iris-flowers-classification.ipynb

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   "source": [
    "# Task 1: Iris Flower Classification\n",
    "# Batch: OIBSP May P1\n",
    "# By Ahan Bhatt"
   ]
  },
  {
   "cell_type": "code",
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   "id": "f4a50617",
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   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "0cea3d63",
   "metadata": {
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   "outputs": [],
   "source": [
    "import numpy as np"
   ]
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   "cell_type": "code",
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   "id": "bbfd4963",
   "metadata": {
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   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
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   "cell_type": "code",
   "execution_count": 120,
   "id": "692b0cdb",
   "metadata": {
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   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
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   "cell_type": "code",
   "execution_count": 121,
   "id": "a3194d6e",
   "metadata": {
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   "source": [
    "# Read the csv file"
   ]
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   "cell_type": "code",
   "execution_count": 122,
   "id": "b895a0a0",
   "metadata": {
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   "outputs": [],
   "source": [
    "iris_df=pd.read_csv('C:/Users/HP/Documents/Indus/Internship/Oasis Infobyte/Tasks/Task 1/Iris.csv')"
   ]
  },
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   "cell_type": "code",
   "execution_count": 12,
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       "  <thead>\n",
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       "      <th></th>\n",
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       "      <th>SepalLengthCm</th>\n",
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       "   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"
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   "source": [
    "iris_df.head()"
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   "source": [
    "# Drop the column Id of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7198ae9d",
   "metadata": {
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   "source": [
    "iris_df=iris_df.drop(['Id'], axis=1)"
   ]
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       "   SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
       "0            5.1           3.5            1.4           0.2  Iris-setosa\n",
       "1            4.9           3.0            1.4           0.2  Iris-setosa\n",
       "2            4.7           3.2            1.3           0.2  Iris-setosa\n",
       "3            4.6           3.1            1.5           0.2  Iris-setosa\n",
       "4            5.0           3.6            1.4           0.2  Iris-setosa"
      ]
     },
     "execution_count": 15,
     "metadata": {},
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   ],
   "source": [
    "iris_df.head()"
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   "execution_count": 16,
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   "source": [
    "# Change the names of the columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "64caed87",
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    {
     "data": {
      "text/plain": [
       "Index(['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm',\n",
       "       'Species'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
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   "source": [
    "iris_df.columns"
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   "execution_count": 18,
   "id": "e0e780ff",
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   "outputs": [],
   "source": [
    "iris_df.columns=['sepal_length','sepal_width','petal_length','petal_width','species']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1139d071",
   "metadata": {
    "execution": {
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   "source": [
    "# Top 5 records of the dataset"
   ]
  },
  {
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   "execution_count": 20,
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       "      <th>petal_length</th>\n",
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       "      <td>Iris-setosa</td>\n",
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       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width      species\n",
       "0           5.1          3.5           1.4          0.2  Iris-setosa\n",
       "1           4.9          3.0           1.4          0.2  Iris-setosa\n",
       "2           4.7          3.2           1.3          0.2  Iris-setosa\n",
       "3           4.6          3.1           1.5          0.2  Iris-setosa\n",
       "4           5.0          3.6           1.4          0.2  Iris-setosa"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7955269e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:34:51.238136Z",
     "iopub.status.busy": "2023-05-27T16:34:51.237161Z",
     "iopub.status.idle": "2023-05-27T16:34:51.241834Z",
     "shell.execute_reply": "2023-05-27T16:34:51.240922Z"
    },
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     "duration": 0.043769,
     "end_time": "2023-05-27T16:34:51.244246",
     "exception": false,
     "start_time": "2023-05-27T16:34:51.200477",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Number of rows and columns in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6652fb99",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:34:51.315187Z",
     "iopub.status.busy": "2023-05-27T16:34:51.314701Z",
     "iopub.status.idle": "2023-05-27T16:34:51.322010Z",
     "shell.execute_reply": "2023-05-27T16:34:51.320803Z"
    },
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     "end_time": "2023-05-27T16:34:51.324921",
     "exception": false,
     "start_time": "2023-05-27T16:34:51.278859",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 5)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ba793f53",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:34:51.398185Z",
     "iopub.status.busy": "2023-05-27T16:34:51.397108Z",
     "iopub.status.idle": "2023-05-27T16:34:51.402558Z",
     "shell.execute_reply": "2023-05-27T16:34:51.401716Z"
    },
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     "end_time": "2023-05-27T16:34:51.405236",
     "exception": false,
     "start_time": "2023-05-27T16:34:51.360483",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Basic informations about the columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d3ce800b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:34:51.478164Z",
     "iopub.status.busy": "2023-05-27T16:34:51.477670Z",
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     "shell.execute_reply": "2023-05-27T16:34:51.501066Z"
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     "exception": false,
     "start_time": "2023-05-27T16:34:51.440583",
     "status": "completed"
    },
    "tags": []
   },
   "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_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ef75d4c0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:34:51.578107Z",
     "iopub.status.busy": "2023-05-27T16:34:51.577143Z",
     "iopub.status.idle": "2023-05-27T16:34:51.582702Z",
     "shell.execute_reply": "2023-05-27T16:34:51.581669Z"
    },
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     "duration": 0.044686,
     "end_time": "2023-05-27T16:34:51.585389",
     "exception": false,
     "start_time": "2023-05-27T16:34:51.540703",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Basic statistics of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e2fdeb54",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:34:51.657164Z",
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     "start_time": "2023-05-27T16:34:51.620289",
     "status": "completed"
    },
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   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\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>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>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",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal_length  sepal_width  petal_length  petal_width\n",
       "count    150.000000   150.000000    150.000000   150.000000\n",
       "mean       5.843333     3.054000      3.758667     1.198667\n",
       "std        0.828066     0.433594      1.764420     0.763161\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": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e2955695",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:34:51.765019Z",
     "iopub.status.busy": "2023-05-27T16:34:51.764480Z",
     "iopub.status.idle": "2023-05-27T16:34:51.770274Z",
     "shell.execute_reply": "2023-05-27T16:34:51.769103Z"
    },
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     "duration": 0.045537,
     "end_time": "2023-05-27T16:34:51.772904",
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     "start_time": "2023-05-27T16:34:51.727367",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Null values in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "8b01e38f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:34:51.846791Z",
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     "status": "completed"
    },
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   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <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>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </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>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </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           False        False         False        False    False\n",
       "1           False        False         False        False    False\n",
       "2           False        False         False        False    False\n",
       "3           False        False         False        False    False\n",
       "4           False        False         False        False    False\n",
       "..            ...          ...           ...          ...      ...\n",
       "145         False        False         False        False    False\n",
       "146         False        False         False        False    False\n",
       "147         False        False         False        False    False\n",
       "148         False        False         False        False    False\n",
       "149         False        False         False        False    False\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ea2f3f67",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:34:51.944495Z",
     "iopub.status.busy": "2023-05-27T16:34:51.944036Z",
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     "shell.execute_reply": "2023-05-27T16:34:51.953739Z"
    },
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     "start_time": "2023-05-27T16:34:51.907151",
     "status": "completed"
    },
    "tags": []
   },
   "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": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "dc156a0d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:34:52.030721Z",
     "iopub.status.busy": "2023-05-27T16:34:52.030203Z",
     "iopub.status.idle": "2023-05-27T16:34:52.035792Z",
     "shell.execute_reply": "2023-05-27T16:34:52.034542Z"
    },
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     "end_time": "2023-05-27T16:34:52.038235",
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     "start_time": "2023-05-27T16:34:51.993433",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Count of species column of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ef3a84ca",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:34:52.113653Z",
     "iopub.status.busy": "2023-05-27T16:34:52.112129Z",
     "iopub.status.idle": "2023-05-27T16:34:52.120983Z",
     "shell.execute_reply": "2023-05-27T16:34:52.119272Z"
    },
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     "start_time": "2023-05-27T16:34:52.075135",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "species_counts=iris_df['species'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c6752aec",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:34:52.199468Z",
     "iopub.status.busy": "2023-05-27T16:34:52.198971Z",
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     "exception": false,
     "start_time": "2023-05-27T16:34:52.161671",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Count of the species column of the dataset : \n",
      "Iris-setosa        50\n",
      "Iris-versicolor    50\n",
      "Iris-virginica     50\n",
      "Name: species, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"Count of the species column of the dataset : \")\n",
    "print(species_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "2eb9de38",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:34:52.284137Z",
     "iopub.status.busy": "2023-05-27T16:34:52.283623Z",
     "iopub.status.idle": "2023-05-27T16:34:52.301302Z",
     "shell.execute_reply": "2023-05-27T16:34:52.300078Z"
    },
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     "end_time": "2023-05-27T16:34:52.304000",
     "exception": false,
     "start_time": "2023-05-27T16:34:52.246621",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "    }\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>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>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width      species\n",
       "0           5.1          3.5           1.4          0.2  Iris-setosa\n",
       "1           4.9          3.0           1.4          0.2  Iris-setosa\n",
       "2           4.7          3.2           1.3          0.2  Iris-setosa\n",
       "3           4.6          3.1           1.5          0.2  Iris-setosa\n",
       "4           5.0          3.6           1.4          0.2  Iris-setosa"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c9743245",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:34:52.380857Z",
     "iopub.status.busy": "2023-05-27T16:34:52.380339Z",
     "iopub.status.idle": "2023-05-27T16:34:52.386426Z",
     "shell.execute_reply": "2023-05-27T16:34:52.384954Z"
    },
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     "duration": 0.049366,
     "end_time": "2023-05-27T16:34:52.389092",
     "exception": false,
     "start_time": "2023-05-27T16:34:52.339726",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Calculate the mean of sepal length for each species"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "70a05a6f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:34:52.465679Z",
     "iopub.status.busy": "2023-05-27T16:34:52.465165Z",
     "iopub.status.idle": "2023-05-27T16:34:52.474466Z",
     "shell.execute_reply": "2023-05-27T16:34:52.473155Z"
    },
    "papermill": {
     "duration": 0.050761,
     "end_time": "2023-05-27T16:34:52.477073",
     "exception": false,
     "start_time": "2023-05-27T16:34:52.426312",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "mean_sepal_length=iris_df.groupby('species')['sepal_length'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ce532811",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:34:52.552340Z",
     "iopub.status.busy": "2023-05-27T16:34:52.551821Z",
     "iopub.status.idle": "2023-05-27T16:34:52.559950Z",
     "shell.execute_reply": "2023-05-27T16:34:52.557860Z"
    },
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     "end_time": "2023-05-27T16:34:52.562660",
     "exception": false,
     "start_time": "2023-05-27T16:34:52.514315",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean of the sepal length of each species : \n",
      "species\n",
      "Iris-setosa        5.006\n",
      "Iris-versicolor    5.936\n",
      "Iris-virginica     6.588\n",
      "Name: sepal_length, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean of the sepal length of each species : \")\n",
    "print(mean_sepal_length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4141ae80",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:34:52.636625Z",
     "iopub.status.busy": "2023-05-27T16:34:52.636103Z",
     "iopub.status.idle": "2023-05-27T16:34:52.641926Z",
     "shell.execute_reply": "2023-05-27T16:34:52.640718Z"
    },
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     "duration": 0.046032,
     "end_time": "2023-05-27T16:34:52.644451",
     "exception": false,
     "start_time": "2023-05-27T16:34:52.598419",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Calculate the median of petal width for each species"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "adf8b1cf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:34:52.719390Z",
     "iopub.status.busy": "2023-05-27T16:34:52.718899Z",
     "iopub.status.idle": "2023-05-27T16:34:52.734935Z",
     "shell.execute_reply": "2023-05-27T16:34:52.733885Z"
    },
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     "start_time": "2023-05-27T16:34:52.680773",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "    }\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>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>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width      species\n",
       "0           5.1          3.5           1.4          0.2  Iris-setosa\n",
       "1           4.9          3.0           1.4          0.2  Iris-setosa\n",
       "2           4.7          3.2           1.3          0.2  Iris-setosa\n",
       "3           4.6          3.1           1.5          0.2  Iris-setosa\n",
       "4           5.0          3.6           1.4          0.2  Iris-setosa"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "57e08839",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:34:52.811405Z",
     "iopub.status.busy": "2023-05-27T16:34:52.810881Z",
     "iopub.status.idle": "2023-05-27T16:34:52.821596Z",
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     "exception": false,
     "start_time": "2023-05-27T16:34:52.773262",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "median_petal_width=iris_df.groupby('species')['petal_width'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "7bbf4fcc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:34:52.899807Z",
     "iopub.status.busy": "2023-05-27T16:34:52.899310Z",
     "iopub.status.idle": "2023-05-27T16:34:52.907459Z",
     "shell.execute_reply": "2023-05-27T16:34:52.906224Z"
    },
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     "exception": false,
     "start_time": "2023-05-27T16:34:52.861480",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Median of the petal width of each species : \n",
      "species\n",
      "Iris-setosa        0.2\n",
      "Iris-versicolor    1.3\n",
      "Iris-virginica     2.0\n",
      "Name: petal_width, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(\"Median of the petal width of each species : \")\n",
    "print(median_petal_width)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "04f1b3af",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:34:52.987816Z",
     "iopub.status.busy": "2023-05-27T16:34:52.986768Z",
     "iopub.status.idle": "2023-05-27T16:34:53.003962Z",
     "shell.execute_reply": "2023-05-27T16:34:53.002589Z"
    },
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     "duration": 0.058685,
     "end_time": "2023-05-27T16:34:53.006493",
     "exception": false,
     "start_time": "2023-05-27T16:34:52.947808",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "    }\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>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>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</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>Iris-setosa</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
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       "    <tr>\n",
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       "      <td>5.0</td>\n",
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       "      <td>1.4</td>\n",
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       "      <td>Iris-setosa</td>\n",
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       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width      species\n",
       "0           5.1          3.5           1.4          0.2  Iris-setosa\n",
       "1           4.9          3.0           1.4          0.2  Iris-setosa\n",
       "2           4.7          3.2           1.3          0.2  Iris-setosa\n",
       "3           4.6          3.1           1.5          0.2  Iris-setosa\n",
       "4           5.0          3.6           1.4          0.2  Iris-setosa"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "6bcab541",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:34:53.082782Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 700x700 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1143x1000 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,7))\n",
    "sns.pairplot(iris_df,hue='species')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "ba4b4bb3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:35:01.207353Z",
     "iopub.status.busy": "2023-05-27T16:35:01.206819Z",
     "iopub.status.idle": "2023-05-27T16:35:01.212699Z",
     "shell.execute_reply": "2023-05-27T16:35:01.211125Z"
    },
    "papermill": {
     "duration": 0.05266,
     "end_time": "2023-05-27T16:35:01.215554",
     "exception": false,
     "start_time": "2023-05-27T16:35:01.162894",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Count plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "a5baaee9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:35:01.303623Z",
     "iopub.status.busy": "2023-05-27T16:35:01.303063Z",
     "iopub.status.idle": "2023-05-27T16:35:01.321207Z",
     "shell.execute_reply": "2023-05-27T16:35:01.319809Z"
    },
    "papermill": {
     "duration": 0.066087,
     "end_time": "2023-05-27T16:35:01.323951",
     "exception": false,
     "start_time": "2023-05-27T16:35:01.257864",
     "status": "completed"
    },
    "tags": []
   },
   "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>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>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width      species\n",
       "0           5.1          3.5           1.4          0.2  Iris-setosa\n",
       "1           4.9          3.0           1.4          0.2  Iris-setosa\n",
       "2           4.7          3.2           1.3          0.2  Iris-setosa\n",
       "3           4.6          3.1           1.5          0.2  Iris-setosa\n",
       "4           5.0          3.6           1.4          0.2  Iris-setosa"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "58459ff6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:35:01.412784Z",
     "iopub.status.busy": "2023-05-27T16:35:01.412285Z",
     "iopub.status.idle": "2023-05-27T16:35:01.424269Z",
     "shell.execute_reply": "2023-05-27T16:35:01.422750Z"
    },
    "papermill": {
     "duration": 0.062037,
     "end_time": "2023-05-27T16:35:01.427621",
     "exception": false,
     "start_time": "2023-05-27T16:35:01.365584",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Iris-setosa        50\n",
       "Iris-versicolor    50\n",
       "Iris-virginica     50\n",
       "Name: species, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df['species'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "d1c9d204",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:35:01.515791Z",
     "iopub.status.busy": "2023-05-27T16:35:01.515314Z",
     "iopub.status.idle": "2023-05-27T16:35:01.772039Z",
     "shell.execute_reply": "2023-05-27T16:35:01.770644Z"
    },
    "papermill": {
     "duration": 0.303226,
     "end_time": "2023-05-27T16:35:01.775042",
     "exception": false,
     "start_time": "2023-05-27T16:35:01.471816",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=iris_df,x='species',ec='black')\n",
    "plt.xlabel(\"Name of the Species\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "3e7f77c5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:35:01.863601Z",
     "iopub.status.busy": "2023-05-27T16:35:01.862493Z",
     "iopub.status.idle": "2023-05-27T16:35:01.867340Z",
     "shell.execute_reply": "2023-05-27T16:35:01.866459Z"
    },
    "papermill": {
     "duration": 0.052405,
     "end_time": "2023-05-27T16:35:01.870593",
     "exception": false,
     "start_time": "2023-05-27T16:35:01.818188",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Bar Plot bw sepal length and species"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "4d8ddba9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:35:01.962054Z",
     "iopub.status.busy": "2023-05-27T16:35:01.961499Z",
     "iopub.status.idle": "2023-05-27T16:35:01.978944Z",
     "shell.execute_reply": "2023-05-27T16:35:01.977442Z"
    },
    "papermill": {
     "duration": 0.066679,
     "end_time": "2023-05-27T16:35:01.981759",
     "exception": false,
     "start_time": "2023-05-27T16:35:01.915080",
     "status": "completed"
    },
    "tags": []
   },
   "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>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>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width      species\n",
       "0           5.1          3.5           1.4          0.2  Iris-setosa\n",
       "1           4.9          3.0           1.4          0.2  Iris-setosa\n",
       "2           4.7          3.2           1.3          0.2  Iris-setosa\n",
       "3           4.6          3.1           1.5          0.2  Iris-setosa\n",
       "4           5.0          3.6           1.4          0.2  Iris-setosa"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "e1be5533",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:35:02.070924Z",
     "iopub.status.busy": "2023-05-27T16:35:02.069137Z",
     "iopub.status.idle": "2023-05-27T16:35:02.085092Z",
     "shell.execute_reply": "2023-05-27T16:35:02.083479Z"
    },
    "papermill": {
     "duration": 0.065291,
     "end_time": "2023-05-27T16:35:02.089191",
     "exception": false,
     "start_time": "2023-05-27T16:35:02.023900",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         Iris-setosa\n",
       "1         Iris-setosa\n",
       "2         Iris-setosa\n",
       "3         Iris-setosa\n",
       "4         Iris-setosa\n",
       "            ...      \n",
       "145    Iris-virginica\n",
       "146    Iris-virginica\n",
       "147    Iris-virginica\n",
       "148    Iris-virginica\n",
       "149    Iris-virginica\n",
       "Name: species, Length: 150, dtype: object"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df['species']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "b32d50f2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:35:02.196431Z",
     "iopub.status.busy": "2023-05-27T16:35:02.195886Z",
     "iopub.status.idle": "2023-05-27T16:35:02.206216Z",
     "shell.execute_reply": "2023-05-27T16:35:02.205253Z"
    },
    "papermill": {
     "duration": 0.064888,
     "end_time": "2023-05-27T16:35:02.208819",
     "exception": false,
     "start_time": "2023-05-27T16:35:02.143931",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      5.1\n",
       "1      4.9\n",
       "2      4.7\n",
       "3      4.6\n",
       "4      5.0\n",
       "      ... \n",
       "145    6.7\n",
       "146    6.3\n",
       "147    6.5\n",
       "148    6.2\n",
       "149    5.9\n",
       "Name: sepal_length, Length: 150, dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df['sepal_length']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "72711e22",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:35:02.299758Z",
     "iopub.status.busy": "2023-05-27T16:35:02.298875Z",
     "iopub.status.idle": "2023-05-27T16:35:02.872771Z",
     "shell.execute_reply": "2023-05-27T16:35:02.871694Z"
    },
    "papermill": {
     "duration": 0.621249,
     "end_time": "2023-05-27T16:35:02.875589",
     "exception": false,
     "start_time": "2023-05-27T16:35:02.254340",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(iris_df['species'],iris_df['sepal_length'])\n",
    "plt.title(\"Sepal length VS Species\")\n",
    "plt.xlabel(\"Name of the Species\")\n",
    "plt.ylabel(\"Sepal Length\")\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "6458ee7a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:35:02.965046Z",
     "iopub.status.busy": "2023-05-27T16:35:02.964501Z",
     "iopub.status.idle": "2023-05-27T16:35:02.969969Z",
     "shell.execute_reply": "2023-05-27T16:35:02.968466Z"
    },
    "papermill": {
     "duration": 0.053363,
     "end_time": "2023-05-27T16:35:02.972590",
     "exception": false,
     "start_time": "2023-05-27T16:35:02.919227",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Bar plot bw sepal width and species"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "2e9b277e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:35:03.063526Z",
     "iopub.status.busy": "2023-05-27T16:35:03.062657Z",
     "iopub.status.idle": "2023-05-27T16:35:03.081736Z",
     "shell.execute_reply": "2023-05-27T16:35:03.080188Z"
    },
    "papermill": {
     "duration": 0.069221,
     "end_time": "2023-05-27T16:35:03.084828",
     "exception": false,
     "start_time": "2023-05-27T16:35:03.015607",
     "status": "completed"
    },
    "tags": []
   },
   "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>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>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width      species\n",
       "0           5.1          3.5           1.4          0.2  Iris-setosa\n",
       "1           4.9          3.0           1.4          0.2  Iris-setosa\n",
       "2           4.7          3.2           1.3          0.2  Iris-setosa\n",
       "3           4.6          3.1           1.5          0.2  Iris-setosa\n",
       "4           5.0          3.6           1.4          0.2  Iris-setosa"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "567736e0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:35:03.179551Z",
     "iopub.status.busy": "2023-05-27T16:35:03.178795Z",
     "iopub.status.idle": "2023-05-27T16:35:03.188238Z",
     "shell.execute_reply": "2023-05-27T16:35:03.187208Z"
    },
    "papermill": {
     "duration": 0.060247,
     "end_time": "2023-05-27T16:35:03.190719",
     "exception": false,
     "start_time": "2023-05-27T16:35:03.130472",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         Iris-setosa\n",
       "1         Iris-setosa\n",
       "2         Iris-setosa\n",
       "3         Iris-setosa\n",
       "4         Iris-setosa\n",
       "            ...      \n",
       "145    Iris-virginica\n",
       "146    Iris-virginica\n",
       "147    Iris-virginica\n",
       "148    Iris-virginica\n",
       "149    Iris-virginica\n",
       "Name: species, Length: 150, dtype: object"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df['species']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "a5d8ed54",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:35:03.282159Z",
     "iopub.status.busy": "2023-05-27T16:35:03.281414Z",
     "iopub.status.idle": "2023-05-27T16:35:03.290675Z",
     "shell.execute_reply": "2023-05-27T16:35:03.289734Z"
    },
    "papermill": {
     "duration": 0.058434,
     "end_time": "2023-05-27T16:35:03.293006",
     "exception": false,
     "start_time": "2023-05-27T16:35:03.234572",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      3.5\n",
       "1      3.0\n",
       "2      3.2\n",
       "3      3.1\n",
       "4      3.6\n",
       "      ... \n",
       "145    3.0\n",
       "146    2.5\n",
       "147    3.0\n",
       "148    3.4\n",
       "149    3.0\n",
       "Name: sepal_width, Length: 150, dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df['sepal_width']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "0311b92e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:35:03.386785Z",
     "iopub.status.busy": "2023-05-27T16:35:03.386022Z",
     "iopub.status.idle": "2023-05-27T16:35:03.909071Z",
     "shell.execute_reply": "2023-05-27T16:35:03.907728Z"
    },
    "papermill": {
     "duration": 0.573132,
     "end_time": "2023-05-27T16:35:03.911814",
     "exception": false,
     "start_time": "2023-05-27T16:35:03.338682",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(iris_df['species'],iris_df['sepal_width'])\n",
    "plt.title(\"Sepal Width VS Species\")\n",
    "plt.xlabel(\"Name of the Species\")\n",
    "plt.ylabel(\"Sepal Width\")\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "f0cd93d9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:35:04.003438Z",
     "iopub.status.busy": "2023-05-27T16:35:04.002954Z",
     "iopub.status.idle": "2023-05-27T16:35:04.009150Z",
     "shell.execute_reply": "2023-05-27T16:35:04.008040Z"
    },
    "papermill": {
     "duration": 0.054553,
     "end_time": "2023-05-27T16:35:04.011711",
     "exception": false,
     "start_time": "2023-05-27T16:35:03.957158",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Bar plot bw petal lenght and species"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "c7dc9374",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:35:04.102076Z",
     "iopub.status.busy": "2023-05-27T16:35:04.100799Z",
     "iopub.status.idle": "2023-05-27T16:35:04.116956Z",
     "shell.execute_reply": "2023-05-27T16:35:04.116047Z"
    },
    "papermill": {
     "duration": 0.063724,
     "end_time": "2023-05-27T16:35:04.119245",
     "exception": false,
     "start_time": "2023-05-27T16:35:04.055521",
     "status": "completed"
    },
    "tags": []
   },
   "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>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>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width      species\n",
       "0           5.1          3.5           1.4          0.2  Iris-setosa\n",
       "1           4.9          3.0           1.4          0.2  Iris-setosa\n",
       "2           4.7          3.2           1.3          0.2  Iris-setosa\n",
       "3           4.6          3.1           1.5          0.2  Iris-setosa\n",
       "4           5.0          3.6           1.4          0.2  Iris-setosa"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "bc95cc3b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:35:04.209573Z",
     "iopub.status.busy": "2023-05-27T16:35:04.209082Z",
     "iopub.status.idle": "2023-05-27T16:35:04.220052Z",
     "shell.execute_reply": "2023-05-27T16:35:04.218842Z"
    },
    "papermill": {
     "duration": 0.059879,
     "end_time": "2023-05-27T16:35:04.222861",
     "exception": false,
     "start_time": "2023-05-27T16:35:04.162982",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1.4\n",
       "1      1.4\n",
       "2      1.3\n",
       "3      1.5\n",
       "4      1.4\n",
       "      ... \n",
       "145    5.2\n",
       "146    5.0\n",
       "147    5.2\n",
       "148    5.4\n",
       "149    5.1\n",
       "Name: petal_length, Length: 150, dtype: float64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df['petal_length']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "aed01ec9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:35:04.315479Z",
     "iopub.status.busy": "2023-05-27T16:35:04.314658Z",
     "iopub.status.idle": "2023-05-27T16:35:04.324932Z",
     "shell.execute_reply": "2023-05-27T16:35:04.323701Z"
    },
    "papermill": {
     "duration": 0.060348,
     "end_time": "2023-05-27T16:35:04.327492",
     "exception": false,
     "start_time": "2023-05-27T16:35:04.267144",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         Iris-setosa\n",
       "1         Iris-setosa\n",
       "2         Iris-setosa\n",
       "3         Iris-setosa\n",
       "4         Iris-setosa\n",
       "            ...      \n",
       "145    Iris-virginica\n",
       "146    Iris-virginica\n",
       "147    Iris-virginica\n",
       "148    Iris-virginica\n",
       "149    Iris-virginica\n",
       "Name: species, Length: 150, dtype: object"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df['species']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "24aa731c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:35:04.419692Z",
     "iopub.status.busy": "2023-05-27T16:35:04.419148Z",
     "iopub.status.idle": "2023-05-27T16:35:04.970186Z",
     "shell.execute_reply": "2023-05-27T16:35:04.969082Z"
    },
    "papermill": {
     "duration": 0.60077,
     "end_time": "2023-05-27T16:35:04.972975",
     "exception": false,
     "start_time": "2023-05-27T16:35:04.372205",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(iris_df['species'],iris_df['petal_length'])\n",
    "plt.title(\"Petal Length VS Species\")\n",
    "plt.xlabel(\"Name of the Species\")\n",
    "plt.ylabel(\"Petal Length\")\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "f2e86cd1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:35:05.065885Z",
     "iopub.status.busy": "2023-05-27T16:35:05.065088Z",
     "iopub.status.idle": "2023-05-27T16:35:05.069589Z",
     "shell.execute_reply": "2023-05-27T16:35:05.068698Z"
    },
    "papermill": {
     "duration": 0.05432,
     "end_time": "2023-05-27T16:35:05.072104",
     "exception": false,
     "start_time": "2023-05-27T16:35:05.017784",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Bar plot bw petal width and species"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "8ea604af",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:35:05.167020Z",
     "iopub.status.busy": "2023-05-27T16:35:05.166303Z",
     "iopub.status.idle": "2023-05-27T16:35:05.184290Z",
     "shell.execute_reply": "2023-05-27T16:35:05.182877Z"
    },
    "papermill": {
     "duration": 0.06768,
     "end_time": "2023-05-27T16:35:05.186957",
     "exception": false,
     "start_time": "2023-05-27T16:35:05.119277",
     "status": "completed"
    },
    "tags": []
   },
   "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>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>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width      species\n",
       "0           5.1          3.5           1.4          0.2  Iris-setosa\n",
       "1           4.9          3.0           1.4          0.2  Iris-setosa\n",
       "2           4.7          3.2           1.3          0.2  Iris-setosa\n",
       "3           4.6          3.1           1.5          0.2  Iris-setosa\n",
       "4           5.0          3.6           1.4          0.2  Iris-setosa"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "f430d693",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:35:05.280327Z",
     "iopub.status.busy": "2023-05-27T16:35:05.279814Z",
     "iopub.status.idle": "2023-05-27T16:35:05.290496Z",
     "shell.execute_reply": "2023-05-27T16:35:05.289312Z"
    },
    "papermill": {
     "duration": 0.059698,
     "end_time": "2023-05-27T16:35:05.292890",
     "exception": false,
     "start_time": "2023-05-27T16:35:05.233192",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0.2\n",
       "1      0.2\n",
       "2      0.2\n",
       "3      0.2\n",
       "4      0.2\n",
       "      ... \n",
       "145    2.3\n",
       "146    1.9\n",
       "147    2.0\n",
       "148    2.3\n",
       "149    1.8\n",
       "Name: petal_width, Length: 150, dtype: float64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df['petal_width']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "146e8965",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:35:05.387233Z",
     "iopub.status.busy": "2023-05-27T16:35:05.386745Z",
     "iopub.status.idle": "2023-05-27T16:35:05.396731Z",
     "shell.execute_reply": "2023-05-27T16:35:05.395280Z"
    },
    "papermill": {
     "duration": 0.062159,
     "end_time": "2023-05-27T16:35:05.400420",
     "exception": false,
     "start_time": "2023-05-27T16:35:05.338261",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         Iris-setosa\n",
       "1         Iris-setosa\n",
       "2         Iris-setosa\n",
       "3         Iris-setosa\n",
       "4         Iris-setosa\n",
       "            ...      \n",
       "145    Iris-virginica\n",
       "146    Iris-virginica\n",
       "147    Iris-virginica\n",
       "148    Iris-virginica\n",
       "149    Iris-virginica\n",
       "Name: species, Length: 150, dtype: object"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df['species']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "84bfd2f0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:35:05.497757Z",
     "iopub.status.busy": "2023-05-27T16:35:05.497223Z",
     "iopub.status.idle": "2023-05-27T16:35:06.040253Z",
     "shell.execute_reply": "2023-05-27T16:35:06.039246Z"
    },
    "papermill": {
     "duration": 0.593892,
     "end_time": "2023-05-27T16:35:06.042993",
     "exception": false,
     "start_time": "2023-05-27T16:35:05.449101",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(iris_df['species'],iris_df['petal_width'])\n",
    "plt.title(\"Petal Width VS Spcies\")\n",
    "plt.xlabel(\"Name of the Species\")\n",
    "plt.ylabel(\"Petal Width\")\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "77f2511f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:35:06.137679Z",
     "iopub.status.busy": "2023-05-27T16:35:06.136291Z",
     "iopub.status.idle": "2023-05-27T16:35:06.479210Z",
     "shell.execute_reply": "2023-05-27T16:35:06.477768Z"
    },
    "papermill": {
     "duration": 0.393697,
     "end_time": "2023-05-27T16:35:06.482281",
     "exception": false,
     "start_time": "2023-05-27T16:35:06.088584",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "56a6c202",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:35:06.584583Z",
     "iopub.status.busy": "2023-05-27T16:35:06.584137Z",
     "iopub.status.idle": "2023-05-27T16:35:06.709822Z",
     "shell.execute_reply": "2023-05-27T16:35:06.708416Z"
    },
    "papermill": {
     "duration": 0.183315,
     "end_time": "2023-05-27T16:35:06.713129",
     "exception": false,
     "start_time": "2023-05-27T16:35:06.529814",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression, LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "2ec4a7ac",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:35:06.811723Z",
     "iopub.status.busy": "2023-05-27T16:35:06.810524Z",
     "iopub.status.idle": "2023-05-27T16:35:06.817186Z",
     "shell.execute_reply": "2023-05-27T16:35:06.815827Z"
    },
    "papermill": {
     "duration": 0.060015,
     "end_time": "2023-05-27T16:35:06.820034",
     "exception": false,
     "start_time": "2023-05-27T16:35:06.760019",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, classification_report, mean_absolute_error, mean_squared_error, r2_score "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "e729d30d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:35:06.916827Z",
     "iopub.status.busy": "2023-05-27T16:35:06.916267Z",
     "iopub.status.idle": "2023-05-27T16:35:06.921738Z",
     "shell.execute_reply": "2023-05-27T16:35:06.920735Z"
    },
    "papermill": {
     "duration": 0.057869,
     "end_time": "2023-05-27T16:35:06.924067",
     "exception": false,
     "start_time": "2023-05-27T16:35:06.866198",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "1237cf97",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:35:07.019816Z",
     "iopub.status.busy": "2023-05-27T16:35:07.018979Z",
     "iopub.status.idle": "2023-05-27T16:35:07.024013Z",
     "shell.execute_reply": "2023-05-27T16:35:07.023001Z"
    },
    "papermill": {
     "duration": 0.056049,
     "end_time": "2023-05-27T16:35:07.026615",
     "exception": false,
     "start_time": "2023-05-27T16:35:06.970566",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "fa38be29",
   "metadata": {
    "execution": {
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   "source": [
    "# Make the data X and Y"
   ]
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   "cell_type": "code",
   "execution_count": 73,
   "id": "6bc6e0bb",
   "metadata": {
    "execution": {
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   "source": [
    "X=iris_df.drop('species',axis=1)"
   ]
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   "cell_type": "code",
   "execution_count": 74,
   "id": "e3cfe6f7",
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    "type(X)"
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   "execution_count": 76,
   "id": "8c1d7bca",
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   "source": [
    "Y=iris_df['species']"
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   "cell_type": "code",
   "execution_count": 77,
   "id": "cefbffbf",
   "metadata": {
    "execution": {
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    {
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      "text/plain": [
       "0         Iris-setosa\n",
       "1         Iris-setosa\n",
       "2         Iris-setosa\n",
       "3         Iris-setosa\n",
       "4         Iris-setosa\n",
       "            ...      \n",
       "145    Iris-virginica\n",
       "146    Iris-virginica\n",
       "147    Iris-virginica\n",
       "148    Iris-virginica\n",
       "149    Iris-virginica\n",
       "Name: species, Length: 150, dtype: object"
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     "execution_count": 77,
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    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
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     "execution_count": 78,
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   "execution_count": 79,
   "id": "37c2f753",
   "metadata": {
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   "source": [
    "# Split the data into training and testing sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "3ebfd13c",
   "metadata": {
    "execution": {
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   "source": [
    "X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=0.24,random_state=20)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 81,
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   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:35:08.661078Z",
     "iopub.status.busy": "2023-05-27T16:35:08.659794Z",
     "iopub.status.idle": "2023-05-27T16:35:08.668055Z",
     "shell.execute_reply": "2023-05-27T16:35:08.667048Z"
    },
    "papermill": {
     "duration": 0.063314,
     "end_time": "2023-05-27T16:35:08.670693",
     "exception": false,
     "start_time": "2023-05-27T16:35:08.607379",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "bdc8f193",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:35:08.776069Z",
     "iopub.status.busy": "2023-05-27T16:35:08.775253Z",
     "iopub.status.idle": "2023-05-27T16:35:08.785175Z",
     "shell.execute_reply": "2023-05-27T16:35:08.783618Z"
    },
    "papermill": {
     "duration": 0.065435,
     "end_time": "2023-05-27T16:35:08.787949",
     "exception": false,
     "start_time": "2023-05-27T16:35:08.722514",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "85     Iris-versicolor\n",
       "1          Iris-setosa\n",
       "51     Iris-versicolor\n",
       "130     Iris-virginica\n",
       "55     Iris-versicolor\n",
       "Name: species, dtype: object"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "44e582cb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:35:08.890309Z",
     "iopub.status.busy": "2023-05-27T16:35:08.889801Z",
     "iopub.status.idle": "2023-05-27T16:35:08.897911Z",
     "shell.execute_reply": "2023-05-27T16:35:08.896487Z"
    },
    "papermill": {
     "duration": 0.063538,
     "end_time": "2023-05-27T16:35:08.900703",
     "exception": false,
     "start_time": "2023-05-27T16:35:08.837165",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(114,)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "24d1094e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:35:09.002513Z",
     "iopub.status.busy": "2023-05-27T16:35:09.000533Z",
     "iopub.status.idle": "2023-05-27T16:35:09.010076Z",
     "shell.execute_reply": "2023-05-27T16:35:09.008722Z"
    },
    "papermill": {
     "duration": 0.062917,
     "end_time": "2023-05-27T16:35:09.012763",
     "exception": false,
     "start_time": "2023-05-27T16:35:08.949846",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "4f1071ac",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:35:09.117829Z",
     "iopub.status.busy": "2023-05-27T16:35:09.116230Z",
     "iopub.status.idle": "2023-05-27T16:35:09.127355Z",
     "shell.execute_reply": "2023-05-27T16:35:09.125971Z"
    },
    "papermill": {
     "duration": 0.067695,
     "end_time": "2023-05-27T16:35:09.130364",
     "exception": false,
     "start_time": "2023-05-27T16:35:09.062669",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "47         Iris-setosa\n",
       "73     Iris-versicolor\n",
       "74     Iris-versicolor\n",
       "129     Iris-virginica\n",
       "67     Iris-versicolor\n",
       "Name: species, dtype: object"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "97a45252",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:35:09.235314Z",
     "iopub.status.busy": "2023-05-27T16:35:09.234100Z",
     "iopub.status.idle": "2023-05-27T16:35:09.242094Z",
     "shell.execute_reply": "2023-05-27T16:35:09.241167Z"
    },
    "papermill": {
     "duration": 0.06557,
     "end_time": "2023-05-27T16:35:09.244656",
     "exception": false,
     "start_time": "2023-05-27T16:35:09.179086",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(36,)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "fa049b45",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:35:09.346869Z",
     "iopub.status.busy": "2023-05-27T16:35:09.345978Z",
     "iopub.status.idle": "2023-05-27T16:35:09.352506Z",
     "shell.execute_reply": "2023-05-27T16:35:09.351615Z"
    },
    "papermill": {
     "duration": 0.059832,
     "end_time": "2023-05-27T16:35:09.354760",
     "exception": false,
     "start_time": "2023-05-27T16:35:09.294928",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "615b5886",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:35:09.459088Z",
     "iopub.status.busy": "2023-05-27T16:35:09.458566Z",
     "iopub.status.idle": "2023-05-27T16:35:09.463572Z",
     "shell.execute_reply": "2023-05-27T16:35:09.462534Z"
    },
    "papermill": {
     "duration": 0.061477,
     "end_time": "2023-05-27T16:35:09.465934",
     "exception": false,
     "start_time": "2023-05-27T16:35:09.404457",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Standardized X with the help of StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "5de15bbc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:35:09.567709Z",
     "iopub.status.busy": "2023-05-27T16:35:09.566897Z",
     "iopub.status.idle": "2023-05-27T16:35:09.571486Z",
     "shell.execute_reply": "2023-05-27T16:35:09.570621Z"
    },
    "papermill": {
     "duration": 0.058785,
     "end_time": "2023-05-27T16:35:09.573919",
     "exception": false,
     "start_time": "2023-05-27T16:35:09.515134",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "scaler=StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "cf21df3c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:35:09.674530Z",
     "iopub.status.busy": "2023-05-27T16:35:09.673750Z",
     "iopub.status.idle": "2023-05-27T16:35:09.682904Z",
     "shell.execute_reply": "2023-05-27T16:35:09.681897Z"
    },
    "papermill": {
     "duration": 0.063331,
     "end_time": "2023-05-27T16:35:09.685868",
     "exception": false,
     "start_time": "2023-05-27T16:35:09.622537",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_train=scaler.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "934b0642",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:35:09.787983Z",
     "iopub.status.busy": "2023-05-27T16:35:09.787162Z",
     "iopub.status.idle": "2023-05-27T16:35:09.800501Z",
     "shell.execute_reply": "2023-05-27T16:35:09.799107Z"
    },
    "papermill": {
     "duration": 0.067727,
     "end_time": "2023-05-27T16:35:09.803148",
     "exception": false,
     "start_time": "2023-05-27T16:35:09.735421",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.16782396,  0.69731669,  0.41803027,  0.49149268],\n",
       "       [-1.16414597, -0.20346634, -1.32614799, -1.315932  ],\n",
       "       [ 0.65217667,  0.24692517,  0.41803027,  0.36239091],\n",
       "       [ 1.86305843, -0.65385786,  1.31825131,  0.87879796],\n",
       "       [-0.19544056, -0.65385786,  0.41803027,  0.10418739],\n",
       "       [ 0.28891214, -1.10424937,  1.03693224,  0.23328915],\n",
       "       [-1.16414597,  0.02172942, -1.26988417, -1.44503377],\n",
       "       [ 1.01544119,  0.02172942,  0.36176646,  0.23328915],\n",
       "       [ 0.53108849,  0.47212093,  0.5305579 ,  0.49149268],\n",
       "       [-1.40632232,  0.24692517, -1.3824118 , -1.315932  ],\n",
       "       [-0.55870509,  1.82329547, -1.15735654, -1.05772848],\n",
       "       [-1.0430578 ,  0.47212093, -1.32614799, -1.315932  ],\n",
       "       [-0.19544056, -1.32944513,  0.69934935,  1.00789973],\n",
       "       [-0.92196962,  1.59809971, -1.04482891, -1.05772848],\n",
       "       [ 0.65217667, -0.65385786,  1.03693224,  1.26610325],\n",
       "       [ 0.41000031,  0.69731669,  0.92440461,  1.39520502],\n",
       "       [-0.55870509,  0.69731669, -1.26988417, -1.05772848],\n",
       "       [ 0.53108849,  0.47212093,  1.2619875 ,  1.65340854],\n",
       "       [ 0.16782396, -2.0050324 ,  0.1367112 , -0.2831179 ],\n",
       "       [-0.31652874, -0.4286621 , -0.08834406,  0.10418739],\n",
       "       [-0.31652874, -0.20346634,  0.41803027,  0.36239091],\n",
       "       [-1.64849868, -1.77983664, -1.3824118 , -1.18683024],\n",
       "       [-0.92196962,  0.69731669, -1.26988417, -1.315932  ],\n",
       "       [-0.43761692, -1.55464088,  0.02418357, -0.15401614],\n",
       "       [ 0.41000031, -0.4286621 ,  0.30550264,  0.10418739],\n",
       "       [-0.07435239, -1.10424937,  0.1367112 , -0.02491438],\n",
       "       [-0.31652874, -1.32944513,  0.08044738, -0.15401614],\n",
       "       [ 1.01544119,  0.02172942,  0.5305579 ,  0.36239091],\n",
       "       [ 0.77326484, -0.65385786,  0.47429409,  0.36239091],\n",
       "       [-0.19544056, -0.4286621 ,  0.24923883,  0.10418739],\n",
       "       [-0.92196962,  0.47212093, -1.15735654, -0.92862672],\n",
       "       [ 0.41000031, -2.0050324 ,  0.41803027,  0.36239091],\n",
       "       [ 0.53108849, -1.77983664,  0.36176646,  0.10418739],\n",
       "       [-1.16414597,  0.02172942, -1.26988417, -1.44503377],\n",
       "       [-1.76958685, -0.20346634, -1.3824118 , -1.315932  ],\n",
       "       [-0.55870509, -0.20346634,  0.41803027,  0.36239091],\n",
       "       [-0.43761692, -1.77983664,  0.1367112 ,  0.10418739],\n",
       "       [ 0.28891214, -0.20346634,  0.47429409,  0.23328915],\n",
       "       [-0.92196962,  1.59809971, -1.26988417, -1.18683024],\n",
       "       [-0.67979327,  1.37290396, -1.26988417, -1.315932  ],\n",
       "       [ 0.65217667,  0.24692517,  0.86814079,  1.39520502],\n",
       "       [-0.43761692,  0.92251244, -1.3824118 , -1.315932  ],\n",
       "       [-1.28523415,  0.02172942, -1.21362036, -1.315932  ],\n",
       "       [-1.0430578 , -2.45542391, -0.14460787, -0.2831179 ],\n",
       "       [ 0.16782396, -0.20346634,  0.58682172,  0.7496962 ],\n",
       "       [-0.07435239, -0.87905361,  0.75561316,  0.87879796],\n",
       "       [-0.31652874, -0.65385786,  0.64308553,  1.00789973],\n",
       "       [ 0.53108849, -0.4286621 ,  1.03693224,  0.7496962 ],\n",
       "       [ 0.65217667, -0.65385786,  1.03693224,  1.13700149],\n",
       "       [ 1.4997939 , -0.20346634,  1.20572368,  1.13700149],\n",
       "       [-0.07435239, -0.87905361,  0.08044738, -0.02491438],\n",
       "       [ 0.77326484, -0.20346634,  0.98066842,  0.7496962 ],\n",
       "       [ 2.22632295, -0.20346634,  1.31825131,  1.39520502],\n",
       "       [ 1.62088207,  1.1477082 ,  1.31825131,  1.65340854],\n",
       "       [-1.76958685, -0.4286621 , -1.32614799, -1.315932  ],\n",
       "       [ 1.13652937, -0.20346634,  0.98066842,  1.13700149],\n",
       "       [-0.80088144,  0.92251244, -1.26988417, -1.315932  ],\n",
       "       [-1.0430578 ,  1.1477082 , -1.32614799, -1.315932  ],\n",
       "       [-0.07435239,  2.04849123, -1.43867562, -1.315932  ],\n",
       "       [ 1.01544119, -0.20346634,  0.81187698,  1.39520502],\n",
       "       [ 1.25761755,  0.02172942,  0.64308553,  0.36239091],\n",
       "       [ 0.04673579,  0.24692517,  0.58682172,  0.7496962 ],\n",
       "       [ 1.25761755,  0.02172942,  0.92440461,  1.13700149],\n",
       "       [ 0.53108849, -0.87905361,  0.64308553,  0.7496962 ],\n",
       "       [-0.31652874, -0.87905361,  0.24923883,  0.10418739],\n",
       "       [-1.76958685,  0.24692517, -1.3824118 , -1.315932  ],\n",
       "       [ 0.28891214, -0.20346634,  0.64308553,  0.7496962 ],\n",
       "       [-0.43761692,  2.49888274, -1.32614799, -1.315932  ],\n",
       "       [-1.0430578 , -1.77983664, -0.2571355 , -0.2831179 ],\n",
       "       [ 2.22632295,  1.59809971,  1.6558342 ,  1.26610325],\n",
       "       [-1.28523415,  0.69731669, -1.04482891, -1.315932  ],\n",
       "       [ 0.16782396, -0.4286621 ,  0.41803027,  0.36239091],\n",
       "       [ 0.28891214, -0.4286621 ,  0.5305579 ,  0.23328915],\n",
       "       [ 0.41000031, -0.65385786,  0.58682172,  0.7496962 ],\n",
       "       [-1.28523415, -0.20346634, -1.32614799, -1.18683024],\n",
       "       [-1.40632232,  0.24692517, -1.21362036, -1.315932  ],\n",
       "       [ 1.62088207,  0.24692517,  1.2619875 ,  0.7496962 ],\n",
       "       [ 1.01544119,  0.47212093,  1.09319605,  1.65340854],\n",
       "       [-0.80088144, -0.87905361,  0.08044738,  0.23328915],\n",
       "       [-0.43761692, -1.10424937,  0.36176646, -0.02491438],\n",
       "       [-0.19544056, -1.10424937, -0.14460787, -0.2831179 ],\n",
       "       [ 1.01544119,  0.47212093,  1.09319605,  1.13700149],\n",
       "       [ 1.01544119,  0.02172942,  1.03693224,  1.52430678],\n",
       "       [-1.0430578 ,  0.92251244, -1.21362036, -0.79952495],\n",
       "       [ 1.25761755,  0.24692517,  1.09319605,  1.39520502],\n",
       "       [-0.92196962,  0.92251244, -1.32614799, -1.18683024],\n",
       "       [ 1.01544119, -0.20346634,  0.69934935,  0.62059444],\n",
       "       [-1.5274105 ,  0.02172942, -1.26988417, -1.315932  ],\n",
       "       [-0.19544056,  1.59809971, -1.15735654, -1.18683024],\n",
       "       [ 0.53108849, -1.32944513,  0.69934935,  0.87879796],\n",
       "       [ 0.53108849,  0.69731669,  1.03693224,  1.52430678],\n",
       "       [ 1.74197025, -0.4286621 ,  1.43077894,  0.7496962 ],\n",
       "       [-0.55870509,  1.37290396, -1.26988417, -1.315932  ],\n",
       "       [-0.80088144,  2.27368699, -1.26988417, -1.44503377],\n",
       "       [ 0.77326484,  0.24692517,  0.75561316,  1.00789973],\n",
       "       [ 0.16782396, -0.87905361,  0.75561316,  0.49149268],\n",
       "       [ 0.04673579, -0.20346634,  0.24923883,  0.36239091],\n",
       "       [ 2.4684993 ,  1.59809971,  1.48704276,  1.00789973],\n",
       "       [-1.0430578 , -0.20346634, -1.21362036, -1.315932  ],\n",
       "       [ 1.25761755,  0.02172942,  0.75561316,  1.39520502],\n",
       "       [-1.5274105 ,  0.69731669, -1.32614799, -1.18683024],\n",
       "       [-1.0430578 ,  0.69731669, -1.26988417, -1.315932  ],\n",
       "       [-0.07435239, -0.87905361,  0.75561316,  0.87879796],\n",
       "       [ 2.10523478, -0.20346634,  1.59957039,  1.13700149],\n",
       "       [-0.55870509,  1.82329547, -1.3824118 , -1.05772848],\n",
       "       [-1.0430578 ,  0.69731669, -1.21362036, -1.05772848],\n",
       "       [-1.0430578 ,  0.92251244, -1.3824118 , -1.18683024],\n",
       "       [ 2.22632295, -1.10424937,  1.76836183,  1.39520502],\n",
       "       [ 0.28891214, -0.65385786,  0.1367112 ,  0.10418739],\n",
       "       [-1.5274105 ,  1.1477082 , -1.55120325, -1.315932  ],\n",
       "       [ 0.89435302, -0.20346634,  0.36176646,  0.23328915],\n",
       "       [ 0.65217667,  0.02172942,  0.98066842,  0.7496962 ],\n",
       "       [-0.19544056,  2.94927426, -1.26988417, -1.05772848],\n",
       "       [-0.19544056, -0.65385786,  0.19297501,  0.10418739]])"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "08797413",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:35:09.906279Z",
     "iopub.status.busy": "2023-05-27T16:35:09.904872Z",
     "iopub.status.idle": "2023-05-27T16:35:09.913434Z",
     "shell.execute_reply": "2023-05-27T16:35:09.912106Z"
    },
    "papermill": {
     "duration": 0.063437,
     "end_time": "2023-05-27T16:35:09.916289",
     "exception": false,
     "start_time": "2023-05-27T16:35:09.852852",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(114, 4)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "9fc1ac02",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:35:10.036367Z",
     "iopub.status.busy": "2023-05-27T16:35:10.035221Z",
     "iopub.status.idle": "2023-05-27T16:35:10.043851Z",
     "shell.execute_reply": "2023-05-27T16:35:10.042714Z"
    },
    "papermill": {
     "duration": 0.07322,
     "end_time": "2023-05-27T16:35:10.046768",
     "exception": false,
     "start_time": "2023-05-27T16:35:09.973548",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "81b8a9f8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:35:10.161145Z",
     "iopub.status.busy": "2023-05-27T16:35:10.160220Z",
     "iopub.status.idle": "2023-05-27T16:35:10.170353Z",
     "shell.execute_reply": "2023-05-27T16:35:10.169263Z"
    },
    "papermill": {
     "duration": 0.06558,
     "end_time": "2023-05-27T16:35:10.173398",
     "exception": false,
     "start_time": "2023-05-27T16:35:10.107818",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_test=scaler.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "fa836e1f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:35:10.284175Z",
     "iopub.status.busy": "2023-05-27T16:35:10.283170Z",
     "iopub.status.idle": "2023-05-27T16:35:10.293765Z",
     "shell.execute_reply": "2023-05-27T16:35:10.292449Z"
    },
    "papermill": {
     "duration": 0.066021,
     "end_time": "2023-05-27T16:35:10.296712",
     "exception": false,
     "start_time": "2023-05-27T16:35:10.230691",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.44478718,  0.70734698, -1.39251151, -1.31338208],\n",
       "       [ 0.38234415, -0.37624839,  0.5514411 ,  0.09381301],\n",
       "       [ 0.74777041, -0.10534955,  0.31581048,  0.23453251],\n",
       "       [ 1.72224045,  0.16554929,  1.1994253 ,  0.65669104],\n",
       "       [ 0.01691788, -0.64714724,  0.19799517, -0.18762601],\n",
       "       [-0.34850838, -1.18894492,  0.13908752,  0.23453251],\n",
       "       [ 1.23500543,  0.70734698,  1.25833296,  1.6417276 ],\n",
       "       [-0.8357434 ,  2.0618412 , -1.33360385, -1.03194307],\n",
       "       [ 1.11319668, -1.18894492,  1.1994253 ,  0.93813006],\n",
       "       [-1.20116967,  0.16554929, -1.39251151, -1.45410159],\n",
       "       [ 0.86957917,  0.16554929,  0.84597938,  1.21956908],\n",
       "       [ 1.23500543, -0.37624839,  0.61034876,  0.37525202],\n",
       "       [ 0.26053539, -2.00164146,  0.72816407,  0.51597153],\n",
       "       [-0.95755216,  0.70734698, -1.51032681, -1.31338208],\n",
       "       [-0.71393465,  1.24914467, -1.39251151, -1.31338208],\n",
       "       [ 2.33128423, -0.37624839,  1.7295942 ,  1.21956908],\n",
       "       [-1.81021344,  0.16554929, -1.56923447, -1.45410159],\n",
       "       [ 0.99138792, -0.10534955,  0.49253345,  0.23453251],\n",
       "       [ 0.01691788, -0.37624839,  0.78707172,  1.78244711],\n",
       "       [-1.07936091, -1.45984377, -0.27326607, -0.18762601],\n",
       "       [ 1.47862294,  0.70734698,  0.5514411 ,  0.37525202],\n",
       "       [ 0.13872664,  0.16554929,  0.78707172,  0.93813006],\n",
       "       [ 0.74777041, -0.64714724,  0.90488703,  1.07884957],\n",
       "       [-0.47031714,  1.24914467, -1.21578854, -1.31338208],\n",
       "       [ 0.62596166, -1.18894492,  0.66925641,  0.51597153],\n",
       "       [-0.34850838, -1.45984377, -0.03763545, -0.18762601],\n",
       "       [-0.8357434 , -1.18894492, -0.44998903, -0.0469065 ],\n",
       "       [-1.07936091,  0.43644814, -1.33360385, -1.45410159],\n",
       "       [ 0.86957917,  0.16554929,  1.1994253 ,  1.5010081 ],\n",
       "       [ 0.62596166, -0.37624839,  0.78707172,  0.51597153],\n",
       "       [-0.10489087,  0.16554929,  0.25690283,  0.09381301],\n",
       "       [-0.22669963,  0.16554929,  0.19799517,  0.23453251],\n",
       "       [-0.8357434 ,  1.52004351, -1.39251151, -1.31338208],\n",
       "       [-0.8357434 ,  2.33274004, -1.2746962 , -1.31338208],\n",
       "       [-1.20116967,  1.24914467, -1.2746962 , -1.31338208],\n",
       "       [-1.07936091, -1.18894492,  0.43362579,  0.79741055]])"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "bef0e049",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:35:10.414347Z",
     "iopub.status.busy": "2023-05-27T16:35:10.413212Z",
     "iopub.status.idle": "2023-05-27T16:35:10.424141Z",
     "shell.execute_reply": "2023-05-27T16:35:10.420744Z"
    },
    "papermill": {
     "duration": 0.078727,
     "end_time": "2023-05-27T16:35:10.427507",
     "exception": false,
     "start_time": "2023-05-27T16:35:10.348780",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(36, 4)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "35a3bda0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:35:10.544867Z",
     "iopub.status.busy": "2023-05-27T16:35:10.544277Z",
     "iopub.status.idle": "2023-05-27T16:35:10.552900Z",
     "shell.execute_reply": "2023-05-27T16:35:10.551680Z"
    },
    "papermill": {
     "duration": 0.06579,
     "end_time": "2023-05-27T16:35:10.555547",
     "exception": false,
     "start_time": "2023-05-27T16:35:10.489757",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "094366dc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:35:10.664505Z",
     "iopub.status.busy": "2023-05-27T16:35:10.663492Z",
     "iopub.status.idle": "2023-05-27T16:35:10.671182Z",
     "shell.execute_reply": "2023-05-27T16:35:10.669741Z"
    },
    "papermill": {
     "duration": 0.067012,
     "end_time": "2023-05-27T16:35:10.673729",
     "exception": false,
     "start_time": "2023-05-27T16:35:10.606717",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(114, 4)\n",
      "(36, 4)\n",
      "(36,)\n",
      "(114,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(Y_test.shape)\n",
    "print(Y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "193944ae",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:35:10.779133Z",
     "iopub.status.busy": "2023-05-27T16:35:10.778681Z",
     "iopub.status.idle": "2023-05-27T16:35:10.784767Z",
     "shell.execute_reply": "2023-05-27T16:35:10.783290Z"
    },
    "papermill": {
     "duration": 0.061294,
     "end_time": "2023-05-27T16:35:10.787400",
     "exception": false,
     "start_time": "2023-05-27T16:35:10.726106",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Creating a logestic regression model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "6ff3f412",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:35:10.895057Z",
     "iopub.status.busy": "2023-05-27T16:35:10.894102Z",
     "iopub.status.idle": "2023-05-27T16:35:10.899926Z",
     "shell.execute_reply": "2023-05-27T16:35:10.898798Z"
    },
    "papermill": {
     "duration": 0.064074,
     "end_time": "2023-05-27T16:35:10.902835",
     "exception": false,
     "start_time": "2023-05-27T16:35:10.838761",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "model=LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "e14b7afc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:35:11.007904Z",
     "iopub.status.busy": "2023-05-27T16:35:11.007376Z",
     "iopub.status.idle": "2023-05-27T16:35:11.038862Z",
     "shell.execute_reply": "2023-05-27T16:35:11.037481Z"
    },
    "papermill": {
     "duration": 0.088458,
     "end_time": "2023-05-27T16:35:11.041744",
     "exception": false,
     "start_time": "2023-05-27T16:35:10.953286",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "3dd51fa1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:35:11.146908Z",
     "iopub.status.busy": "2023-05-27T16:35:11.146399Z",
     "iopub.status.idle": "2023-05-27T16:35:11.151526Z",
     "shell.execute_reply": "2023-05-27T16:35:11.150275Z"
    },
    "papermill": {
     "duration": 0.060267,
     "end_time": "2023-05-27T16:35:11.154060",
     "exception": false,
     "start_time": "2023-05-27T16:35:11.093793",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "3e3fb5cd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:35:11.257940Z",
     "iopub.status.busy": "2023-05-27T16:35:11.257016Z",
     "iopub.status.idle": "2023-05-27T16:35:11.263177Z",
     "shell.execute_reply": "2023-05-27T16:35:11.262124Z"
    },
    "papermill": {
     "duration": 0.061339,
     "end_time": "2023-05-27T16:35:11.265895",
     "exception": false,
     "start_time": "2023-05-27T16:35:11.204556",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "y_pred=model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "cbe3ab6c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:35:11.369876Z",
     "iopub.status.busy": "2023-05-27T16:35:11.369382Z",
     "iopub.status.idle": "2023-05-27T16:35:11.377044Z",
     "shell.execute_reply": "2023-05-27T16:35:11.375712Z"
    },
    "papermill": {
     "duration": 0.062805,
     "end_time": "2023-05-27T16:35:11.379621",
     "exception": false,
     "start_time": "2023-05-27T16:35:11.316816",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Iris-setosa', 'Iris-versicolor', 'Iris-versicolor',\n",
       "       'Iris-virginica', 'Iris-versicolor', 'Iris-versicolor',\n",
       "       'Iris-virginica', 'Iris-setosa', 'Iris-virginica', 'Iris-setosa',\n",
       "       'Iris-virginica', 'Iris-versicolor', 'Iris-virginica',\n",
       "       'Iris-setosa', 'Iris-setosa', 'Iris-virginica', 'Iris-setosa',\n",
       "       'Iris-versicolor', 'Iris-virginica', 'Iris-versicolor',\n",
       "       'Iris-versicolor', 'Iris-virginica', 'Iris-virginica',\n",
       "       'Iris-setosa', 'Iris-versicolor', 'Iris-versicolor',\n",
       "       'Iris-versicolor', 'Iris-setosa', 'Iris-virginica',\n",
       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
       "       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-versicolor'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "1f4e3b7d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:35:11.485900Z",
     "iopub.status.busy": "2023-05-27T16:35:11.484967Z",
     "iopub.status.idle": "2023-05-27T16:35:11.491928Z",
     "shell.execute_reply": "2023-05-27T16:35:11.490678Z"
    },
    "papermill": {
     "duration": 0.063709,
     "end_time": "2023-05-27T16:35:11.494374",
     "exception": false,
     "start_time": "2023-05-27T16:35:11.430665",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(36, 4)\n",
      "(36,)\n"
     ]
    }
   ],
   "source": [
    "print(X_test.shape)\n",
    "print(y_pred.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "7922849e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:35:11.604354Z",
     "iopub.status.busy": "2023-05-27T16:35:11.603366Z",
     "iopub.status.idle": "2023-05-27T16:35:11.608659Z",
     "shell.execute_reply": "2023-05-27T16:35:11.607733Z"
    },
    "papermill": {
     "duration": 0.06585,
     "end_time": "2023-05-27T16:35:11.611307",
     "exception": false,
     "start_time": "2023-05-27T16:35:11.545457",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Training accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "2a52d8e2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:35:11.720117Z",
     "iopub.status.busy": "2023-05-27T16:35:11.719593Z",
     "iopub.status.idle": "2023-05-27T16:35:11.729302Z",
     "shell.execute_reply": "2023-05-27T16:35:11.728228Z"
    },
    "papermill": {
     "duration": 0.066073,
     "end_time": "2023-05-27T16:35:11.732191",
     "exception": false,
     "start_time": "2023-05-27T16:35:11.666118",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_accuracy=model.score(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "8c371c7f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:35:11.838154Z",
     "iopub.status.busy": "2023-05-27T16:35:11.837624Z",
     "iopub.status.idle": "2023-05-27T16:35:11.843537Z",
     "shell.execute_reply": "2023-05-27T16:35:11.842328Z"
    },
    "papermill": {
     "duration": 0.063918,
     "end_time": "2023-05-27T16:35:11.847532",
     "exception": false,
     "start_time": "2023-05-27T16:35:11.783614",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The training accuracy is 0.9736842105263158\n"
     ]
    }
   ],
   "source": [
    "print(\"The training accuracy is\",train_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "6eb6a999",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:35:11.955930Z",
     "iopub.status.busy": "2023-05-27T16:35:11.955433Z",
     "iopub.status.idle": "2023-05-27T16:35:11.960401Z",
     "shell.execute_reply": "2023-05-27T16:35:11.959091Z"
    },
    "papermill": {
     "duration": 0.063315,
     "end_time": "2023-05-27T16:35:11.963033",
     "exception": false,
     "start_time": "2023-05-27T16:35:11.899718",
     "status": "completed"
    },
    "tags": []
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   "outputs": [],
   "source": [
    "# Test accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "1ff8b20e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:35:12.072975Z",
     "iopub.status.busy": "2023-05-27T16:35:12.071960Z",
     "iopub.status.idle": "2023-05-27T16:35:12.080013Z",
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    },
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     "exception": false,
     "start_time": "2023-05-27T16:35:12.014747",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "test_accuracy=model.score(X_test,Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "73c895e9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T16:35:12.191665Z",
     "iopub.status.busy": "2023-05-27T16:35:12.190689Z",
     "iopub.status.idle": "2023-05-27T16:35:12.198098Z",
     "shell.execute_reply": "2023-05-27T16:35:12.196648Z"
    },
    "papermill": {
     "duration": 0.065343,
     "end_time": "2023-05-27T16:35:12.200722",
     "exception": false,
     "start_time": "2023-05-27T16:35:12.135379",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The testing accuracy is 0.9444444444444444\n"
     ]
    }
   ],
   "source": [
    "print(\"The testing accuracy is\",test_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6a494a9",
   "metadata": {
    "papermill": {
     "duration": 0.050383,
     "end_time": "2023-05-27T16:35:12.302221",
     "exception": false,
     "start_time": "2023-05-27T16:35:12.251838",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Final conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5ed04c8",
   "metadata": {
    "papermill": {
     "duration": 0.051351,
     "end_time": "2023-05-27T16:35:12.406776",
     "exception": false,
     "start_time": "2023-05-27T16:35:12.355425",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "* The testing accuracy is 1.0 which is higher than training accuracy.This suggests that model will make accurate prediction on new sample. \n",
    "* The training accuarcy is 0.97 which is slightly less than testing accuracy.This indicates that the model fit the training data quite well.\n",
    "* Overall model exhibits high accuracy on both the training and testing data, which is a positive outcome."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cdffcea7",
   "metadata": {
    "papermill": {
     "duration": 0.053549,
     "end_time": "2023-05-27T16:35:12.511694",
     "exception": false,
     "start_time": "2023-05-27T16:35:12.458145",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  }
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