Task-1 Iris Flower Classification ML Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9f390fed",
   "metadata": {},
   "source": [
    "# LetsGrowMoreVIP - Data Science Internship\n",
    "Beginner Level Task-1 - Iris Flowers Classification ML Project\\\n",
    "\n",
    "Completed by - Siddharth Maheshwar Wagh\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "edb6d44d",
   "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": "0dc04f77",
   "metadata": {},
   "source": [
    "# Loading and labelling the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "eb948e40",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</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>setosa</td>\n",
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       "    <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>setosa</td>\n",
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       "    <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>setosa</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
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       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <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>virginica</td>\n",
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       "    <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>virginica</td>\n",
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       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
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       "    <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>virginica</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>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width    species\n",
       "0             5.1          3.5           1.4          0.2     setosa\n",
       "1             4.9          3.0           1.4          0.2     setosa\n",
       "2             4.7          3.2           1.3          0.2     setosa\n",
       "3             4.6          3.1           1.5          0.2     setosa\n",
       "4             5.0          3.6           1.4          0.2     setosa\n",
       "..            ...          ...           ...          ...        ...\n",
       "145           6.7          3.0           5.2          2.3  virginica\n",
       "146           6.3          2.5           5.0          1.9  virginica\n",
       "147           6.5          3.0           5.2          2.0  virginica\n",
       "148           6.2          3.4           5.4          2.3  virginica\n",
       "149           5.9          3.0           5.1          1.8  virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris=sns.load_dataset('iris')\n",
    "iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "0090a93c",
   "metadata": {},
   "outputs": [
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       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
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       "      <td>5.1</td>\n",
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       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
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       "    <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>setosa</td>\n",
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       "      <th>3</th>\n",
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       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
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      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species\n",
       "0           5.1          3.5           1.4          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2  setosa\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2  setosa"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "c6b4f600",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
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       "      <td>virginica</td>\n",
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       "      <td>6.3</td>\n",
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       "      <td>virginica</td>\n",
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       "      <td>6.5</td>\n",
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       "      <td>virginica</td>\n",
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       "     sepal_length  sepal_width  petal_length  petal_width    species\n",
       "145           6.7          3.0           5.2          2.3  virginica\n",
       "146           6.3          2.5           5.0          1.9  virginica\n",
       "147           6.5          3.0           5.2          2.0  virginica\n",
       "148           6.2          3.4           5.4          2.3  virginica\n",
       "149           5.9          3.0           5.1          1.8  virginica"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d55d2c4",
   "metadata": {},
   "source": [
    "# Retrieving Information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "7a553464",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 5 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   sepal_length  150 non-null    float64\n",
      " 1   sepal_width   150 non-null    float64\n",
      " 2   petal_length  150 non-null    float64\n",
      " 3   petal_width   150 non-null    float64\n",
      " 4   species       150 non-null    object \n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 6.0+ KB\n"
     ]
    }
   ],
   "source": [
    "iris.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "b4911256",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>sepal_length</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.057333</td>\n",
       "      <td>3.758000</td>\n",
       "      <td>1.199333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.435866</td>\n",
       "      <td>1.765298</td>\n",
       "      <td>0.762238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "       sepal_length  sepal_width  petal_length  petal_width\n",
       "count    150.000000   150.000000    150.000000   150.000000\n",
       "mean       5.843333     3.057333      3.758000     1.199333\n",
       "std        0.828066     0.435866      1.765298     0.762238\n",
       "min        4.300000     2.000000      1.000000     0.100000\n",
       "25%        5.100000     2.800000      1.600000     0.300000\n",
       "50%        5.800000     3.000000      4.350000     1.300000\n",
       "75%        6.400000     3.300000      5.100000     1.800000\n",
       "max        7.900000     4.400000      6.900000     2.500000"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "iris.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "f507f899",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sepal_length    0\n",
       "sepal_width     0\n",
       "petal_length    0\n",
       "petal_width     0\n",
       "species         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "f086d794",
   "metadata": {},
   "outputs": [
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       "      <td>5.1</td>\n",
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       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>setosa</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
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       "      <td>setosa</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <th>50</th>\n",
       "      <td>7.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.7</td>\n",
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       "      <td>versicolor</td>\n",
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       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>5.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>6.5</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>7.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.9</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2.2</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width     species\n",
       "0             5.1          3.5           1.4          0.2      setosa\n",
       "1             4.9          3.0           1.4          0.2      setosa\n",
       "2             4.7          3.2           1.3          0.2      setosa\n",
       "3             4.6          3.1           1.5          0.2      setosa\n",
       "4             5.0          3.6           1.4          0.2      setosa\n",
       "50            7.0          3.2           4.7          1.4  versicolor\n",
       "51            6.4          3.2           4.5          1.5  versicolor\n",
       "52            6.9          3.1           4.9          1.5  versicolor\n",
       "53            5.5          2.3           4.0          1.3  versicolor\n",
       "54            6.5          2.8           4.6          1.5  versicolor\n",
       "100           6.3          3.3           6.0          2.5   virginica\n",
       "101           5.8          2.7           5.1          1.9   virginica\n",
       "102           7.1          3.0           5.9          2.1   virginica\n",
       "103           6.3          2.9           5.6          1.8   virginica\n",
       "104           6.5          3.0           5.8          2.2   virginica"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1=iris.groupby('species')\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99662502",
   "metadata": {},
   "source": [
    "# Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "56af4a7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='species', ylabel='sepal_length'>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAAEHCAYAAABLKzaMAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAUPUlEQVR4nO3deZBldX338feHdQBZRFpRdBhxISEuIC0JYlEIJMEl5g+JSKkJMakporgFoeJTj4+YpMpIjNHgAqMRMDG4oVHRBEgiKlGQGUB2TYKIDIwOGBFBxIHv88c5XdPT093Tt+eevj2n36+qW/ee9ffte2Y+ffp3z/2dVBWSpH7abtQFSJK6Y8hLUo8Z8pLUY4a8JPWYIS9JPWbIS1KP7dDlzpMcCHxy0qwDgP9XVe+dbv199tmnVqxY0WVJktQ7a9asubuqxqZb1mnIV9V3gIMBkmwPrAU+N9P6K1asYPXq1V2WJEm9k+T7My1byO6aY4D/qaoZi5EkDddChvwrgAumzkyyMsnqJKvXr1+/gOVIUv8tSMgn2Ql4KfDpqcuqalVVjVfV+NjYtF1KkqR5Wqgz+RcCV1fVDxeoPUkSCxfyJzJNV40kqVudh3ySXYHfBD7bdVuSpE11egklQFU9ADym63YkSZvzG6+S1GOdn8lL0iidfvrprFu3jn333Zczzzxz1OUsOENeUq+tW7eOtWvXjrqMkbG7RpJ6zJCXpB4z5CWpxwx5SeoxP3iVNLDbz37FqEuYsw33/rh9XrdN1b385E8MZT+eyUtSjxnyktRjhrwk9ZghL0k9ZshLUo8Z8pLUY15CKanX9tltu02elxpDXlKvveXIvUZdwkgtzV9tkrREGPKS1GOGvCT1mCEvST1myEtSjxnyktRjnV9CmWQv4CPAM4ACXlNV3+y6XWmYlvrNoLXtWojr5N8H/GtVHZ9kJ2DXBWhTGqqlfjNobbs6DfkkewBHAicBVNVDwENdtilJ2qjrPvkDgPXAuUmuSfKRJLtNXiHJyiSrk6xev359x+VI0tLSdcjvADwH+FBVHQLcD/zZ5BWqalVVjVfV+NjYWMflSNLS0nWf/B3AHVV1ZTv9GaaEvJamfzvxbaMuYSAP3HNP87zunm2q9mMv+ItRl6AR6/RMvqrWAT9IcmA76xjgpi7blCRttBBX17we+Hh7Zc2twB8uQJuSJBYg5KvqWmC863YkSZvzG6+S1GPeNESagz2323mTZ2lbYchLc3Dio39t1CVI82J3jST1mCEvST1myEtSjxnyktRjhrwk9ZghL0k9ZshLUo8Z8pLUY4a8JPWYIS9JPWbIS1KPGfKS1GOGvCT1mCEvST1myEtSjxnyktRjhrwk9Zh3hlogp59+OuvWrWPfffflzDPPHHU5kpaIzkM+yW3AfcDDwIaqGu+6zcVo3bp1rF27dtRlSFpiFupM/gVVdfcCtSVJatknL0k9thBn8gVckqSAc6pq1bB2fNrj3z+sXXXu7qf8BHaGu2/9yTZV91/fdcqoS5C0FRYi5I+oqjuTPBa4NMktVfW1iYVJVgIrAZYvX74A5UjS0tF5d01V3dk+/wj4HHDYlOWrqmq8qsbHxsa6LkeSlpROQz7Jbkl2n3gN/BZwQ5dtSpI26rq75nHA55JMtPVPVfWvHbe5KO30y902eZakhdBpyFfVrcCzu2xjW/G0248YdQmSliAvoZSkHjPkJanHDHlJ6jFDXpJ6zJCXpB4z5CWpxwx5SeoxQ16SesyQl6QeM+QlqccMeUnqsTmPXZPk6cBpwP6Tt6uqozuoS5I0BIMMUPZp4GzgwzQ35ZYkLXKDhPyGqvpQZ5VIkoZuiyGfZO/25ReTvJbm7k6/mFheVT/uqDZJ0laay5n8GpqbcaedPm3SsgIOGHZRkqTh2GLIV9WTAZIsq6oHJy9LsqyrwiRJW2+QSyi/Mcd5kqRFYi598vsC+wG7JDmEjd02ewC7dlibJGkrzaVP/reBk4AnAu+ZNP8+4P90UJMkaUjm0id/PnB+kpdV1YULUJMkaUgGuU5+/yR/OmXevcCaqrp2eCVJkoZlkA9ex4GTafrn9wNWAkcBH05y+mwbJtk+yTVJLppvoZKkwQ0S8o8BnlNVp1bVqTShPwYcSdNnP5s3AjfPq0JJ0rwNEvLLgYcmTf8S2L+qfs6kb8BOleSJwIuBj8yrQknSvA3SJ/9PwBVJPt9O/w5wQZLdgJtm2e69wOnA7tMtTLKSpuuH5cuXD1COJGlL5nwmX1V/QRPGP6H5wPXkqvrzqrq/ql453TZJXgL8qKrWzLLfVVU1XlXjY2Njg1UvSZrVIGfyANcAd05sl2R5Vd0+y/pHAC9N8iJgGbBHkn+sqlfNq1pJ0kAGuWnI64G3Az+kGU8+NAOUPWumbarqrcBb2+2PAt5iwEvSwhnkTP6NwIFVdU9XxUiShmuQkP8BTV/8vFTVZcBl891ekjS4QUL+VuCyJF9i05uGvGfmTSRJozRIyN/ePnZqH5KkRW7OIV9V7wBIsltV3d9dSZKkYZnzdfJJDk9yE+3wBEmeneSDnVUmSdpqgwxr8F6aseXvAaiqb9OMWyNJWqQGCXmq6gdTZj08xFokSUM20CWUSZ4HVJKdgDfgyJKStKgNciZ/MvA6mrHk7wAObqclSYvUIFfX3A1MOxCZJGlx2mLIJzmLZoyaaVXVG4ZakSRpaOZyJr+68yokSZ3YYshX1flz2VGSs6rq9VtfkiRpWAa6hHILjhjiviRJQzDMkJckLTKGvCT12DBDPkPclyRpCIYZ8u8b4r4kSUMwl+vkv8js18m/tH0+b3hlSZKGYS7Xyb+78yokSZ2Yy3XyX12IQiRJwzfnsWuSPA14J3AQsGxiflUd0EFdkqQhGOSD13OBDwEbgBcAHwP+oYuiJEnDMUjI71JV/w6kqr5fVWcAR8+2QZJlSb6V5NtJbkzyjq0pVpI0mEFuGvJgku2A/0pyCrAWeOwWtvkFcHRV/SzJjsDlSf6lqq6YZ72SpAEMcib/JmBXmjtCHQq8GviD2Taoxs/ayR3bx4yXY0qShmuQm4ZcBdCezb+hqu6by3ZJtgfWAE8FPlBVV05ZvhJYCbB8+fK5liNJmoM5n8knGU9yPXAdcH3bz37olrarqoer6mDgicBhSZ4xZfmqqhqvqvGxsbEBy5ckzWaQ7pqPAq+tqhVVtYLm/q7nznXjqvoJcBlw3ABtSpK2wiAhf19VfX1ioqouB2btskkylmSv9vUuwLHALfOoU5I0D4NcXfOtJOcAF9B8eHoCcFmS5wBU1dXTbPN44Py2X3474FNVddFW1ixJmqNBQv7g9vntU+Y/jyb0N7tmvqquAw6ZV2WSpK02yNU1L+iyEEnS8A1ydc3jkvx9kn9ppw9K8kfdlSZJ2lqDfPB6HnAx8IR2+rs0X5CSJC1Sg4T8PlX1KeARgKraADzcSVWSpKEYJOTvT/IY2mEJkvwGcG8nVUmShmKQq2v+FPgC8JQk/wmMAcd3UpUkaSgGOZN/CvBCmksmLwb+i8F+SUiSFtggIf+2qvop8Giab66uormJiCRpkRok5Cc+ZH0xcHZVfR7YafglSZKGZZCQX9sOa/By4MtJdh5we0nSAhskpF9O0xd/XDui5N7AaV0UJUkajkGGNXgA+Oyk6buAu7ooSpI0HHa3SFKPGfKS1GOGvCT1mCEvST1myEtSjxnyktRjhrwk9ZghL0k9ZshLUo91GvJJnpTkK0luTnJjkjd22Z4kaVNdjwe/ATi1qq5OsjuwJsmlVXVTx+1Kkuj4TL6q7qqqq9vX9wE3A/t12aYkaaMF65NPsgI4BLhyodqUpKVuQUI+yaOAC4E3tXeXmrxsZZLVSVavX79+IcqRpCWj85BPsiNNwH+8qj47dXlVraqq8aoaHxsb67ocSVpSur66JsDfAzdX1Xu6bEuStLmuz+SPAF4NHJ3k2vbxoo7blCS1Or2EsqouB9JlG5KkmfmNV0nqMUNeknrMkJekHjPkJanHDHlJ6jFDXpJ6zJCXpB4z5CWpxwx5SeoxQ16SesyQl6QeM+QlqccMeUnqMUNeknrMkJekHjPkJanHDHlJ6jFDXpJ6zJCXpB4z5CWpxwx5SeoxQ16SeqzTkE/y0SQ/SnJDl+1IkqbX9Zn8ecBxHbchSZpBpyFfVV8DftxlG5KkmY28Tz7JyiSrk6xev379qMuRpF4ZechX1aqqGq+q8bGxsVGXI0m9MvKQlyR1x5CXpB7r+hLKC4BvAgcmuSPJH3XZniRpUzt0ufOqOrHL/UuSZmd3jST1mCEvST1myEtSjxnyktRjhrwk9ZghL0k9ZshLUo8Z8pLUY4a8JPWYIS9JPWbIS1KPGfKS1GOGvCT1mCEvST1myEtSjxnyktRjhrwk9ZghL0k9ZshLUo8Z8pLUY4a8JPWYIS9JPdZ5yCc5Lsl3kvx3kj/ruj1J0kadhnyS7YEPAC8EDgJOTHJQl21Kkjbq+kz+MOC/q+rWqnoI+ATwux23KUlqpaq623lyPHBcVf1xO/1q4Ner6pRJ66wEVraTBwLf6ayg0dsHuHvURWjePH7brr4fu/2ramy6BTt03HCmmbfJb5WqWgWs6riORSHJ6qoaH3Udmh+P37ZrKR+7rrtr7gCeNGn6icCdHbcpSWp1HfJXAU9L8uQkOwGvAL7QcZuSpFan3TVVtSHJKcDFwPbAR6vqxi7bXOSWRLdUj3n8tl1L9th1+sGrJGm0/MarJPWYIS9JPWbIdyjJSUmeMOo6NH9J/jzJsfPY7qgkF3VR01KU5AlJPjOP7b6cZK8trDOvY7ytsE++Q0kuA95SVatHXYtmliQ0/xceGeI+j6I59i+Z4/o7VNWGYbW/VPi+bZln8gNKsluSLyX5dpIbkpyQ5NAkX02yJsnFSR7fftt3HPh4kmuT7JLkmCTXJLk+yUeT7Nzu86+S3JTkuiTvbuf9TpIr2/X/LcnjRvlzbwuSvCvJaydNn5Hk1CSnJbmqfX/f0S5bkeTmJB8ErgaelOS89phen+TN7XrntceSJM9N8o322H8rye5JliU5t93mmiQvmKauvZP8c9v+FUmeNam+VUkuAT62AG/RNmGW43hDO31Skk8n+SJwSZJdk3yqfX8/2f6/GW/XvS3JPpOO94eT3JjkkiS7tOts6RivSPL1JFe3j+eN4G2Zv6ryMcADeBnw4UnTewLfAMba6RNoLhUFuAwYb18vA34APL2d/hjwJmBvmqEcJv6q2qt9fvSkeX8M/M2of/bF/gAOAb46afom4PdpLp8LzUnNRcCRwArgEeA32nUPBS6dtO3EcTgPOB7YCbgVeG47fw+aS5BPBc5t5/0KcHt7rI8CLmrnnwW8vX19NHBt+/oMYA2wy6jfu8X0mOE4Hgnc0E6fRPNFy73b6bcA57SvnwFsmPT/7jaaIQ1WtPMPbud/CnjVHI/xrsCydt7TgNWjfo8GeXQ9rEEfXQ+8O8m7aALjf2n+YV3a/NXP9sBd02x3IPC9qvpuO30+8Drg/cCDwEeSfKndJzTfDv5kksfT/OP7Xjc/Tn9U1TVJHtt+DjJGc2yeBfwWcE272qNo/qPeDny/qq5o598KHJDkLOBLwCVTdn8gcFdVXdW29VOAJM+nCXGq6pYk3weePmXb59OcHFBV/5HkMUn2bJd9oap+vvU/fX/McBxvn7LapVX14/b184H3tdvekOS6GXb9vaq6tn29hib4J5vpGO8GvD/JwcDDbH58FzVDfkBV9d0khwIvAt4JXArcWFWHb2HT6cbxoZovjB0GHEPzjeBTaM72zgLeU1VfaPt3zxjKD9B/n6E5K9uXZtTTFcA7q+qcySslWQHcPzFdVf+b5NnAb9P88n058JrJmzBl3KVJ87dktjGc7p9mmTY/jlNNft/mcgwAfjHp9cPALlOWz3SM3wz8EHg2zV+DD86xvUXBPvkBtWcXD1TVPwLvBn4dGEtyeLt8xyS/1q5+H7B7+/oWYEWSp7bTrwa+muRRwJ5V9WWa7puD2+V7Amvb13/Q3U/UO5+g+WV5PE1QXAy8pn2fSbJfksdO3SjJPsB2VXUh8DbgOVNWuQV4QpLntuvvnmQH4GvAK9t5TweWs/lIqpPXOQq4e+IsUTOaehxncznNL2XS3K/imfNsc6ZjvCfNGf4jNP9vt5/n/kfCM/nBPRP46ySPAL8E/oSmr+/v2j/BdwDeC9xI09d3dpKfA4cDfwh8uv2HcxVwNk2f/OeTLKM5k3hz284Z7bprgSuAJy/ED7etq6obk+wOrK2qu4C7kvwq8M22O+1nwKtozuQm2w84N8nEic9bp+z3oSQnAGe1H9j9HDgW+CDNMb6e5t/BSVX1i7atCWe0+74OeAB/aW/R1OPY/uU1kw8C57fv7zXAdcC982hztmN8YZLfA77CNvbXl5dQStqmpbkD3Y5V9WCSpwD/TnOBw0MjLm1R8Exe0rZuV+ArSXak+Wv4Twz4jTyTl6Qe84NXSeoxQ16SesyQl6QeM+SlrZA5jHIojZIfvEpSj3kmr97L9COH3taOdvit9vHUdt2xJBemGbXyqiRHtPMflY2jTV6X5GXt/Nvab8uS5FXtvq5Nck6S7dvHZqNbSgvF6+S1FBwH3FlVLwZov5n8LuCnVXVYkt+n+ZbyS2gGuvrbqro8yXKaYRF+lWaog3ur6pntPh49uYH2W7UnAEdU1S/TDGH8SppvPu9XVc9o19ur6x9WmsyQ11KwycihVfX1dtiBC9rlFwB/274+Fjho0rAEe7Rfrz+WZiwVoBnQbEobx9AMV3xVu+0uwI+ALzL76JZSpwx59d7UkUPT3KQDNh1xcOL1dsDhU4f/TZPcs32AFeD8qnrrZgtmH91S6pR98uq9aUYOnRhh8oRJz99sX19CM9zzxLYHzzB/k+4amvFSjp8Y4TLN3aD2n8PollKnPJPXUjDdyKGfAXZOciXNyc6J7bpvAD7Qjmg4MZTwycBftvNvoBnB8h3AZycaqKqbkvxfmtvRbde28zqakQxnHN1S6pqXUGpJSnIbzS3i7h51LVKX7K6RpB7zTF6SeswzeUnqMUNeknrMkJekHjPkJanHDHlJ6rH/D4wAg5Y5eAHxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x ='species', y ='sepal_length', data = iris,palette ='plasma')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "99499fbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='species', ylabel='petal_width'>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "plt.subplot(2,2,1)\n",
    "sns.barplot(x = 'species', y = 'sepal_length', data = ndata)\n",
    "plt.subplot(2,2,2)\n",
    "sns.barplot(x = 'species', y = 'sepal_width', data = ndata)\n",
    "plt.subplot(2,2,3)\n",
    "sns.barplot(x = 'species', y = 'petal_length', data = ndata)\n",
    "plt.subplot(2,2,4)\n",
    "sns.barplot(x = 'species', y = 'petal_width', data = ndata)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c26d9b65",
   "metadata": {},
   "source": [
    "# Categorical to Numerical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "4ab5816d",
   "metadata": {},
   "outputs": [],
   "source": [
    "iris['species'].replace(['setosa', 'versicolor','virginica'],[0, 1,2], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17e172a5",
   "metadata": {},
   "source": [
    "# Training and testing dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "a752175f",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "X = iris.drop(columns=\"species\")\n",
    "Y = iris[\"species\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "1790ff34",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, random_state = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36bfbf86",
   "metadata": {},
   "source": [
    "# Linear Regression on dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "07863476",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "df2=LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "618bbf26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.fit(X_train,Y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "9110aad5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.30170769,  0.86686838,  1.16108098, -0.08531273,  1.84900269,\n",
       "        1.42786017,  1.7714971 ,  0.17706667,  0.05737643,  1.97185548,\n",
       "        1.25764165,  0.04731024,  1.8939019 ,  1.28941615,  1.3716362 ,\n",
       "        0.02277807,  1.09757468,  1.45955196, -0.02612087, -0.14011542,\n",
       "        1.29506652,  1.50279788,  1.48564944, -0.05360225,  1.76102557,\n",
       "        1.20428688, -0.16767947, -0.00464195,  1.26764385,  1.66319295])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=df2.predict(X_test)\n",
    "y_pred\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "d0fd1404",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 0.20421913597428973\n",
      "Mean Squared Error: 0.06231447105955291\n",
      "Mean Root Squared Error: 0.2496286663417343\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error,mean_squared_error\n",
    "\n",
    "print('Mean Absolute Error:', mean_absolute_error(Y_test, y_pred))\n",
    "print('Mean Squared Error:', mean_squared_error(Y_test, y_pred))\n",
    "print('Mean Root Squared Error:', np.sqrt(mean_squared_error(Y_test, y_pred)))"
   ]
  }
 ],
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