Logistic Regression_Classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4ce23060",
   "metadata": {},
   "source": [
    "# Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2f86e58c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e859dc9",
   "metadata": {},
   "source": [
    "# Read Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "91a8396b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"E:\\Tanisha Bangar\\Documents\\Data Science\\Iris1.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45685adb",
   "metadata": {},
   "source": [
    "# Analysis of the Dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8d6328b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dd56e73c",
   "metadata": {},
   "outputs": [
    {
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       "  <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",
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       "  </thead>\n",
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       "    <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",
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       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
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       "      <th>147</th>\n",
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       "      <td>6.5</td>\n",
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       "      <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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "640f7172",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
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       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>5.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>142</td>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>5.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>121</td>\n",
       "      <td>6.9</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>97</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>4.8</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>144</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</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>99</th>\n",
       "      <td>100</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.3</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>67</td>\n",
       "      <td>5.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>42</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>69</td>\n",
       "      <td>6.2</td>\n",
       "      <td>2.2</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>81</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.4</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.1</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>133</th>\n",
       "      <td>134</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\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>24</th>\n",
       "      <td>25</td>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>44</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "      Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\n",
       "27    28            5.2           3.5            1.5           0.2   \n",
       "141  142            6.9           3.1            5.1           2.3   \n",
       "18    19            5.7           3.8            1.7           0.3   \n",
       "120  121            6.9           3.2            5.7           2.3   \n",
       "96    97            5.7           2.9            4.2           1.3   \n",
       "30    31            4.8           3.1            1.6           0.2   \n",
       "143  144            6.8           3.2            5.9           2.3   \n",
       "26    27            5.0           3.4            1.6           0.4   \n",
       "54    55            6.5           2.8            4.6           1.5   \n",
       "99   100            5.7           2.8            4.1           1.3   \n",
       "66    67            5.6           3.0            4.5           1.5   \n",
       "41    42            4.5           2.3            1.3           0.3   \n",
       "68    69            6.2           2.2            4.5           1.5   \n",
       "80    81            5.5           2.4            3.8           1.1   \n",
       "52    53            6.9           3.1            4.9           1.5   \n",
       "133  134            6.3           2.8            5.1           1.5   \n",
       "0      1            5.1           3.5            1.4           0.2   \n",
       "24    25            4.8           3.4            1.9           0.2   \n",
       "43    44            5.0           3.5            1.6           0.6   \n",
       "16    17            5.4           3.9            1.3           0.4   \n",
       "\n",
       "             Species  \n",
       "27       Iris-setosa  \n",
       "141   Iris-virginica  \n",
       "18       Iris-setosa  \n",
       "120   Iris-virginica  \n",
       "96   Iris-versicolor  \n",
       "30       Iris-setosa  \n",
       "143   Iris-virginica  \n",
       "26       Iris-setosa  \n",
       "54   Iris-versicolor  \n",
       "99   Iris-versicolor  \n",
       "66   Iris-versicolor  \n",
       "41       Iris-setosa  \n",
       "68   Iris-versicolor  \n",
       "80   Iris-versicolor  \n",
       "52   Iris-versicolor  \n",
       "133   Iris-virginica  \n",
       "0        Iris-setosa  \n",
       "24       Iris-setosa  \n",
       "43       Iris-setosa  \n",
       "16       Iris-setosa  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "24a50b26",
   "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: 7.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ac66238b",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
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       "      <th>PetalWidthCm</th>\n",
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       "      <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",
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       "    <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>"
      ],
      "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"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "318da777",
   "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5f09d4d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm',\n",
       "       'Species'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "69d20eb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id               150\n",
       "SepalLengthCm     35\n",
       "SepalWidthCm      23\n",
       "PetalLengthCm     43\n",
       "PetalWidthCm      22\n",
       "Species            3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32f5867e",
   "metadata": {},
   "source": [
    "# Label Encoding of Categorical Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "376c159c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le=LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7863afa7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Iris-setosa        50\n",
       "Iris-versicolor    50\n",
       "Iris-virginica     50\n",
       "Name: Species, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Species'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e1204beb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Species']=le.fit_transform(df['Species'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "42337369",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    50\n",
       "1    50\n",
       "2    50\n",
       "Name: Species, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Species'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e1fb5d85",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1=df[df[\"Species\"]!=2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d94aa443",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    50\n",
       "1    50\n",
       "Name: Species, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['Species'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ded5c1a5",
   "metadata": {},
   "source": [
    "# Defining Dependent and Independent variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a3a3cfa4",
   "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>0</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>0</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        0\n",
       "1   2            4.9           3.0            1.4           0.2        0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "30652917",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df1.drop(['Id','Species'],axis=1)\n",
    "y=df1['Species']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f8178d11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((100, 4), (100,), pandas.core.frame.DataFrame, pandas.core.series.Series)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape,y.shape,type(x),type(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f96570e9",
   "metadata": {},
   "source": [
    "# Split x and y into train and test Dataset\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7e25b792",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((75, 4), (25, 4), (75,), (25,))"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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=.25)\n",
    "x_train.shape,x_test.shape,y_train.shape,y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57471c4c",
   "metadata": {},
   "source": [
    "# Import Model / Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "44dea81b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(random_state=0)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression  \n",
    "lrc= LogisticRegression(random_state=0)\n",
    "lrc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "399b0f93",
   "metadata": {},
   "source": [
    "# Train Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b2b9673f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(random_state=0)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lrc.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "514aa650",
   "metadata": {},
   "source": [
    "# Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a2b7165d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0,\n",
       "       0, 0, 1])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=lrc.predict(x_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8564c5d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y_predict[1 0 1 0 1 0 0 0 1 0 1 1 0 0 1 1 0 1 0 0 1 0 0 0 1]y_test[1 0 1 0 1 0 0 0 1 0 1 1 0 0 1 1 0 1 0 0 1 0 0 0 1]\n"
     ]
    }
   ],
   "source": [
    "print(f'y_predict{y_pred}y_test{y_test.values}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97656ccf",
   "metadata": {},
   "source": [
    "# Evalution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a0d5b4d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lrc.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6dab69b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix,accuracy_score,classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "73d59836",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[14,  0],\n",
       "       [ 0, 11]], dtype=int64)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm=confusion_matrix(y_test,y_pred)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "86484e0f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        14\n",
      "           1       1.00      1.00      1.00        11\n",
      "\n",
      "    accuracy                           1.00        25\n",
      "   macro avg       1.00      1.00      1.00        25\n",
      "weighted avg       1.00      1.00      1.00        25\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_pred,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "dfbf016f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(y_pred,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5b888084",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lrc.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "adc88e97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x144 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,2))\n",
    "sns.heatmap(cm,annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "83afe8fc",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'x_train' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[1;32mIn [8]\u001b[0m, in \u001b[0;36m<cell line: 3>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpreprocessing\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m StandardScaler    \n\u001b[0;32m      2\u001b[0m st_x\u001b[38;5;241m=\u001b[39m StandardScaler()    \n\u001b[1;32m----> 3\u001b[0m x_train\u001b[38;5;241m=\u001b[39m st_x\u001b[38;5;241m.\u001b[39mfit_transform(\u001b[43mx_train\u001b[49m)    \n\u001b[0;32m      4\u001b[0m x_test\u001b[38;5;241m=\u001b[39m st_x\u001b[38;5;241m.\u001b[39mtransform(x_test)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'x_train' is not defined"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler    \n",
    "st_x= StandardScaler()    \n",
    "x_train= st_x.fit_transform(x_train)    \n",
    "x_test= st_x.transform(x_test)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5ed34392",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'x_train' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[1;32mIn [7]\u001b[0m, in \u001b[0;36m<cell line: 2>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcolors\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ListedColormap  \n\u001b[1;32m----> 2\u001b[0m x_set, y_set \u001b[38;5;241m=\u001b[39m \u001b[43mx_train\u001b[49m, y_train  \n\u001b[0;32m      3\u001b[0m x1, x2 \u001b[38;5;241m=\u001b[39m nm\u001b[38;5;241m.\u001b[39mmeshgrid(nm\u001b[38;5;241m.\u001b[39marange(start \u001b[38;5;241m=\u001b[39m x_set[:, \u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mmin() \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m, stop \u001b[38;5;241m=\u001b[39m x_set[:, \u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mmax() \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m, step  \u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.01\u001b[39m),  \n\u001b[0;32m      4\u001b[0m nm\u001b[38;5;241m.\u001b[39marange(start \u001b[38;5;241m=\u001b[39m x_set[:, \u001b[38;5;241m1\u001b[39m]\u001b[38;5;241m.\u001b[39mmin() \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m, stop \u001b[38;5;241m=\u001b[39m x_set[:, \u001b[38;5;241m1\u001b[39m]\u001b[38;5;241m.\u001b[39mmax() \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m, step \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.01\u001b[39m))  \n\u001b[0;32m      5\u001b[0m plt\u001b[38;5;241m.\u001b[39mcontourf(x1, x2, classifier\u001b[38;5;241m.\u001b[39mpredict(nm\u001b[38;5;241m.\u001b[39marray([x1\u001b[38;5;241m.\u001b[39mravel(), x2\u001b[38;5;241m.\u001b[39mravel()])\u001b[38;5;241m.\u001b[39mT)\u001b[38;5;241m.\u001b[39mreshape(x1\u001b[38;5;241m.\u001b[39mshape),  \n\u001b[0;32m      6\u001b[0m alpha \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.75\u001b[39m, cmap \u001b[38;5;241m=\u001b[39m ListedColormap((\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpurple\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgreen\u001b[39m\u001b[38;5;124m'\u001b[39m )))  \n",
      "\u001b[1;31mNameError\u001b[0m: name 'x_train' is not defined"
     ]
    }
   ],
   "source": [
    "from matplotlib.colors import ListedColormap  \n",
    "x_set, y_set = x_train, y_train  \n",
    "x1, x2 = nm.meshgrid(nm.arange(start = x_set[:, 0].min() - 1, stop = x_set[:, 0].max() + 1, step  =0.01),  \n",
    "nm.arange(start = x_set[:, 1].min() - 1, stop = x_set[:, 1].max() + 1, step = 0.01))  \n",
    "plt.contourf(x1, x2, classifier.predict(nm.array([x1.ravel(), x2.ravel()]).T).reshape(x1.shape),  \n",
    "alpha = 0.75, cmap = ListedColormap(('purple','green' )))  \n",
    "plt.xlim(x1.min(), x1.max())  \n",
    "plt.ylim(x2.min(), x2.max())  \n",
    "for i, j in enumerate(nm.unique(y_set)):  \n",
    "    mtp.scatter(x_set[y_set == j, 0], x_set[y_set == j, 1],  \n",
    "        c = ListedColormap(('purple', 'green'))(i), label = j)    \n",
    "plt.legend()  \n",
    "plt.show()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f0617490",
   "metadata": {},
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}