Task 1 - Iris FLower Classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "12967c09",
   "metadata": {},
   "source": [
    "# LGMVIP Oct-Nov Data Science Internship Beginner level task 1\n",
    "Iris Flower classification ML Project "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d82ddf90",
   "metadata": {},
   "source": [
    "# Steps to build a ML model:\n",
    "1. import dataset\n",
    "2. visualizing the dataset\n",
    "3. Data preparation\n",
    "4. Training the algorithm\n",
    "5. Making Perdiction.\n",
    "6. Model Evolution"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eefe8b97",
   "metadata": {},
   "source": [
    "# Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1ccb4de6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27a42dfd",
   "metadata": {},
   "source": [
    "# 1. Importing Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "29a9d3de",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('E:\\DATA\\Iris.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fbc72d3c",
   "metadata": {
    "scrolled": true
   },
   "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d2ad9cb2",
   "metadata": {},
   "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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</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\n",
       "5   6            5.4           3.9            1.7           0.4  Iris-setosa\n",
       "6   7            4.6           3.4            1.4           0.3  Iris-setosa\n",
       "7   8            5.0           3.4            1.5           0.2  Iris-setosa\n",
       "8   9            4.4           2.9            1.4           0.2  Iris-setosa\n",
       "9  10            4.9           3.1            1.5           0.1  Iris-setosa"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "703eefc9",
   "metadata": {},
   "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>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>"
      ],
      "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  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3c199b10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 6)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6713f33b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id               0\n",
       "SepalLengthCm    0\n",
       "SepalWidthCm     0\n",
       "PetalLengthCm    0\n",
       "PetalWidthCm     0\n",
       "Species          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e4d7d2e3",
   "metadata": {},
   "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "779f70fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=df.groupby('Species')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9b41ed82",
   "metadata": {},
   "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",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>51</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>52</td>\n",
       "      <td>6.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>53</td>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>54</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>55</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>101</td>\n",
       "      <td>6.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>102</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>103</td>\n",
       "      <td>7.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.1</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>104</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.9</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>105</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2.2</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\n",
       "0      1            5.1           3.5            1.4           0.2   \n",
       "1      2            4.9           3.0            1.4           0.2   \n",
       "2      3            4.7           3.2            1.3           0.2   \n",
       "3      4            4.6           3.1            1.5           0.2   \n",
       "4      5            5.0           3.6            1.4           0.2   \n",
       "50    51            7.0           3.2            4.7           1.4   \n",
       "51    52            6.4           3.2            4.5           1.5   \n",
       "52    53            6.9           3.1            4.9           1.5   \n",
       "53    54            5.5           2.3            4.0           1.3   \n",
       "54    55            6.5           2.8            4.6           1.5   \n",
       "100  101            6.3           3.3            6.0           2.5   \n",
       "101  102            5.8           2.7            5.1           1.9   \n",
       "102  103            7.1           3.0            5.9           2.1   \n",
       "103  104            6.3           2.9            5.6           1.8   \n",
       "104  105            6.5           3.0            5.8           2.2   \n",
       "\n",
       "             Species  \n",
       "0        Iris-setosa  \n",
       "1        Iris-setosa  \n",
       "2        Iris-setosa  \n",
       "3        Iris-setosa  \n",
       "4        Iris-setosa  \n",
       "50   Iris-versicolor  \n",
       "51   Iris-versicolor  \n",
       "52   Iris-versicolor  \n",
       "53   Iris-versicolor  \n",
       "54   Iris-versicolor  \n",
       "100   Iris-virginica  \n",
       "101   Iris-virginica  \n",
       "102   Iris-virginica  \n",
       "103   Iris-virginica  \n",
       "104   Iris-virginica  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c445625e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Species'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fd7af3d1",
   "metadata": {},
   "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: 6.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8e428bf",
   "metadata": {},
   "source": [
    "# 2. visualizing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "bf49e6d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x8ab1298>,\n",
       "  <matplotlib.lines.Line2D at 0x8ab1448>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x8ab15f8>,\n",
       "  <matplotlib.lines.Line2D at 0x8ab17a8>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x8ab1118>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x8ab1958>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x8ab1ad8>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD4CAYAAAD8Zh1EAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAANV0lEQVR4nO3dQYyc9X2H8edbTNQahdrE20Axwo2EOFCJxBoZXCREShPVFMHFB0eKUK1KjhFKm16q9ELSnntowJJXFm0q1IRKQTFFlUFU6iEcCtLaGEIDkRwCsTEJA7h2iZGo218PnlTDeHbnnfWs1/7zfKTVvvO+f8/8Tg/Du+/Mm6pCknTp+7XVHkCSNBsGXZIaYdAlqREGXZIaYdAlqRFrVuuFN2zYUJs2bVqtl5ekS9LBgwffqaq5ccdWLeibNm1iYWFhtV5eki5JSd5Y7FinUy5J/jzJfyR5OcljSX595HiSPJTkSJKXkmw+36ElSdOZGPQk1wJ/CvSq6neBy4AdI8u2ATcMfnYBe2c8pyRpgq5/FF0D/EaSNcBa4PjI8XuBR+us54B1Sa6Z4ZySpAkmBr2q3gT+BvgZ8BZwsqqeGVl2LXB06PGxwb6PSLIryUKShX6/v/ypJUnn6HLKZT1n34H/DvDbwBVJvjy6bMw/PedLYqpqX1X1qqo3Nzf2j7SSpGXqcsrlD4CfVlW/qv4b+D7weyNrjgHXDT3eyLmnZSRJK6hL0H8G3JpkbZIAdwKvjKx5ErhvcLXLrZw9LfPWjGeVJC1h4nXoVfV8kseBQ8AZ4AVgX5Ldg+PzwAHgLuAIcBrYuWITS5LGymp9H3qv1ys/WKQL4ez/WK487y2gCyHJwarqjTu2ap8UlS6U5YQ2iYHWJccv55KkRhh0SWqEQZekRhh0SWqEQZekRhh0SWqEQZekRhh0SWqEQZekRhh0SWqEQZekRhh0SWqEQZekRhh0SWqEQZekRhh0SWqEQZekRkwMepIbkxwe+jmV5Gsja+5IcnJozYMrNrEkaawuN4n+MfBZgCSXAW8C+8csfbaq7p7pdJKkzqY95XIn8JOqemMlhpEkLd+0Qd8BPLbIsa1JXkzyVJKbxi1IsivJQpKFfr8/5UtLkpbSOehJPgHcA3xvzOFDwPVVdTPwMPDEuOeoqn1V1auq3tzc3DLGlSQtZpp36NuAQ1X1i9EDVXWqqt4fbB8ALk+yYUYzSpI6mCboX2KR0y1Jrk6SwfaWwfO+e/7jSZK6mniVC0CStcAXgK8M7dsNUFXzwHbg/iRngA+AHVVVsx9XkrSYTkGvqtPAp0b2zQ9t7wH2zHY0SdI0/KSoJDXCoEtSIwy6JDXCoEtSIwy6JDXCoEtSIwy6JDXCoEtSIwy6JDXCoEtSIwy6JDXCoEtSIwy6JDXCoEtSIwy6JDXCoEtSIwy6JDXCoEtSIwy6JDViYtCT3Jjk8NDPqSRfG1mTJA8lOZLkpSSbV2xiSdJYE28SXVU/Bj4LkOQy4E1g/8iybcANg59bgL2D35KkC2TaUy53Aj+pqjdG9t8LPFpnPQesS3LNTCaUJHUybdB3AI+N2X8tcHTo8bHBvo9IsivJQpKFfr8/5UtLkpbSOehJPgHcA3xv3OEx++qcHVX7qqpXVb25ubnuU0qSJprmHfo24FBV/WLMsWPAdUOPNwLHz2cwSdJ0pgn6lxh/ugXgSeC+wdUutwInq+qt855OktTZxKtcAJKsBb4AfGVo326AqpoHDgB3AUeA08DOmU8qSVpSp6BX1WngUyP75oe2C3hgtqNJkqbhJ0UlqREGXZIaYdAlqREGXZIaYdAlqREGXZIaYdAlqREGXZIaYdAlqREGXZIaYdAlqREGXZIaYdAlqREGXZIaYdAlqREGXZIaYdAlqREGXZIa0SnoSdYleTzJq0leSbJ15PgdSU4mOTz4eXBlxpUkLabTPUWBbwFPV9X2JJ8A1o5Z82xV3T270SRJ05gY9CRXArcDfwxQVR8CH67sWJKkaXU55fIZoA98O8kLSR5JcsWYdVuTvJjkqSQ3jXuiJLuSLCRZ6Pf75zO3JGlEl6CvATYDe6vqc8Avga+PrDkEXF9VNwMPA0+Me6Kq2ldVvarqzc3NLX9qSdI5ugT9GHCsqp4fPH6cs4H/f1V1qqreH2wfAC5PsmGmk0qSljQx6FX1c+BokhsHu+4EfjS8JsnVSTLY3jJ43ndnPKskaQldr3L5KvCdwRUurwE7k+wGqKp5YDtwf5IzwAfAjqqqlRhYkjRep6BX1WGgN7J7fuj4HmDP7MaSJE2r6zt06aJw1VVXceLEiQvyWoOziCtm/fr1vPfeeyv6Gvp4Mei6pJw4cYJWzuat9H8w9PHjd7lIUiMMuiQ1wqBLUiMMuiQ1wqBLUiMMuiQ1wqBLUiMMuiQ1wqBLUiMMuiQ1wqBLUiMMuiQ1wqBLUiMMuiQ1wqBLUiMMuiQ1olPQk6xL8niSV5O8kmTryPEkeSjJkSQvJdm8MuNKkhbT9Y5F3wKerqrtgxtFrx05vg24YfBzC7B38FuSdIFMfIee5ErgduDvAKrqw6r6z5Fl9wKP1lnPAeuSXDPrYSVJi+tyyuUzQB/4dpIXkjyS5IqRNdcCR4ceHxvskyRdIF2CvgbYDOytqs8BvwS+PrJm3N1uz7mTb5JdSRaSLPT7/amHlSQtrkvQjwHHqur5wePHORv40TXXDT3eCBwffaKq2ldVvarqzc3NLWdeSdIiJga9qn4OHE1y42DXncCPRpY9Cdw3uNrlVuBkVb0121ElSUvpepXLV4HvDK5weQ3YmWQ3QFXNAweAu4AjwGlg5wrMKklaQqegV9VhoDeye37oeAEPzG4sSdK0/KSoJDXCoEtSIwy6JDXCoEtSIwy6JDXCoEtSI7pehy5dFOobV8I3f3O1x5iJ+saVqz2CGmPQdUnJX53i7MceLn1JqG+u9hRqiadcJKkRBl2SGmHQJakRBl2SGmHQJakRBl2SGmHQJakRBl2SGmHQJakRBl2SGmHQJakRnb7LJcnrwH8B/wOcqareyPE7gH8GfjrY9f2q+uuZTSlJmmiaL+f6fFW9s8TxZ6vq7vMdSJK0PJ5ykaRGdA16Ac8kOZhk1yJrtiZ5MclTSW4atyDJriQLSRb6/f6yBpYkjdf1lMttVXU8yW8B/5rk1ar6wdDxQ8D1VfV+kruAJ4AbRp+kqvYB+wB6vV4bX2otSReJTu/Qq+r44PfbwH5gy8jxU1X1/mD7AHB5kg0znlWStISJQU9yRZJP/mob+CLw8siaq5NksL1l8Lzvzn5cSdJiupxy+TSwf9DrNcB3q+rpJLsBqmoe2A7cn+QM8AGwo1q5T5gkXSImBr2qXgNuHrN/fmh7D7BntqNJkqbhZYuS1AiDLkmNMOiS1AiDLkmNMOiS1AiDLkmNMOiS1AiDLkmNMOiS1AiDLkmNMOiS1AiDLkmNMOiS1AiDLkmNMOiS1AiDLkmN6HqTaOmiMbh71iVv/fr1qz2CGmPQdUm5UHc2THLBXkualU6nXJK8nuSHSQ4nWRhzPEkeSnIkyUtJNs9+VEnSUqZ5h/75qnpnkWPbgBsGP7cAewe/JUkXyKz+KHov8Gid9RywLsk1M3puSVIHXYNewDNJDibZNeb4tcDRocfHBvs+IsmuJAtJFvr9/vTTSpIW1TXot1XVZs6eWnkgye0jx8dddnDOX5Sqal9V9aqqNzc3N+WokqSldAp6VR0f/H4b2A9sGVlyDLhu6PFG4PgsBpQkdTMx6EmuSPLJX20DXwReHln2JHDf4GqXW4GTVfXWzKeVJC2qy1Uunwb2Dz7MsQb4blU9nWQ3QFXNAweAu4AjwGlg58qMK0lazMSgV9VrwM1j9s8PbRfwwGxHkyRNw+9ykaRGGHRJaoRBl6RGGHRJaoRBl6RGGHRJaoRBl6RGGHRJaoRBl6RGGHRJaoRBl6RGGHRJaoRBl6RGGHRJaoRBl6RGGHRJaoRBl6RGGHRJaoRBl6RGdA56ksuSvJDkX8YcuyPJySSHBz8PznZMSdIkE28SPeTPgFeAKxc5/mxV3X3+I0mSlqPTO/QkG4E/Ah5Z2XEkScvV9ZTL3wJ/AfzvEmu2JnkxyVNJbhq3IMmuJAtJFvr9/pSjSpKWMjHoSe4G3q6qg0ssOwRcX1U3Aw8DT4xbVFX7qqpXVb25ubnlzCtJWkSXd+i3AfckeR34J+D3k/zj8IKqOlVV7w+2DwCXJ9kw62ElSYubGPSq+suq2lhVm4AdwL9V1ZeH1yS5OkkG21sGz/vuCswrSVrENFe5fESS3QBVNQ9sB+5Pcgb4ANhRVTWbESVJXWS1utvr9WphYWFVXluaJAm+J9HFKMnBquqNO+YnRSWpEQZdkhph0CWpEQZdkhph0CWpEcu+bFG6VAw+IrHi/86rYrTaDLqaZ2j1ceEpF0lqhEGXpEYYdElqhEGXpEYYdElqhEGXpEYYdElqhEGXpEas2vehJ+kDb6zKi0uTbQDeWe0hpDGur6qxN2VetaBLF7MkC4vdREC6WHnKRZIaYdAlqREGXRpv32oPIE3Lc+iS1AjfoUtSIwy6JDXCoEtDkvx9kreTvLzas0jTMujSR/0D8IerPYS0HAZdGlJVPwDeW+05pOUw6JLUCIMuSY0w6JLUCIMuSY0w6NKQJI8B/w7cmORYkj9Z7ZmkrvzovyQ1wnfoktQIgy5JjTDoktQIgy5JjTDoktQIgy5JjTDoktSI/wNWoEmSwxvGIgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(df['SepalLengthCm'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "48f15864",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x4901928>,\n",
       "  <matplotlib.lines.Line2D at 0x4901f40>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x48cd118>,\n",
       "  <matplotlib.lines.Line2D at 0x48cdb50>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x4901118>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x48cd820>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x49771a8>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 30,
     "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": [
    "plt.boxplot(df['SepalWidthCm'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "82243a52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x8ad2748>,\n",
       "  <matplotlib.lines.Line2D at 0x8ad2988>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x8ad2ad8>,\n",
       "  <matplotlib.lines.Line2D at 0x49d7700>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x8ad2448>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x49d74f0>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x49d7238>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 31,
     "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": [
    "plt.boxplot(df['PetalLengthCm'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "b7ad506c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x8b4b310>,\n",
       "  <matplotlib.lines.Line2D at 0x8b4b4c0>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x8b4b670>,\n",
       "  <matplotlib.lines.Line2D at 0x8b4b7f0>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x8b4b190>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x8b4b970>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x8b4bb20>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 33,
     "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": [
    "plt.boxplot(df['PetalWidthCm'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "7e480c30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18ae7249",
   "metadata": {},
   "source": [
    "# 3. Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "f411b618",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('Id',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "5db77203",
   "metadata": {},
   "outputs": [],
   "source": [
    "sp={'Iris-setosa':1,'Iris-versicolor':2,'Iris-virginica':3}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e9359967",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.Species=[sp[i] for i in df.Species]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "2ddfceb9",
   "metadata": {},
   "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>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>1</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>1</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>1</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>1</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  Species\n",
       "0              5.1           3.5            1.4           0.2        1\n",
       "1              4.9           3.0            1.4           0.2        1\n",
       "2              4.7           3.2            1.3           0.2        1\n",
       "3              4.6           3.1            1.5           0.2        1\n",
       "4              5.0           3.6            1.4           0.2        1\n",
       "..             ...           ...            ...           ...      ...\n",
       "145            6.7           3.0            5.2           2.3        3\n",
       "146            6.3           2.5            5.0           1.9        3\n",
       "147            6.5           3.0            5.2           2.0        3\n",
       "148            6.2           3.4            5.4           2.3        3\n",
       "149            5.9           3.0            5.1           1.8        3\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "8a1cdf2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df.iloc[:,0:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "f33afd5e",
   "metadata": {},
   "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>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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 4 columns</p>\n",
       "</div>"
      ],
      "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\n",
       "..             ...           ...            ...           ...\n",
       "145            6.7           3.0            5.2           2.3\n",
       "146            6.3           2.5            5.0           1.9\n",
       "147            6.5           3.0            5.2           2.0\n",
       "148            6.2           3.4            5.4           2.3\n",
       "149            5.9           3.0            5.1           1.8\n",
       "\n",
       "[150 rows x 4 columns]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "229914a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "y=df.iloc[:,4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "9d37277c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1\n",
       "1      1\n",
       "2      1\n",
       "3      1\n",
       "4      1\n",
       "      ..\n",
       "145    3\n",
       "146    3\n",
       "147    3\n",
       "148    3\n",
       "149    3\n",
       "Name: Species, Length: 150, dtype: int64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "f2e6f4fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.33,random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77bac6e9",
   "metadata": {},
   "source": [
    "# Traning Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "ff58ff3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "6c53df26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "25428a2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9304223675331595"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X,y) #coef of prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "4a656eb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.10974146, -0.04424045,  0.22700138,  0.60989412])"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "28588bf9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.19208399482814"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.intercept_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad2a7c78",
   "metadata": {},
   "source": [
    "# Making Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "1e74464b",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=model.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bfaa0b3",
   "metadata": {},
   "source": [
    "# Model Evolution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "a14480c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error: 0.04\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean squared error: %.2f\" % np.mean((y_pred - y_test) ** 2))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}