\"]);"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "76f2cd80",
      "metadata": {
        "id": "76f2cd80",
        "outputId": "19866bc3-1839-47c6-c2f4-c7c2a1873678"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:>"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "data1.boxplot()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "31e8df5a",
      "metadata": {
        "id": "31e8df5a",
        "outputId": "660c0ac8-e661-49d5-da7e-7d30dc227e1a"
      },
      "outputs": [
        {
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAArgAAAHRCAYAAACB08ogAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAY2klEQVR4nO3dfWxddf3A8c9hZd0DwroBiQkB4UdILqkJ0X/UQHABAf8ABYfsIUVDNWZZeDAEEarLFMskxEQkGUMy/yGkYBTJjAlGsVHBhBDjyBqPD+DU8BACWxMypLUb9/eHv9Xfsq23207vOf3s9UpuQtfr8ZOz7+7e/e7ce4p2u90OAABI4qS6BwAAgCoJXAAAUhG4AACkInABAEhF4AIAkIrABQAglZ6qD7hjx47o7e2t+rAnrMnJSeeTRrI2aSprkyazPqszOTkZF1100WG/V3ng9vb2RqvVqvqwJ6yyLJ1PGsnapKmsTZrM+qxOWZZH/J5LFAAASEXgAgCQisAFACAVgQsAQCoCFwCAVAQuAACpCFwAAFIRuAAApCJwAQBIpeOdzKampuKrX/1qvPrqq3HSSSfFPffcE//zP//TjdkAAOCoddzB/fWvfx379u2Lxx9/PDZs2BDf/e53uzAWAAAcm46Be+6558b+/fvjvffei71790ZPT8dNXwAAqE3HWl2yZEm8+uqr8clPfjLGx8dj69at3ZgLAACOSdFut9szPWHz5s2xcOHCuP322+P111+Pz33uc/HTn/40ent7D/v8HTt2HPF7HL2JiYlYtGhR3WPAIaxNmsrapMmsz2q1Wq3D/nrHHdxTTz01Tj755IiIOO2002Lfvn2xf//+Iz6/t7f3iP9nHL2yLJ1PGsnapKmsTZrM+qxOWZZH/F7HwP385z8fd999d6xduzampqbiy1/+cixZsqTSAQEAoCodA3fp0qXxwAMPdGMWAAA4bm70AABAKgJ3DvT390dRFJU8LrzwwkqO09/fX/dpAQDoCh9qOwfGxsYqO1ZRFNHhgy4AAPh/7OACAJCKwAUAIBWBCwBAKgIXAIBUBC4AAKkIXAAAUhG4AACkInABAEhF4AIAkIrABQAgFYELAEAqAhcAgFQELgAAqQhcAABSEbgAAKQicAEASEXgAgCQisAFACAVgQsAQCoCFwCAVAQuAACpCFwAAFIRuAAApCJwAQBIReACAJCKwAUAIBWBCwBAKgIXAIBUBC4AAKkIXAAAUhG4AACkInABAEhF4AIAkIrABQAgFYELAEAqAhcAgFQELgAAqQhcAABSEbgAAKQicAEASKWn0xOefPLJ+MlPfhIREZOTk1GWZTz33HNx6qmnzvlwAABwtDoG7nXXXRfXXXddRER84xvfiM985jPiFgCAxpr1JQo7d+6Ml156KW644Ya5nAcAAI5Lxx3cAx5++OHYsGFDx+cduIyB6jifNNHExIS1SSNZmzSZ9dkdswrct99+O/72t7/FRz7ykY7P7e3tjVarddyD8V/OJ01UlqW1SSNZmzSZ9VmdmX5QmNUlCi+88EJ87GMfq2wgAACYK7MK3F27dsVZZ50117MAAMBxm9UlCl/4whfmeg4AAKiEGz0AULv+/v4oiqKSx4UXXljZsfr7++s+NcAxmPWnKADAXBkbG6vsWEVRRLvdrux4wPxjBxcAgFQELgAAqQhcAABSEbgAAKQicAEASEXgAgCQisAFACAVgQsAQCoCFwCAVAQuAACpCFwAAFIRuAAApCJwAQBIReACAJCKwAUAIBWBCwBAKgIXAIBUBC4AAKkIXAAAUhG4AACkInABAEhF4AIAkIrABQAgFYELAEAqPXUP0BTLly+P8fHxusc4rKIo6h7hIH19fbFnz566xwAAOCyB+3/Gx8ej3W7XPcYhyrKMVqtV9xgHaVpwAwD8fy5RAAAgFYELAEAqAhcAgFQELgAAqQhcAABSEbgAAKQicAEASEXgAgCQisAFACAVgQsAQCoCFwCAVAQuAACpCFwAAFIRuAAApCJwAQBIpWc2T3r44YfjV7/6VUxNTcWaNWvi+uuvn+u5AADgmHQM3Oeffz7+8Ic/xMjISLz77rvxgx/8oBtzAQDAMekYuM8++2xccMEFsWHDhti7d2985Stf6cZcAABwTDoG7vj4eLz22muxdevWeOWVV2L9+vXx9NNPR1EUh33+5ORklGVZ+aDd0MS5JyYmGjlXE2eiu5q6NiHCaxTN5bWzOzoG7rJly+K8886LhQsXxnnnnRe9vb2xZ8+eWLFixWGf39vbG61Wq/JBu6GJc5dl2ci5mjgT3dXUtQkRXqNoLq+d1ZnpB4WOn6Lw4Q9/OH77299Gu92ON954I959991YtmxZlfMBAEBlOu7grly5Ml544YVYtWpVtNvt2LhxYyxYsKAbswEAwFGb1ceEeWMZAADzhRs9AACQisAFACAVgQsAQCoCFwCAVAQuAACpzOpTFE4EO9cvjdh0Wt1jHKKJHwW9c/3SukcAADgigft/PvjQO9Fut+se4xBNvOPJB4si2lvqngIA4PBcogAAQCoCFwCAVAQuAACpCFwAAFIRuAAApCJwAQBIReACAJCKwAUAIBWBCwBAKgIXAIBUBC4AAKkIXAAAUhG4AACkInABAEhF4AIAkIrABQAgFYELAEAqAhcAgFQELgAAqQhcAABSEbgAAKQicAEASEXgAgCQisAFACAVgQsAQCoCFwCAVAQuAACpCFwAAFIRuAAApCJwAQBIReACAJCKwAUAIBWBCwBAKgIXAIBUeuoeoEmKoqh7hHmhr6+v7hGAhli+fHmMj4/XPcYhmvh63tfXF3v27Kl7DDghzCpwP/3pT8f73ve+iIg466yzYvPmzXM6VB3a7XbdIxxWURSNnQ1gfHy8ca9RZVlGq9Wqe4xDNDG6IauOgTs5ORkREY8++uicDwMAAMer4zW4f/rTn+Ldd9+Nm266KW688cbYsWNHF8YCAIBj03EHd9GiRTE4OBjXX399/P3vf48vfvGL8fTTT0dPj8t3AQBono6Veu6558Y555wTRVHEueeeG8uWLYs333wz3v/+9x/2+ZOTk1GWZeWDnsicT5poYmLC2iQimvca1eS12dS56J4mr89MOgbuj370o/jLX/4SmzZtijfeeCP27t0bZ5xxxhGf39vb28iL++cz55Mmauobeei+pq2DJq/Nps5F9zR5fc43M/2g0DFwV61aFXfddVesWbMmiqKIe++91+UJAAA0VsdSXbhwYXznO9/pxiwAAHDc3MkMAIBUBC4AAKkIXAAAUhG4AACkInABAEhF4AIAkIrABQAgFYELAEAqAhcAgFQELgAAqQhcAABSEbgAAKQicAEASEXgAgCQisAFACAVgQsAQCoCFwCAVAQuAACpCFwAAFIRuAAApCJwAQBIReACAJCKwAUAIBWBCxyVkZGR6O/vn36MjIzUPRIAHKSn7gGA+WNkZCSGhoZi27ZtsWLFiti9e3cMDg5GRMSaNWtqng4A/sMOLjBrw8PDsW3btli5cmWcfPLJsXLlyti2bVsMDw/XPRoATBO4wKyVZRkXX3zxQb928cUXR1mWNU0EAIcSuMCstVqtePbZZw/6tWeffTZarVZNEwHAoQQuMGtDQ0MxODgYo6OjMTU1FaOjozE4OBhDQ0N1jwYA07zJDJi1A28ku/nmm6Msy2i1WjE8POwNZiewneuXRmw6re4xDtLUf0/YuX5p3SPACaNot9vtKg944C89qlEURVT8WwSV8GediGa+RjV1bTbxXNF9TV2f89FM59IlCnOgv78/iqKo5BERlRynv7+/5rMCANAdLlGYA2NjY5Udy096AABHxw4ucFTcyQyAprODC8yaO5kBMB/YwQVmzZ3MAJgPBC4wa+5kBsB8IHCBWXMnMwDmA9fgArM2NDQUN9xwQyxdujT+8Y9/xDnnnBPvvPNOPPDAA3WPBgDT7OACx+TA5zQDQNMIXGDWhoeH44knnohdu3bF2NhY7Nq1K5544glvMgOgUQQuMGveZAbAfCBwgVnzJjMA5oNZBe7u3bvj0ksvjZdffnmu5wEabGhoKAYHB2N0dDSmpqZidHQ0BgcHY2hoqO7RAGBax09RmJqaio0bN8aiRYu6MQ/QYAfuVnbzzTdHWZbRarVieHjYXcwAaJSOO7j33XdfrF69Os4888xuzAM03Jo1a2JsbGz6IW4BaJoZd3CffPLJWL58eVxyySXx/e9/f1YHnJyc9IaTCk1MTDifVOaaa66Jl156qe4xDnH++efH9u3b6x6DY9S016gmv242dS66p8nrM5Oi3W63j/TNdevWRVEUURRFlGUZH/jAB+Khhx6KM84444gHPPDPllTD+aSpiqKIGV4+OEE0cR009XWzieeK7mvq+pyPZjqXM+7gPvbYY9P/PTAwEJs2bZoxbgEAoG4+JgwAgFQ6forCAY8++uhczgEAAJWwgwsAQCoCFwCAVAQuAACpCFwAAFIRuAAApCJwAQBIReACAJCKwAUAIBWBCwBAKgIXAIBUBC4AAKkIXAAAUhG4AACkInABAEhF4AIAkIrABQAgFYELAEAqAhcAgFQELgAAqQhcAABSEbgAAKQicAEASEXgAgCQisAFACAVgQsAQCoCFwCAVAQuAACpCFwAAFIRuAAApCJwAQBIReACAJBKT90DADNbvnx5jI+P1z3GYRVFUfcIh+jr64s9e/bUPQYANRK40HDj4+PRbrfrHuMQZVlGq9Wqe4xDNDG6AegulygAAJCKwAUAIBWBCwBAKgIXAIBUBC4AAKkIXAAAUhG4AACkInABAEhF4AIAkIrABQAglY636t2/f3987Wtfi127dsWCBQti8+bNcfbZZ3djNgAAOGodd3BHR0cjIuLxxx+PW265JTZv3jznQwEAwLHquIN7+eWXx8c//vGIiHjttdfi9NNPn+uZAADgmHUM3IiInp6euPPOO+MXv/hFfO9735vxuZOTk1GWZSXDETExMeF8nuB2rl8asem0usc4RKvuAY5g5/ql/sx0WdPOd5NfN5s6F93T5PWZSdFut9uzffKbb74Zn/3sZ+NnP/tZLFmy5LDPKcsyWq2m/tU3/zifFEURR/HHtGuaujaber6yauL5tjZpsqauz/lopnPZ8Rrcp556Kh5++OGIiFi8eHEURRELFiyodkIAAKhIx0sUrrjiirjrrrti3bp1sW/fvrj77rujt7e3G7MBAMBR6xi4S5YsiQceeKAbswAAwHFzowcAAFIRuAAApCJwAQBIReACAJCKwAUAIJVZ3ckMqFdRFHWPMG/09fXVPQIANRO40HBNvfORuzIB0FQuUQAAIBWBCwBAKgIXAIBUBC4AAKkIXAAAUhG4AACkInABAEhF4AIAkIrABQAgFYELAEAqAhcAgFQELgAAqQhcAABSEbgAADPo7++PoigqeVx44YWVHau/v7/uU9NYPXUPAADQZGNjY5UdqyiKaLfblR2Pw7ODCwBAKgIXAIBUBC4AAKkIXAAAUhG4AACkInABAEhF4AIAkIrABQAgFYELAEAqAhcAgFQELgAAqQhcAABSEbgAAKQicAEASEXgwgmkv78/iqKo5BERlR2rv7+/5jMDQCY9dQ8AdM/Y2FhlxyrLMlqtVmXHA4Cq2MEFACAVgQsAQCoCFwCAVAQuAACpCFwAAFKZMXCnpqbijjvuiLVr18aqVavimWee6dZcQEONjIxEf3//9GNkZKTukQDgIDN+TNj27dtj2bJlcf/998f4+Hhce+21cdlll3VrNqBhRkZGYmhoKLZt2xYrVqyI3bt3x+DgYERErFmzpubpAOA/ZtzBveqqq+LWW2+d/nrBggVzPhDQXMPDw7Ft27ZYuXJlnHzyybFy5crYtm1bDA8P1z0aAEybcQd36dKlERGxd+/euOWWW+K2227reMDJyckoy7KS4YiYmJhwPmmMsixjxYoVUZbl9No88LV1euJq2u99k183mzoX3WUdzL2OdzJ7/fXXY8OGDbF27dq4+uqrOx6wt7fX3Y0q5G5RNEmr1Yrdu3fHypUrp9fm6OhotFot6/QE1rTf+ya/bjZ1LrrLOqjGTD8ozHiJwltvvRU33XRT3HHHHbFq1arKBwPml6GhoRgcHIzR0dGYmpqK0dHRGBwcjKGhobpHA4BpM+7gbt26Nd5+++3YsmVLbNmyJSIiHnnkkVi0aFFXhgOa5cAbyW6++ebpXbLh4WFvMAOgUYp2u92u8oBN/qeh+cj5pKmsTSIiiqKIiv8aOW5NXZtNPFd0n3VQnZn+rLvRAwAAqQhcAABSEbgAAKQicAEASEXgAgCQSscbPQAAzDfLly+P8fHxusc4rKIo6h7hIH19fbFnz566x6iUwAUA0hkfH2/kx3E18WPsmhbcVXCJAgAAqQhcAABSEbgAAKQicAEASEXgAgCQisAFACAVgQsAQCoCFwCAVNzoAYDjkvFD4udCX19f3SPACUPgAnDMmninqKIoGjkX0D0uUQAAIBWBCwBAKgIXAIBUBC4AAKkIXAAAUhG4AACkInABAEhF4AIAkIrABQAgFYELAEAqAhcAgFQELgAAqQhcAABS6al7AACAqu1cvzRi02l1j3GIVt0DHMbO9UvrHqFyAhcASOeDD70T7Xa77jEOUZZltFrNytwPFkW0t9Q9RbVcogAAQCoCFwCAVAQuAACpCFwAAFIRuAAApCJwAQBIReACAJCKwAUAIBWBCwBAKgIXAIBUZhW4L774YgwMDMz1LAAAcNx6Oj3hkUceie3bt8fixYu7MQ8AAByXjju4Z599djz44IPdmAUAAI5bxx3cK6+8Ml555ZVZH3BycjLKsjyuofiviYkJ55NGsjap0jXXXBMvvfRSZccriqKS45x//vmxffv2So5F9zXxNaqpr51NnOl4dAzco9Xb2xutVqvqw56wyrJ0Pmkka5Mq/fWvf63sWNYmBzRxHTR1fTZxpk5minKfogAAQCoCFwCAVGYVuGeddVb88Ic/nOtZAADguNnBBQAgFYELAEAqAhcAgFQELgAAqQhcAABSEbgAAKQicAEASEXgAgCQisAFACAVgQsAQCoCFwCAVAQuAACpCFwAAFIRuAAApCJwAQBIReACAJCKwAUAIBWBCwBAKgIXAIBUBC4AAKkIXAAAUhG4AACkInABAEhF4AIAkIrABQAgFYELAEAqAhcAgFQELgAAqQhcAABSEbgAAKQicAEASKWn7gEAAOZCURR1jzAv9PX11T1C5QQuAJBOu92ue4TDKoqisbNl4hIFAABSEbgAAKQicAEASEXgAgCQisAFACAVgQsAQCoCFwCAVAQuAACpCFwAgBn09/dHURSVPCKismP19/fXfGaay53MAABmMDY2VtmxyrKMVqtV2fE4vI6B+95778WmTZviz3/+cyxcuDC+9a1vxTnnnNON2QAA4Kh1vEThl7/8Zfz73/+OJ554Im6//fb49re/3Y25AADgmHQM3N///vdxySWXRETERRddVOk2PQAAVK3jJQp79+6NU045ZfrrBQsWxL59+6Kn5/D/08nJySjLsroJT3ATExPOJ41kbdJU1iZNZn12R8fAPeWUU+Kdd96Z/vq99947YtxGRPT29rp4ukIuRqeprE2aytqkyazP6sz0g0LHSxQ+9KEPxW9+85uIiNixY0dccMEF1U0GAAAV67iD+4lPfCKee+65WL16dbTb7bj33nu7MRcAAByTjoF70kknxTe/+c1uzAIAAMfNncwAAEhF4AIAkIrABQAgFYELAEAqAhcAgFQELgAAqQhcAABSEbgAAKQicAEASEXgAgCQStFut9tVHnDHjh3R29tb5SEBAOAgk5OTcdFFFx32e5UHLgAA1MklCgAApCJwAQBIReACAJCKwAUAIBWBCwBAKgK3wV588cUYGBioewyYNjU1FXfccUesXbs2Vq1aFc8880zdI8G0/fv3x1133RWrV6+OdevWxT//+c+6R4KD7N69Oy699NJ4+eWX6x4lvZ66B+DwHnnkkdi+fXssXry47lFg2vbt22PZsmVx//33x/j4eFx77bVx2WWX1T0WRETE6OhoREQ8/vjj8fzzz8fmzZvjoYceqnkq+I+pqanYuHFjLFq0qO5RTgh2cBvq7LPPjgcffLDuMeAgV111Vdx6663TXy9YsKDGaeBgl19+edxzzz0REfHaa6/F6aefXvNE8F/33XdfrF69Os4888y6RzkhCNyGuvLKK6OnxwY7zbJ06dI45ZRTYu/evXHLLbfEbbfdVvdIcJCenp64884745577okrr7yy7nEgIiKefPLJWL58eVxyySV1j3LCELjAUXn99dfjxhtvjE996lNx9dVX1z0OHOK+++6Ln//85/H1r389/vWvf9U9DsSPf/zj+N3vfhcDAwNRlmXceeed8eabb9Y9Vmq2CIFZe+utt+Kmm26KjRs3xkc/+tG6x4GDPPXUU/HGG2/El770pVi8eHEUReEyGhrhsccem/7vgYGB2LRpU5xxxhk1TpSfHVxg1rZu3Rpvv/12bNmyJQYGBmJgYCAmJibqHgsiIuKKK66IP/7xj7Fu3boYHByMu+++O3p7e+seC6hB0W6323UPAQAAVbGDCwBAKgIXAIBUBC4AAKkIXAAAUhG4AACkInABAEhF4AIAkIrABQAglf8Flmq5qUBU3uwAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "data_to_plot = [data1[\"SepalLengthCm\"],data1[\"SepalWidthCm\"],data1[\"PetalLengthCm\"],data1[\"PetalWidthCm\"]]\n",
        "# Creating a figure instance\n",
        "fig = plt.figure(1, figsize=(12,8))\n",
        "# Creating an axes instance\n",
        "ax = fig.add_subplot(111)\n",
        "# Creating the boxplot\n",
        "bp = ax.boxplot(data_to_plot);"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "092ea085",
      "metadata": {
        "id": "092ea085",
        "outputId": "3a9b34ee-5cd6-4deb-e4c4-0b69d9ea8095"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:xlabel='SepalWidthCm'>"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.boxplot(data1['SepalWidthCm'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4fee0ba6",
      "metadata": {
        "id": "4fee0ba6",
        "outputId": "6760b97b-4ccb-4699-f407-68f37c9f623b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(array([15, 32, 33], dtype=int64),)\n"
          ]
        }
      ],
      "source": [
        "print(np.where(data1['SepalWidthCm']>4.0))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e23f2da8",
      "metadata": {
        "id": "e23f2da8"
      },
      "outputs": [],
      "source": [
        ""
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.7"
    },
    "colab": {
      "name": " task_1_Iris Flowers Classification ML Project.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}