{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-03-19T20:47:50.665341800Z",
     "start_time": "2024-03-19T20:47:42.485262800Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: opendatasets in c:\\users\\arden\\pycharmprojects\\ium_s464980\\venv\\lib\\site-packages (0.1.22)\n",
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      "Requirement already satisfied: webencodings in c:\\users\\arden\\pycharmprojects\\ium_s464980\\venv\\lib\\site-packages (from bleach->kaggle->opendatasets) (0.5.1)\n",
      "Requirement already satisfied: text-unidecode>=1.3 in c:\\users\\arden\\pycharmprojects\\ium_s464980\\venv\\lib\\site-packages (from python-slugify->kaggle->opendatasets) (1.3)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\arden\\pycharmprojects\\ium_s464980\\venv\\lib\\site-packages (from requests->kaggle->opendatasets) (3.3.2)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\arden\\pycharmprojects\\ium_s464980\\venv\\lib\\site-packages (from requests->kaggle->opendatasets) (3.6)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "[notice] A new release of pip available: 22.3.1 -> 24.0\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas in c:\\users\\arden\\pycharmprojects\\ium_s464980\\venv\\lib\\site-packages (2.2.1)\n",
      "Requirement already satisfied: numpy<2,>=1.23.2 in c:\\users\\arden\\pycharmprojects\\ium_s464980\\venv\\lib\\site-packages (from pandas) (1.26.4)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\arden\\pycharmprojects\\ium_s464980\\venv\\lib\\site-packages (from pandas) (2.9.0.post0)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\arden\\pycharmprojects\\ium_s464980\\venv\\lib\\site-packages (from pandas) (2024.1)\n",
      "Requirement already satisfied: tzdata>=2022.7 in c:\\users\\arden\\pycharmprojects\\ium_s464980\\venv\\lib\\site-packages (from pandas) (2024.1)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\arden\\pycharmprojects\\ium_s464980\\venv\\lib\\site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "[notice] A new release of pip available: 22.3.1 -> 24.0\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: seaborn in c:\\users\\arden\\pycharmprojects\\ium_s464980\\venv\\lib\\site-packages (0.13.2)\n",
      "Requirement already satisfied: numpy!=1.24.0,>=1.20 in c:\\users\\arden\\pycharmprojects\\ium_s464980\\venv\\lib\\site-packages (from seaborn) (1.26.4)\n",
      "Requirement already satisfied: pandas>=1.2 in c:\\users\\arden\\pycharmprojects\\ium_s464980\\venv\\lib\\site-packages (from seaborn) (2.2.1)\n",
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      "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\arden\\pycharmprojects\\ium_s464980\\venv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.2.0)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\arden\\pycharmprojects\\ium_s464980\\venv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.12.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\arden\\pycharmprojects\\ium_s464980\\venv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.50.0)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in c:\\users\\arden\\pycharmprojects\\ium_s464980\\venv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.5)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\arden\\pycharmprojects\\ium_s464980\\venv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (24.0)\n",
      "Requirement already satisfied: pillow>=8 in c:\\users\\arden\\pycharmprojects\\ium_s464980\\venv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (10.2.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\arden\\pycharmprojects\\ium_s464980\\venv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.1.2)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\arden\\pycharmprojects\\ium_s464980\\venv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.9.0.post0)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\arden\\pycharmprojects\\ium_s464980\\venv\\lib\\site-packages (from pandas>=1.2->seaborn) (2024.1)\n",
      "Requirement already satisfied: tzdata>=2022.7 in c:\\users\\arden\\pycharmprojects\\ium_s464980\\venv\\lib\\site-packages (from pandas>=1.2->seaborn) (2024.1)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\arden\\pycharmprojects\\ium_s464980\\venv\\lib\\site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "[notice] A new release of pip available: 22.3.1 -> 24.0\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: scikit-learn in c:\\users\\arden\\pycharmprojects\\ium_s464980\\venv\\lib\\site-packages (1.4.1.post1)\n",
      "Requirement already satisfied: numpy<2.0,>=1.19.5 in c:\\users\\arden\\pycharmprojects\\ium_s464980\\venv\\lib\\site-packages (from scikit-learn) (1.26.4)\n",
      "Requirement already satisfied: scipy>=1.6.0 in c:\\users\\arden\\pycharmprojects\\ium_s464980\\venv\\lib\\site-packages (from scikit-learn) (1.12.0)\n",
      "Requirement already satisfied: joblib>=1.2.0 in c:\\users\\arden\\pycharmprojects\\ium_s464980\\venv\\lib\\site-packages (from scikit-learn) (1.3.2)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\arden\\pycharmprojects\\ium_s464980\\venv\\lib\\site-packages (from scikit-learn) (3.3.0)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "[notice] A new release of pip available: 22.3.1 -> 24.0\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
     ]
    }
   ],
   "source": [
    "!pip install opendatasets\n",
    "!pip install pandas\n",
    "!pip install seaborn\n",
    "!pip install scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "outputs": [],
   "source": [
    "import opendatasets as od\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import train_test_split"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-19T20:47:50.666770400Z",
     "start_time": "2024-03-19T20:47:50.658428100Z"
    }
   },
   "id": "93a5ba2f9a7b31"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "od.download(\"https://www.kaggle.com/datasets/nikhil7280/student-performance-multiple-linear-regression/code\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2024-03-19T19:43:55.173000400Z"
    }
   },
   "id": "588b4898babf70ab"
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"student-performance-multiple-linear-regression/Student_Performance.csv\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-19T20:47:50.677033600Z",
     "start_time": "2024-03-19T20:47:50.661824200Z"
    }
   },
   "id": "38e0dd5adceeabd5"
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "outputs": [
    {
     "data": {
      "text/plain": "   Hours Studied  Previous Scores Extracurricular Activities  Sleep Hours  \\\n0              7               99                        Yes            9   \n1              4               82                         No            4   \n2              8               51                        Yes            7   \n3              5               52                        Yes            5   \n4              7               75                         No            8   \n\n   Sample Question Papers Practiced  Performance Index  \n0                                 1               91.0  \n1                                 2               65.0  \n2                                 2               45.0  \n3                                 2               36.0  \n4                                 5               66.0  ",
      "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>Hours Studied</th>\n      <th>Previous Scores</th>\n      <th>Extracurricular Activities</th>\n      <th>Sleep Hours</th>\n      <th>Sample Question Papers Practiced</th>\n      <th>Performance Index</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>7</td>\n      <td>99</td>\n      <td>Yes</td>\n      <td>9</td>\n      <td>1</td>\n      <td>91.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>4</td>\n      <td>82</td>\n      <td>No</td>\n      <td>4</td>\n      <td>2</td>\n      <td>65.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>8</td>\n      <td>51</td>\n      <td>Yes</td>\n      <td>7</td>\n      <td>2</td>\n      <td>45.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>5</td>\n      <td>52</td>\n      <td>Yes</td>\n      <td>5</td>\n      <td>2</td>\n      <td>36.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>7</td>\n      <td>75</td>\n      <td>No</td>\n      <td>8</td>\n      <td>5</td>\n      <td>66.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-19T20:47:50.689553400Z",
     "start_time": "2024-03-19T20:47:50.673502800Z"
    }
   },
   "id": "df598eeed0ebd2d4"
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "outputs": [
    {
     "data": {
      "text/plain": "(10000, 6)"
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-19T20:47:50.690555200Z",
     "start_time": "2024-03-19T20:47:50.681856700Z"
    }
   },
   "id": "446893ffa34c6f4c"
  },
  {
   "cell_type": "markdown",
   "source": [
    "Remove duplicates"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "7addf40a8a02f327"
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "outputs": [],
   "source": [
    "data.drop_duplicates(inplace=True)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-19T20:47:50.727432900Z",
     "start_time": "2024-03-19T20:47:50.686556400Z"
    }
   },
   "id": "fbde2e19a3ac1408"
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "outputs": [
    {
     "data": {
      "text/plain": "(9873, 6)"
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-19T20:47:50.728434300Z",
     "start_time": "2024-03-19T20:47:50.693387600Z"
    }
   },
   "id": "603085c31658e13c"
  },
  {
   "cell_type": "markdown",
   "source": [
    "Change Extra Activities column to int"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "673f2c5f531a6c7a"
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Arden\\AppData\\Local\\Temp\\ipykernel_9200\\3312621466.py:1: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  data[\"Extracurricular Activities\"] = data[\"Extracurricular Activities\"].replace({'Yes': 1, 'No': 0})\n"
     ]
    }
   ],
   "source": [
    "data[\"Extracurricular Activities\"] = data[\"Extracurricular Activities\"].replace({'Yes': 1, 'No': 0})"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-19T20:47:50.816169300Z",
     "start_time": "2024-03-19T20:47:50.697956300Z"
    }
   },
   "id": "a020422ccbfc5acd"
  },
  {
   "cell_type": "markdown",
   "source": [
    "Data exploration"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "ebab4c7c55f4fd61"
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "outputs": [
    {
     "data": {
      "text/plain": "dtype('int64')"
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hours_studied = data[\"Hours Studied\"]\n",
    "hours_studied.dtype"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-19T20:47:50.816169300Z",
     "start_time": "2024-03-19T20:47:50.708855100Z"
    }
   },
   "id": "c0a98b817955cdde"
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "outputs": [
    {
     "data": {
      "text/plain": "<Axes: xlabel='Hours Studied', ylabel='Count'>"
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(hours_studied)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-19T20:47:51.005628100Z",
     "start_time": "2024-03-19T20:47:50.715163500Z"
    }
   },
   "id": "3213d26b44ed8665"
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "outputs": [
    {
     "data": {
      "text/plain": "count    9873.000000\nmean        4.992100\nstd         2.589081\nmin         1.000000\n25%         3.000000\n50%         5.000000\n75%         7.000000\nmax         9.000000\nName: Hours Studied, dtype: float64"
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hours_studied.describe()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-19T20:47:51.005628100Z",
     "start_time": "2024-03-19T20:47:50.879854600Z"
    }
   },
   "id": "fc571334b0d4151e"
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "outputs": [
    {
     "data": {
      "text/plain": "dtype('int64')"
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "previous_score = data[\"Previous Scores\"]\n",
    "previous_score.dtype"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-19T20:47:51.006627100Z",
     "start_time": "2024-03-19T20:47:50.888097300Z"
    }
   },
   "id": "9b1d2f202a5a166e"
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "outputs": [
    {
     "data": {
      "text/plain": "<Axes: xlabel='Previous Scores', ylabel='Count'>"
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(previous_score)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-19T20:47:51.191680400Z",
     "start_time": "2024-03-19T20:47:50.895262500Z"
    }
   },
   "id": "8b9df4e8cd322bf9"
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "outputs": [
    {
     "data": {
      "text/plain": "dtype('int64')"
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extra_activities = data['Extracurricular Activities']\n",
    "extra_activities.dtype"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-19T20:47:51.191680400Z",
     "start_time": "2024-03-19T20:47:51.164905200Z"
    }
   },
   "id": "d1d8315c0a61ad1c"
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "outputs": [
    {
     "data": {
      "text/plain": "<Axes: xlabel='Extracurricular Activities', ylabel='Count'>"
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(extra_activities)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-19T20:47:51.360250800Z",
     "start_time": "2024-03-19T20:47:51.169924800Z"
    }
   },
   "id": "5bbd49fe8b2af15f"
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "outputs": [
    {
     "data": {
      "text/plain": "dtype('int64')"
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sleep_hours = data['Sleep Hours']\n",
    "sleep_hours.dtype"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-19T20:47:51.361251600Z",
     "start_time": "2024-03-19T20:47:51.308469800Z"
    }
   },
   "id": "e06376bb69bb207"
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "outputs": [
    {
     "data": {
      "text/plain": "<Axes: xlabel='Sleep Hours', ylabel='Count'>"
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(sleep_hours)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-19T20:47:51.497557500Z",
     "start_time": "2024-03-19T20:47:51.315580300Z"
    }
   },
   "id": "9e55d8f721fa111b"
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "outputs": [
    {
     "data": {
      "text/plain": "dtype('int64')"
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samples_practised = data[\"Sample Question Papers Practiced\"]\n",
    "samples_practised.dtype"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-19T20:47:51.710548400Z",
     "start_time": "2024-03-19T20:47:51.655084800Z"
    }
   },
   "id": "460fae1c195823e7"
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "outputs": [
    {
     "data": {
      "text/plain": "<Axes: xlabel='Sample Question Papers Practiced', ylabel='Count'>"
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(samples_practised)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-19T20:47:52.306173900Z",
     "start_time": "2024-03-19T20:47:52.167113100Z"
    }
   },
   "id": "4754d54a39bf0ca0"
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "outputs": [
    {
     "data": {
      "text/plain": "dtype('float64')"
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "performance = data[\"Performance Index\"]\n",
    "performance.dtype"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-19T20:47:52.736690500Z",
     "start_time": "2024-03-19T20:47:52.725789700Z"
    }
   },
   "id": "55d7f787f866eeb8"
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "outputs": [
    {
     "data": {
      "text/plain": "<Axes: xlabel='Performance Index', ylabel='Count'>"
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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V448/rqKiIr3//vu69dZbNXbsWO3fv1+SNHPmTP3lL3/RK6+8oq1bt+r48eMaN26cZ3xTU5PGjBmj+vp67dixQ7/97W+1evVqPfroo4E6JAAAEGQCOmfnjjvu8Hq/bNkyPf/889q5c6dSU1P14osvau3atbr11lslSfn5+erfv7927typ4cOH65133lFxcbE2bdoku92uQYMGaenSpZo7d64WLVqk8PDw836u2+2W2+32vHe5XP47SAAAEFBBM2enqalJ69atU01NjRwOh4qKitTQ0KDMzExPn379+iktLU2FhYWSpMLCQg0YMEB2u93TJysrSy6Xy3N16HyWL1+uhIQEz6tHjx7+OzAAABBQAQ87H3/8sWJjYxUREaEf/OAHevXVV5WRkaHy8nKFh4crMTHRq7/dbld5ebkkqby83CvonG0/23Yh8+bN0+nTpz2vo0ePtu1BAQCAoBHwW8/79u2rPXv26PTp0/rjH/+onJwcbd261a+fGRERoYiICL9+BtpWWVmZnE6nz+OSkpKUmprqh4oAAB1Fq8LOFVdcod27dyspKclr+6lTp3Tdddfps88+a/G+wsPDddVVV0mShgwZot27d+t//ud/NGHCBNXX1+vUqVNeV3cqKiqUkpIiSUpJSdF7773ntb+zd2ud7YOOr6ysTH369lPtmRqfx0ZFx+hQyUECDwBcwloVdv7xj3+oqencFTbdbreOHTt2UQU1NzfL7XZryJAhCgsLU0FBgcaPHy/py+e2lJaWyuFwSJIcDoeWLVumyspKJScnS5I2btyo+Ph4ZWRkXFQdCB5Op1O1Z2rkmLxUcfa0Fo+rqihV4aoFcjqdhB0AuIT5FHZef/11z5/ffvttJSQkeN43NTWpoKBAPXv2bPH+5s2bp9tvv11paWmqqqrS2rVrtWXLFs++J02apFmzZqlLly6Kj4/XtGnT5HA4NHz4cEnS6NGjlZGRoYkTJ2rFihUqLy/X/PnzlZuby89UFhRnT/NpNVgAACQfw86dd94pSbLZbMrJyfFqCwsLU8+ePfXUU0+1eH+VlZW65557dOLECSUkJGjgwIF6++239c1vflOS9Mwzz6hTp04aP3683G63srKytHLlSs/4kJAQbdiwQVOmTJHD4VBMTIxycnK0ZMkSXw4LAABYmE9hp7m5WZLUq1cv7d69W5dddtlFffiLL774te2RkZHKy8tTXl7eBfukp6frzTffvKg6AACAdbVqzs6RI0faug4AAAC/aPWt5wUFBSooKFBlZaXnis9Zv/nNby66MAAAgLbQqrCzePFiLVmyREOHDlW3bt1ks9naui4AAIA20aqw88ILL2j16tWaOHFiW9cDAADQplr1uIj6+nqNGDGirWsBAABoc60KOw888IDWrl3b1rUAAAC0uVb9jFVXV6df/epX2rRpkwYOHKiwsDCv9qeffrpNigMAALhYrQo7e/fu1aBBgyRJ+/bt82pjsjIAAAgmrQo77777blvXAQAA4BetmrMDAADQUbTqys4tt9zytT9Xbd68udUFAQAAtKVWhZ2z83XOamho0J49e7Rv375zHhAKAAAQSK0KO88888x5ty9atEjV1dUXVRCsr6ysTE6ns8X9S0pK/FgNAMDqWv1srPP5/ve/rxtuuEE/+9nP2nK3sJCysjL16dtPtWdqfB7b0FDvh4oAAFbXpmGnsLBQkZGRbblLWIzT6VTtmRo5Ji9VnD2tRWPKi3dp7/qVamxs8nN1AAAralXYGTdunNd7Y4xOnDih999/XwsWLGiTwmBtcfY0de7Ru0V9qypK/VwNAMDKWhV2EhISvN536tRJffv21ZIlSzR69Og2KQwAAKAttCrs5Ofnt3UdAAAAfnFRc3aKiop04MABSdLVV1+twYMHt0lRAAAAbaVVYaeyslJ33XWXtmzZosTEREnSqVOndMstt2jdunXq2rVrW9YIAADQaq0KO9OmTVNVVZX279+v/v37S5KKi4uVk5Ojhx56SC+99FKbFgkAQKC0Zq2vpKQkpaam+qEatEarws5bb72lTZs2eYKOJGVkZCgvL48JygAAS6hznZRsNk2YMMHnsVHRMTpUcpDAEyRaFXaam5sVFhZ2zvawsDA1NzdfdFEAAARaQ221ZIwGT5yvruktWypD+nK5jMJVC+R0Ogk7QaJVYefWW2/V9OnT9dJLL6l79+6SpGPHjmnmzJkaNWpUmxYIAEAgxSantnhdMASnTq0Z9Itf/EIul0s9e/bUlVdeqSuvvFK9evWSy+XSz3/+87auEQAAoNVadWWnR48e+uCDD7Rp0yYdPHhQktS/f39lZma2aXEAAAAXy6crO5s3b1ZGRoZcLpdsNpu++c1vatq0aZo2bZquv/56XX311frb3/7mr1oBAAB85lPYefbZZzV58mTFx8ef05aQkKD//u//1tNPP91mxQEAAFwsn8LORx99pNtuu+2C7aNHj1ZRUdFFFwUAANBWfAo7FRUV573l/KzQ0FB98cUXF10UAABAW/Ep7Fx++eXat2/fBdv37t2rbt26XXRRAAAAbcWnsPOtb31LCxYsUF1d3TlttbW1Wrhwob797W+3WXEAAAAXy6dbz+fPn6/169erT58+mjp1qvr27StJOnjwoPLy8tTU1KRHHnnEL4UCAAC0hk9hx263a8eOHZoyZYrmzZsnY4wkyWazKSsrS3l5ebLb7X4pFAAAoDV8XlQwPT1db775pv75z3/q8OHDMsaod+/e6ty5sz/qAwAAuCitWkFZkjp37qzrr7++LWsBAABoc616NhYAAEBHQdgBAACW1uqfsQAACJSSkhK/9IU1EXYAAB1GneukZLNpwoQJPo9taKj3Q0XoCAg7AIAOo6G2WjJGgyfOV9f03i0aU168S3vXr1RjY5Ofq0OwIuwAADqc2ORUde7RsrBTVVHq52oQ7JigDAAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI11dmB5vi4Vz9LyAGAthB1Y1sUsKy+xtDwAWAVhB5bVmmXlJZaWBwCrIezA8nxZVl5iaXkAsBomKAMAAEsj7AAAAEsj7AAAAEtjzg4AAH7g6zIWSUlJSk1N9VM1l7aAhp3ly5dr/fr1OnjwoKKiojRixAg98cQT6tu3r6dPXV2dfvSjH2ndunVyu93KysrSypUrZbfbPX1KS0s1ZcoUvfvuu4qNjVVOTo6WL1+u0FCyHACgfbV22Yuo6BgdKjlI4PGDgKaBrVu3Kjc3V9dff70aGxv1k5/8RKNHj1ZxcbFiYmIkSTNnztQbb7yhV155RQkJCZo6darGjRun7du3S5Kampo0ZswYpaSkaMeOHTpx4oTuuecehYWF6bHHHgvk4QEALkGtWfaiqqJUhasWyOl0Enb8IKBh56233vJ6v3r1aiUnJ6uoqEj/8R//odOnT+vFF1/U2rVrdeutt0qS8vPz1b9/f+3cuVPDhw/XO++8o+LiYm3atEl2u12DBg3S0qVLNXfuXC1atEjh4eGBODQAwCXO12Uv4D9BNUH59OnTkqQuXbpIkoqKitTQ0KDMzExPn379+iktLU2FhYWSpMLCQg0YMMDrZ62srCy5XC7t37//vJ/jdrvlcrm8XgAAwJqCJuw0NzdrxowZGjlypK655hpJUnl5ucLDw5WYmOjV1263q7y83NPnq0HnbPvZtvNZvny5EhISPK8ePXq08dEAAIBgETRhJzc3V/v27dO6dev8/lnz5s3T6dOnPa+jR4/6/TMBAEBgBMXtSlOnTtWGDRu0bds2r4lZKSkpqq+v16lTp7yu7lRUVCglJcXT57333vPaX0VFhaftfCIiIhQREdHGRwEAAIJRQK/sGGM0depUvfrqq9q8ebN69erl1T5kyBCFhYWpoKDAs62kpESlpaVyOBySJIfDoY8//liVlZWePhs3blR8fLwyMjLa50AAAEDQCuiVndzcXK1du1Z//vOfFRcX55ljk5CQoKioKCUkJGjSpEmaNWuWunTpovj4eE2bNk0Oh0PDhw+XJI0ePVoZGRmaOHGiVqxYofLycs2fP1+5ublcvQEAAIENO88//7wk6Rvf+IbX9vz8fN17772SpGeeeUadOnXS+PHjvRYVPCskJEQbNmzQlClT5HA4FBMTo5ycHC1ZsqS9DgMAAASxgIYdY8y/7RMZGam8vDzl5eVdsE96errefPPNtiwNAABYRNDcjQUAAOAPhB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBpoYEuAB1XWVmZnE6nT2NKSkr8VA0AAOdH2EGrlJWVqU/ffqo9U9Oq8Q0N9W1cEQAA50fYQas4nU7VnqmRY/JSxdnTWjyuvHiX9q5fqcbGJj9WBwDA/yHs4KLE2dPUuUfvFvevqij1YzUAAJyLCcoAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSQgNdANpWWVmZnE6nT2OSkpKUmprqp4oAAAgswo6FlJWVqU/ffqo9U+PTuKjoGB0qOUjgAQBYEmHHQpxOp2rP1Mgxeani7GktGlNVUarCVQvkdDoJOwAASyLsWFCcPU2de/QOdBkAAAQFJigDAABLI+wAAABLI+wAAABLI+wAAABLC2jY2bZtm+644w51795dNptNr732mle7MUaPPvqounXrpqioKGVmZuqTTz7x6nPy5EllZ2crPj5eiYmJmjRpkqqrq9vxKAAAQDAL6N1YNTU1uvbaa3X//fdr3Lhx57SvWLFCzz33nH7729+qV69eWrBggbKyslRcXKzIyEhJUnZ2tk6cOKGNGzeqoaFB9913nx588EGtXbu2vQ8HAICLUlJS4vMYFob99wIadm6//Xbdfvvt520zxujZZ5/V/PnzNXbsWEnS//7v/8put+u1117TXXfdpQMHDuitt97S7t27NXToUEnSz3/+c33rW9/Sz372M3Xv3v28+3a73XK73Z73LperjY8MAICWq3OdlGw2TZgwweexLAz77wXtOjtHjhxReXm5MjMzPdsSEhI0bNgwFRYW6q677lJhYaESExM9QUeSMjMz1alTJ+3atUvf/e53z7vv5cuXa/HixX4/BgAAWqKhtloyRoMnzlfX9Javk8bCsC0TtGGnvLxckmS327222+12T1t5ebmSk5O92kNDQ9WlSxdPn/OZN2+eZs2a5XnvcrnUo0ePtiodAIBWiU1OZVFYPwjasONPERERioiICHQZAACgHQTtrecpKSmSpIqKCq/tFRUVnraUlBRVVlZ6tTc2NurkyZOePgAA4NIWtGGnV69eSklJUUFBgWeby+XSrl275HA4JEkOh0OnTp1SUVGRp8/mzZvV3NysYcOGtXvNAAAg+AT0Z6zq6modPnzY8/7IkSPas2ePunTporS0NM2YMUM//elP1bt3b8+t5927d9edd94pSerfv79uu+02TZ48WS+88IIaGho0depU3XXXXRe8Ewvn5+vtjq25PRIA4B/csv71Ahp23n//fd1yyy2e92cnDefk5Gj16tWaM2eOampq9OCDD+rUqVO68cYb9dZbb3nW2JGkNWvWaOrUqRo1apQ6deqk8ePH67nnnmv3Y+moLuZ2R0lqaKhv44oAAC3FLestE9Cw841vfEPGmAu222w2LVmyREuWLLlgny5durCA4EVo7e2O5cW7tHf9SjU2NvmxOgDA1+GW9Za5JO/Gwrl8vd2xqqLUj9UAAHzBLetfL2gnKAMAALQFwg4AALA0wg4AALA0wg4AALA0wg4AALA0wg4AALA0wg4AALA0wg4AALA0wg4AALA0wg4AALA0wg4AALA0wg4AALA0wg4AALA0wg4AALA0wg4AALA0wg4AALA0wg4AALA0wg4AALA0wg4AALA0wg4AALC00EAXAAAAAqOkpMSn/klJSUpNTfVTNf5D2AEA4BJT5zop2WyaMGGCT+OiomN0qORghws8hB0AAC4xDbXVkjEaPHG+uqb3btGYqopSFa5aIKfTSdgBAAAdQ2xyqjr3aFnY6ciYoAwAACyNsAMAACyNsAMAACyNOTtBqqysTE6n06cxvt5CCADApYCwE4TKysrUp28/1Z6padX4hob6Nq4IAICOi7AThJxOp2rP1Mgxeani7GktHldevEt7169UY2OTH6sDAKBjIewEsTh7mk+3BFZVlPqxGgAAOiYmKAMAAEsj7AAAAEsj7AAAAEsj7AAAAEsj7AAAAEsj7AAAAEsj7AAAAEsj7AAAAEsj7AAAAEsj7AAAAEsj7AAAAEsj7AAAAEsj7AAAAEsj7AAAAEsj7AAAAEsj7AAAAEsj7AAAAEsLDXQBAACg4ygpKfF5TFJSklJTU/1QTcsQdgAAwL9V5zop2WyaMGGCz2OjomN0qORgwAIPYcfPysrK5HQ6fRrTmtQMAIA/NdRWS8Zo8MT56preu8XjqipKVbhqgZxOJ2HHisrKytSnbz/Vnqlp1fiGhvo2rggAgIsTm5yqzj1aHnaCAWHHj5xOp2rP1Mgxeani7GktHldevEt7169UY2OTH6sDAODSQNhpB3H2NJ9ScFVFqR+rAQDg0sKt5wAAwNIIOwAAwNIsE3by8vLUs2dPRUZGatiwYXrvvfcCXRIAAAgClgg7L7/8smbNmqWFCxfqgw8+0LXXXqusrCxVVlYGujQAABBglgg7Tz/9tCZPnqz77rtPGRkZeuGFFxQdHa3f/OY3gS4NAAAEWIe/G6u+vl5FRUWaN2+eZ1unTp2UmZmpwsLC845xu91yu92e96dPn5YkuVyuNq2turpakvTPo4fU6K5t8ThX+edf1lX2qcJ8iKOtGdeen0WNl06NfB/UGEzjqDGwNVZVHpX05d+Jbf337Nn9GWO+vqPp4I4dO2YkmR07dnhtnz17trnhhhvOO2bhwoVGEi9evHjx4sXLAq+jR49+bVbo8Fd2WmPevHmaNWuW531zc7NOnjyppKQk2Wy2AFYWnFwul3r06KGjR48qPj4+0OVAnJNgw/kILpyP4OLP82GMUVVVlbp37/61/Tp82LnssssUEhKiiooKr+0VFRVKSUk575iIiAhFRER4bUtMTPRXiZYRHx/P/3EEGc5JcOF8BBfOR3Dx1/lISEj4t306/ATl8PBwDRkyRAUFBZ5tzc3NKigokMPhCGBlAAAgGHT4KzuSNGvWLOXk5Gjo0KG64YYb9Oyzz6qmpkb33XdfoEsDAAABZomwM2HCBH3xxRd69NFHVV5erkGDBumtt96S3W4PdGmWEBERoYULF57z0x8Ch3MSXDgfwYXzEVyC4XzYjPl392sBAAB0XB1+zg4AAMDXIewAAABLI+wAAABLI+wAAABLI+zAY/ny5br++usVFxen5ORk3XnnnSopKfHqU1dXp9zcXCUlJSk2Nlbjx48/Z0FHtL3HH39cNptNM2bM8GzjXLS/Y8eO6fvf/76SkpIUFRWlAQMG6P333/e0G2P06KOPqlu3boqKilJmZqY++eSTAFZsXU1NTVqwYIF69eqlqKgoXXnllVq6dKnXM5I4H/6zbds23XHHHerevbtsNptee+01r/aWfPcnT55Udna24uPjlZiYqEmTJnmeKdnWCDvw2Lp1q3Jzc7Vz505t3LhRDQ0NGj16tGpqajx9Zs6cqb/85S965ZVXtHXrVh0/flzjxo0LYNXWt3v3bv3yl7/UwIEDvbZzLtrXP//5T40cOVJhYWH661//quLiYj311FPq3Lmzp8+KFSv03HPP6YUXXtCuXbsUExOjrKws1dXVBbBya3riiSf0/PPP6xe/+IUOHDigJ554QitWrNDPf/5zTx/Oh//U1NTo2muvVV5e3nnbW/LdZ2dna//+/dq4caM2bNigbdu26cEHH/RPwRf/KE5YVWVlpZFktm7daowx5tSpUyYsLMy88sornj4HDhwwkkxhYWGgyrS0qqoq07t3b7Nx40Zz8803m+nTpxtjOBeBMHfuXHPjjTdesL25udmkpKSYJ5980rPt1KlTJiIiwrz00kvtUeIlZcyYMeb+++/32jZu3DiTnZ1tjOF8tCdJ5tVXX/W8b8l3X1xcbCSZ3bt3e/r89a9/NTabzRw7dqzNa+TKDi7o9OnTkqQuXbpIkoqKitTQ0KDMzExPn379+iktLU2FhYUBqdHqcnNzNWbMGK/vXOJcBMLrr7+uoUOH6j//8z+VnJyswYMHa9WqVZ72I0eOqLy83OucJCQkaNiwYZwTPxgxYoQKCgp06NAhSdJHH32kv//977r99tslcT4CqSXffWFhoRITEzV06FBPn8zMTHXq1Em7du1q85ossYIy2l5zc7NmzJihkSNH6pprrpEklZeXKzw8/JyHptrtdpWXlwegSmtbt26dPvjgA+3evfucNs5F+/vss8/0/PPPa9asWfrJT36i3bt366GHHlJ4eLhycnI83/u/rtzOOfGPH//4x3K5XOrXr59CQkLU1NSkZcuWKTs7W5I4HwHUku++vLxcycnJXu2hoaHq0qWLX84PYQfnlZubq3379unvf/97oEu5JB09elTTp0/Xxo0bFRkZGehyoC//ATB06FA99thjkqTBgwdr3759euGFF5STkxPg6i49f/jDH7RmzRqtXbtWV199tfbs2aMZM2aoe/funA+cg5+xcI6pU6dqw4YNevfdd5WamurZnpKSovr6ep06dcqrf0VFhVJSUtq5SmsrKipSZWWlrrvuOoWGhio0NFRbt27Vc889p9DQUNntds5FO+vWrZsyMjK8tvXv31+lpaWS5Pne//WOOM6Jf8yePVs//vGPddddd2nAgAGaOHGiZs6cqeXLl0vifARSS777lJQUVVZWerU3Njbq5MmTfjk/hB14GGM0depUvfrqq9q8ebN69erl1T5kyBCFhYWpoKDAs62kpESlpaVyOBztXa6ljRo1Sh9//LH27NnjeQ0dOlTZ2dmeP3Mu2tfIkSPPWYrh0KFDSk9PlyT16tVLKSkpXufE5XJp165dnBM/OHPmjDp18v4rLCQkRM3NzZI4H4HUku/e4XDo1KlTKioq8vTZvHmzmpubNWzYsLYvqs2nPKPDmjJliklISDBbtmwxJ06c8LzOnDnj6fODH/zApKWlmc2bN5v333/fOBwO43A4Alj1peOrd2MZw7lob++9954JDQ01y5YtM5988olZs2aNiY6ONr///e89fR5//HGTmJho/vznP5u9e/easWPHml69epna2toAVm5NOTk55vLLLzcbNmwwR44cMevXrzeXXXaZmTNnjqcP58N/qqqqzIcffmg+/PBDI8k8/fTT5sMPPzSff/65MaZl3/1tt91mBg8ebHbt2mX+/ve/m969e5u7777bL/USduAh6byv/Px8T5/a2lrzwx/+0HTu3NlER0eb7373u+bEiROBK/oS8q9hh3PR/v7yl7+Ya665xkRERJh+/fqZX/3qV17tzc3NZsGCBcZut5uIiAgzatQoU1JSEqBqrc3lcpnp06ebtLQ0ExkZaa644grzyCOPGLfb7enD+fCfd99997x/X+Tk5BhjWvbdO51Oc/fdd5vY2FgTHx9v7rvvPlNVVeWXem3GfGW5SQAAAIthzg4AALA0wg4AALA0wg4AALA0wg4AALA0wg4AALA0wg4AALA0wg4AALA0wg4AALA0wg6AVlu0aJHsdrtsNptee+21QJdjKYsWLdKgQYMCXQZgCYQdwOLuvfde2Ww22Ww2hYeH66qrrtKSJUvU2Nh4Ufs9cOCAFi9erF/+8pc6ceKEbr/99jaquGP7xz/+IZvNpj179gS6FAD/v9BAFwDA/2677Tbl5+fL7XbrzTffVG5ursLCwjRv3jyf99XU1CSbzaZPP/1UkjR27FjZbLZW19bQ0KCwsLBWjweAf4crO8AlICIiQikpKUpPT9eUKVOUmZmp119/XZLkdrv18MMP6/LLL1dMTIyGDRumLVu2eMauXr1aiYmJev3115WRkaGIiAjdf//9uuOOOyRJnTp18oSd5uZmLVmyRKmpqYqIiNCgQYP01ltvefZ19qrHyy+/rJtvvlmRkZFas2aN7r33Xt1555167LHHZLfblZiY6Ln6NHv2bHXp0kWpqanKz8/3Oq65c+eqT58+io6O1hVXXKEFCxaooaHB0372p6Df/e536tmzpxISEnTXXXepqqrK06e5uVkrVqzQVVddpYiICKWlpWnZsmWe9qNHj+q//uu/lJiYqC5dumjs2LH6xz/+0eLvfsuWLbLZbCooKNDQoUMVHR2tESNGqKSkxKvf448/Lrvdrri4OE2aNEl1dXXn7OvXv/61+vfvr8jISPXr108rV670tN1///0aOHCg3G63JKm+vl6DBw/WPffc0+JaAcvyy+NFAQSNnJwcM3bsWK9t3/nOd8x1111njDHmgQceMCNGjDDbtm0zhw8fNk8++aSJiIgwhw4dMsYYk5+fb8LCwsyIESPM9u3bzcGDB83p06dNfn6+kWROnDjhedr6008/beLj481LL71kDh48aObMmWPCwsI8+zpy5IiRZHr27Gn+9Kc/mc8++8wcP37c5OTkmLi4OJObm2sOHjxoXnzxRSPJZGVlmWXLlplDhw6ZpUuXmrCwMHP06FHPcSxdutRs377dHDlyxLz++uvGbrebJ554wtO+cOFCExsba8aNG2c+/vhjs23bNpOSkmJ+8pOfePrMmTPHdO7c2axevdocPnzY/O1vfzOrVq0yxhhTX19v+vfvb+6//36zd+9eU1xcbL73ve+Zvn37ej1d+6vOHuOHH35ojPm/p0MPGzbMbNmyxezfv9/cdNNNZsSIEZ4xL7/8somIiDC//vWvzcGDB80jjzxi4uLizLXXXuvp8/vf/95069bN87396U9/Ml26dDGrV682xhhTVVVlrrjiCjNjxgxjjDEPP/yw6dmzpzl9+nTL/ocCWBhhB7C4r4ad5uZms3HjRhMREWEefvhh8/nnn5uQkBBz7NgxrzGjRo0y8+bNM8YYT6jZs2ePV59XX33V/Ou/l7p3726WLVvmte366683P/zhD40x/xcEnn322XNqTE9PN01NTZ5tffv2NTfddJPnfWNjo4mJiTEvvfTSBY/1ySefNEOGDPG8X7hwoYmOjjYul8uzbfbs2WbYsGHGGGNcLpeJiIjwhJt/9bvf/c707dvXNDc3e7a53W4TFRVl3n777fOOuVDY2bRpk6fPG2+8YSSZ2tpaY4wxDofD8x2dNWzYMK+wc+WVV5q1a9d69Vm6dKlxOBye9zt27DBhYWFmwYIFJjQ01Pztb387b43ApYY5O8AlYMOGDYqNjVVDQ4Oam5v1ve99T4sWLdKWLVvU1NSkPn36ePV3u91KSkryvA8PD9fAgQO/9jNcLpeOHz+ukSNHem0fOXKkPvroI69tQ4cOPWf81VdfrU6d/u+XdbvdrmuuucbzPiQkRElJSaqsrPRse/nll/Xcc8/p008/VXV1tRobGxUfH++13549eyouLs7zvlu3bp59HDhwQG63W6NGjTrvMX300Uc6fPiw13hJqqur88xZaqmvfn/dunWTJFVWViotLU0HDhzQD37wA6/+DodD7777riSppqZGn376qSZNmqTJkyd7+jQ2NiohIcFrzMMPP6ylS5dq7ty5uvHGG32qEbAqwg5wCbjlllv0/PPPKzw8XN27d1do6Jf/6VdXVyskJERFRUUKCQnxGhMbG+v5c1RU1EVNQv5XMTEx52z710nKNpvtvNuam5slSYWFhcrOztbixYuVlZWlhIQErVu3Tk899dS/3e/ZfURFRX1tndXV1RoyZIjWrFlzTlvXrl2/duy/+modX53j1BLV1dWSpFWrVmnYsGFebV89b83Nzdq+fbtCQkJ0+PBhn+oDrIywA1wCYmJidNVVV52zffDgwWpqalJlZaVuuummi/qM+Ph4de/eXdu3b9fNN9/s2b59+3bdcMMNF7Xv89mxY4fS09P1yCOPeLZ9/vnnPu2jd+/eioqKUkFBgR544IFz2q+77jq9/PLLSk5OPueKUVvq37+/du3a5TWZeOfOnZ4/2+12de/eXZ999pmys7MvuJ8nn3xSBw8e1NatW5WVlaX8/Hzdd999fqsb6CgIO8AlrE+fPsrOztY999yjp556SoMHD9YXX3yhgoICDRw4UGPGjPFpf7Nnz9bChQt15ZVXatCgQcrPz9eePXvOe2XkYvXu3VulpaVat26drr/+er3xxht69dVXfdpHZGSk5s6dqzlz5ig8PFwjR47UF198of3792vSpEnKzs7Wk08+qbFjx3ruMvv888+1fv16zZkzR6mpqW1yLNOnT9e9996roUOHauTIkVqzZo3279+vK664wtNn8eLFeuihh5SQkKDbbrtNbrdb77//vv75z39q1qxZ+vDDD/Xoo4/qj3/8o0aOHKmnn35a06dP18033+y1H+BSRNgBLnH5+fn66U9/qh/96Ec6duyYLrvsMg0fPlzf/va3fd7XQw89pNOnT+tHP/qRKisrlZGRoddff129e/du87q/853vaObMmZo6darcbrfGjBmjBQsWaNGiRT7tZ8GCBQoNDdWjjz6q48ePq1u3bp75M9HR0dq2bZvmzp2rcePGqaqqSpdffrlGjRrVpld6JkyYoE8//VRz5sxRXV2dxo8frylTpujtt9/29HnggQcUHR2tJ598UrNnz1ZMTIwGDBigGTNmqK6uTt///vd17733epYEePDBB/XGG29o4sSJ2rZt2zk/UwKXEpsxxgS6CAAAAH9hUUEAAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBp/w9Q56brEl8G2gAAAABJRU5ErkJggg=="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(performance)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-19T20:47:53.502279800Z",
     "start_time": "2024-03-19T20:47:53.265530600Z"
    }
   },
   "id": "574f01964c2eb8d5"
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "outputs": [
    {
     "data": {
      "text/plain": "<Axes: xlabel='Previous Scores', ylabel='Performance Index'>"
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=previous_score,y=performance)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-19T20:47:53.888899300Z",
     "start_time": "2024-03-19T20:47:53.728101600Z"
    }
   },
   "id": "df46569402b86850"
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "outputs": [
    {
     "data": {
      "text/plain": "                                   count       mean        std   min   25%  \\\nHours Studied                     9873.0   4.992100   2.589081   1.0   3.0   \nPrevious Scores                   9873.0  69.441102  17.325601  40.0  54.0   \nExtracurricular Activities        9873.0   0.494986   0.500000   0.0   0.0   \nSleep Hours                       9873.0   6.531652   1.697683   4.0   5.0   \nSample Question Papers Practiced  9873.0   4.583004   2.867202   0.0   2.0   \nPerformance Index                 9873.0  55.216651  19.208570  10.0  40.0   \n\n                                   50%   75%    max  \nHours Studied                      5.0   7.0    9.0  \nPrevious Scores                   69.0  85.0   99.0  \nExtracurricular Activities         0.0   1.0    1.0  \nSleep Hours                        7.0   8.0    9.0  \nSample Question Papers Practiced   5.0   7.0    9.0  \nPerformance Index                 55.0  70.0  100.0  ",
      "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>count</th>\n      <th>mean</th>\n      <th>std</th>\n      <th>min</th>\n      <th>25%</th>\n      <th>50%</th>\n      <th>75%</th>\n      <th>max</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Hours Studied</th>\n      <td>9873.0</td>\n      <td>4.992100</td>\n      <td>2.589081</td>\n      <td>1.0</td>\n      <td>3.0</td>\n      <td>5.0</td>\n      <td>7.0</td>\n      <td>9.0</td>\n    </tr>\n    <tr>\n      <th>Previous Scores</th>\n      <td>9873.0</td>\n      <td>69.441102</td>\n      <td>17.325601</td>\n      <td>40.0</td>\n      <td>54.0</td>\n      <td>69.0</td>\n      <td>85.0</td>\n      <td>99.0</td>\n    </tr>\n    <tr>\n      <th>Extracurricular Activities</th>\n      <td>9873.0</td>\n      <td>0.494986</td>\n      <td>0.500000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>Sleep Hours</th>\n      <td>9873.0</td>\n      <td>6.531652</td>\n      <td>1.697683</td>\n      <td>4.0</td>\n      <td>5.0</td>\n      <td>7.0</td>\n      <td>8.0</td>\n      <td>9.0</td>\n    </tr>\n    <tr>\n      <th>Sample Question Papers Practiced</th>\n      <td>9873.0</td>\n      <td>4.583004</td>\n      <td>2.867202</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>5.0</td>\n      <td>7.0</td>\n      <td>9.0</td>\n    </tr>\n    <tr>\n      <th>Performance Index</th>\n      <td>9873.0</td>\n      <td>55.216651</td>\n      <td>19.208570</td>\n      <td>10.0</td>\n      <td>40.0</td>\n      <td>55.0</td>\n      <td>70.0</td>\n      <td>100.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe().T"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-19T20:47:54.305711200Z",
     "start_time": "2024-03-19T20:47:54.261268Z"
    }
   },
   "id": "c39c03017caf635f"
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "outputs": [
    {
     "data": {
      "text/plain": "<Axes: >"
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlation_matrix = data[['Hours Studied', 'Previous Scores', 'Extracurricular Activities', 'Sleep Hours', 'Sample Question Papers Practiced', 'Performance Index']].corr()\n",
    "\n",
    "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=\".2f\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-19T20:47:59.899712500Z",
     "start_time": "2024-03-19T20:47:59.693519400Z"
    }
   },
   "id": "c6c55abb017e0495"
  },
  {
   "cell_type": "markdown",
   "source": [
    "Data standarization to normal distribution"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "ca1e2b8ebde76d1"
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "outputs": [],
   "source": [
    "X = data.drop(\"Performance Index\", axis=1)\n",
    "y = data[\"Performance Index\"]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-19T20:48:01.672431900Z",
     "start_time": "2024-03-19T20:48:01.661061700Z"
    }
   },
   "id": "59c20d509c41cd66"
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=21)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-19T20:48:01.963554600Z",
     "start_time": "2024-03-19T20:48:01.945884600Z"
    }
   },
   "id": "8841e5694b442b1c"
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "outputs": [],
   "source": [
    "scaler = preprocessing.StandardScaler().fit(X_train)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-19T20:48:02.287590400Z",
     "start_time": "2024-03-19T20:48:02.273839600Z"
    }
   },
   "id": "67426be49e5c5dc3"
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "outputs": [],
   "source": [
    "X_train_scaled = scaler.transform(X_train)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-19T20:48:02.645788200Z",
     "start_time": "2024-03-19T20:48:02.627920600Z"
    }
   },
   "id": "4317361ad8568b86"
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "outputs": [],
   "source": [
    "X_test_scaled = scaler.transform(X_test)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-19T20:48:02.956655Z",
     "start_time": "2024-03-19T20:48:02.930133900Z"
    }
   },
   "id": "cd282cdc2d735092"
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[ 0.77824248,  0.02642469, -0.98691768, -1.48058947,  0.84637542],\n       [-0.77049682, -1.58829235,  1.01325574,  0.87108386,  1.54382162],\n       [-0.77049682,  0.48777241, -0.98691768,  1.45900219,  0.14892922],\n       ...,\n       [ 0.00387283,  1.179794  , -0.98691768, -0.89267114, -1.24596317],\n       [ 1.16542731, -1.70362928, -0.98691768,  0.28316553,  0.84637542],\n       [-0.77049682,  0.89145167,  1.01325574, -0.89267114, -0.89724007]])"
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_scaled"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-19T20:48:03.374899900Z",
     "start_time": "2024-03-19T20:48:03.359346400Z"
    }
   },
   "id": "2a718a02f4d47c80"
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Evaluation"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "7390bdac4f5de3e1"
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "outputs": [
    {
     "data": {
      "text/plain": "LinearRegression()",
      "text/html": "<style>#sk-container-id-2 {\n  /* Definition of color scheme common for light and dark mode */\n  --sklearn-color-text: black;\n  --sklearn-color-line: gray;\n  /* Definition of color scheme for unfitted estimators */\n  --sklearn-color-unfitted-level-0: #fff5e6;\n  --sklearn-color-unfitted-level-1: #f6e4d2;\n  --sklearn-color-unfitted-level-2: #ffe0b3;\n  --sklearn-color-unfitted-level-3: chocolate;\n  /* Definition of color scheme for fitted estimators */\n  --sklearn-color-fitted-level-0: #f0f8ff;\n  --sklearn-color-fitted-level-1: #d4ebff;\n  --sklearn-color-fitted-level-2: #b3dbfd;\n  --sklearn-color-fitted-level-3: cornflowerblue;\n\n  /* Specific color for light theme */\n  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n  --sklearn-color-icon: #696969;\n\n  @media (prefers-color-scheme: dark) {\n    /* Redefinition of color scheme for dark theme */\n    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n    --sklearn-color-icon: #878787;\n  }\n}\n\n#sk-container-id-2 {\n  color: var(--sklearn-color-text);\n}\n\n#sk-container-id-2 pre {\n  padding: 0;\n}\n\n#sk-container-id-2 input.sk-hidden--visually {\n  border: 0;\n  clip: rect(1px 1px 1px 1px);\n  clip: rect(1px, 1px, 1px, 1px);\n  height: 1px;\n  margin: -1px;\n  overflow: hidden;\n  padding: 0;\n  position: absolute;\n  width: 1px;\n}\n\n#sk-container-id-2 div.sk-dashed-wrapped {\n  border: 1px dashed var(--sklearn-color-line);\n  margin: 0 0.4em 0.5em 0.4em;\n  box-sizing: border-box;\n  padding-bottom: 0.4em;\n  background-color: var(--sklearn-color-background);\n}\n\n#sk-container-id-2 div.sk-container {\n  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n     but bootstrap.min.css set `[hidden] { display: none !important; }`\n     so we also need the `!important` here to be able to override the\n     default hidden behavior on the sphinx rendered scikit-learn.org.\n     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n  display: inline-block !important;\n  position: relative;\n}\n\n#sk-container-id-2 div.sk-text-repr-fallback {\n  display: none;\n}\n\ndiv.sk-parallel-item,\ndiv.sk-serial,\ndiv.sk-item {\n  /* draw centered vertical line to link estimators */\n  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n  background-size: 2px 100%;\n  background-repeat: no-repeat;\n  background-position: center center;\n}\n\n/* Parallel-specific style estimator block */\n\n#sk-container-id-2 div.sk-parallel-item::after {\n  content: \"\";\n  width: 100%;\n  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n  flex-grow: 1;\n}\n\n#sk-container-id-2 div.sk-parallel {\n  display: flex;\n  align-items: stretch;\n  justify-content: center;\n  background-color: var(--sklearn-color-background);\n  position: relative;\n}\n\n#sk-container-id-2 div.sk-parallel-item {\n  display: flex;\n  flex-direction: column;\n}\n\n#sk-container-id-2 div.sk-parallel-item:first-child::after {\n  align-self: flex-end;\n  width: 50%;\n}\n\n#sk-container-id-2 div.sk-parallel-item:last-child::after {\n  align-self: flex-start;\n  width: 50%;\n}\n\n#sk-container-id-2 div.sk-parallel-item:only-child::after {\n  width: 0;\n}\n\n/* Serial-specific style estimator block */\n\n#sk-container-id-2 div.sk-serial {\n  display: flex;\n  flex-direction: column;\n  align-items: center;\n  background-color: var(--sklearn-color-background);\n  padding-right: 1em;\n  padding-left: 1em;\n}\n\n\n/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\nclickable and can be expanded/collapsed.\n- Pipeline and ColumnTransformer use this feature and define the default style\n- Estimators will overwrite some part of the style using the `sk-estimator` class\n*/\n\n/* Pipeline and ColumnTransformer style (default) */\n\n#sk-container-id-2 div.sk-toggleable {\n  /* Default theme specific background. It is overwritten whether we have a\n  specific estimator or a Pipeline/ColumnTransformer */\n  background-color: var(--sklearn-color-background);\n}\n\n/* Toggleable label */\n#sk-container-id-2 label.sk-toggleable__label {\n  cursor: pointer;\n  display: block;\n  width: 100%;\n  margin-bottom: 0;\n  padding: 0.5em;\n  box-sizing: border-box;\n  text-align: center;\n}\n\n#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n  /* Arrow on the left of the label */\n  content: \"▸\";\n  float: left;\n  margin-right: 0.25em;\n  color: var(--sklearn-color-icon);\n}\n\n#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n  color: var(--sklearn-color-text);\n}\n\n/* Toggleable content - dropdown */\n\n#sk-container-id-2 div.sk-toggleable__content {\n  max-height: 0;\n  max-width: 0;\n  overflow: hidden;\n  text-align: left;\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-2 div.sk-toggleable__content.fitted {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n#sk-container-id-2 div.sk-toggleable__content pre {\n  margin: 0.2em;\n  border-radius: 0.25em;\n  color: var(--sklearn-color-text);\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n  /* unfitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n  /* Expand drop-down */\n  max-height: 200px;\n  max-width: 100%;\n  overflow: auto;\n}\n\n#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n  content: \"▾\";\n}\n\n/* Pipeline/ColumnTransformer-specific style */\n\n#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Estimator-specific style */\n\n/* Colorize estimator box */\n#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n#sk-container-id-2 div.sk-label label {\n  /* The background is the default theme color */\n  color: var(--sklearn-color-text-on-default-background);\n}\n\n/* On hover, darken the color of the background */\n#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n/* Label box, darken color on hover, fitted */\n#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Estimator label */\n\n#sk-container-id-2 div.sk-label label {\n  font-family: monospace;\n  font-weight: bold;\n  display: inline-block;\n  line-height: 1.2em;\n}\n\n#sk-container-id-2 div.sk-label-container {\n  text-align: center;\n}\n\n/* Estimator-specific */\n#sk-container-id-2 div.sk-estimator {\n  font-family: monospace;\n  border: 1px dotted var(--sklearn-color-border-box);\n  border-radius: 0.25em;\n  box-sizing: border-box;\n  margin-bottom: 0.5em;\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-2 div.sk-estimator.fitted {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n/* on hover */\n#sk-container-id-2 div.sk-estimator:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-2 div.sk-estimator.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Specification for estimator info (e.g. \"i\" and \"?\") */\n\n/* Common style for \"i\" and \"?\" */\n\n.sk-estimator-doc-link,\na:link.sk-estimator-doc-link,\na:visited.sk-estimator-doc-link {\n  float: right;\n  font-size: smaller;\n  line-height: 1em;\n  font-family: monospace;\n  background-color: var(--sklearn-color-background);\n  border-radius: 1em;\n  height: 1em;\n  width: 1em;\n  text-decoration: none !important;\n  margin-left: 1ex;\n  /* unfitted */\n  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n  color: var(--sklearn-color-unfitted-level-1);\n}\n\n.sk-estimator-doc-link.fitted,\na:link.sk-estimator-doc-link.fitted,\na:visited.sk-estimator-doc-link.fitted {\n  /* fitted */\n  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n  color: var(--sklearn-color-fitted-level-1);\n}\n\n/* On hover */\ndiv.sk-estimator:hover .sk-estimator-doc-link:hover,\n.sk-estimator-doc-link:hover,\ndiv.sk-label-container:hover .sk-estimator-doc-link:hover,\n.sk-estimator-doc-link:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\ndiv.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n.sk-estimator-doc-link.fitted:hover,\ndiv.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n.sk-estimator-doc-link.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\n/* Span, style for the box shown on hovering the info icon */\n.sk-estimator-doc-link span {\n  display: none;\n  z-index: 9999;\n  position: relative;\n  font-weight: normal;\n  right: .2ex;\n  padding: .5ex;\n  margin: .5ex;\n  width: min-content;\n  min-width: 20ex;\n  max-width: 50ex;\n  color: var(--sklearn-color-text);\n  box-shadow: 2pt 2pt 4pt #999;\n  /* unfitted */\n  background: var(--sklearn-color-unfitted-level-0);\n  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n}\n\n.sk-estimator-doc-link.fitted span {\n  /* fitted */\n  background: var(--sklearn-color-fitted-level-0);\n  border: var(--sklearn-color-fitted-level-3);\n}\n\n.sk-estimator-doc-link:hover span {\n  display: block;\n}\n\n/* \"?\"-specific style due to the `<a>` HTML tag */\n\n#sk-container-id-2 a.estimator_doc_link {\n  float: right;\n  font-size: 1rem;\n  line-height: 1em;\n  font-family: monospace;\n  background-color: var(--sklearn-color-background);\n  border-radius: 1rem;\n  height: 1rem;\n  width: 1rem;\n  text-decoration: none;\n  /* unfitted */\n  color: var(--sklearn-color-unfitted-level-1);\n  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n}\n\n#sk-container-id-2 a.estimator_doc_link.fitted {\n  /* fitted */\n  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n  color: var(--sklearn-color-fitted-level-1);\n}\n\n/* On hover */\n#sk-container-id-2 a.estimator_doc_link:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\n#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-3);\n}\n</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train_scaled, y_train)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-19T20:48:04.831605300Z",
     "start_time": "2024-03-19T20:48:04.821478500Z"
    }
   },
   "id": "2d534985eacd2276"
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "outputs": [],
   "source": [
    "import numpy\n",
    "\n",
    "# Predicting Test Set Results\n",
    "y_pred = regressor.predict(X_test_scaled)\n",
    "y_pred = numpy.round(y_pred, decimals = 2)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-19T20:48:05.677281Z",
     "start_time": "2024-03-19T20:48:05.664556500Z"
    }
   },
   "id": "4e710bfcbee84c00"
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error :  4.213380000000001\n",
      "R Square :  0.9888499650063397\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score, mean_squared_error\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "mean_er = mean_squared_error(y_test, y_pred)\n",
    "print('Mean Squared Error : ', mean_er)\n",
    "print('R Square : ', r2)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-19T20:48:05.978638600Z",
     "start_time": "2024-03-19T20:48:05.967924800Z"
    }
   },
   "id": "40f77178deb0dbee"
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "outputs": [
    {
     "data": {
      "text/plain": "      Actual Performance  Predicted Performance\n4288                31.0                  29.19\n5077                62.0                  59.85\n3955                16.0                  16.21\n9149                73.0                  73.85\n3089                44.0                  45.14\n...                  ...                    ...\n4791                35.0                  39.65\n1750                48.0                  48.94\n8441                75.0                  74.65\n9143                67.0                  65.49\n2522                51.0                  47.46\n\n[1975 rows x 2 columns]",
      "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>Actual Performance</th>\n      <th>Predicted Performance</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>4288</th>\n      <td>31.0</td>\n      <td>29.19</td>\n    </tr>\n    <tr>\n      <th>5077</th>\n      <td>62.0</td>\n      <td>59.85</td>\n    </tr>\n    <tr>\n      <th>3955</th>\n      <td>16.0</td>\n      <td>16.21</td>\n    </tr>\n    <tr>\n      <th>9149</th>\n      <td>73.0</td>\n      <td>73.85</td>\n    </tr>\n    <tr>\n      <th>3089</th>\n      <td>44.0</td>\n      <td>45.14</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>4791</th>\n      <td>35.0</td>\n      <td>39.65</td>\n    </tr>\n    <tr>\n      <th>1750</th>\n      <td>48.0</td>\n      <td>48.94</td>\n    </tr>\n    <tr>\n      <th>8441</th>\n      <td>75.0</td>\n      <td>74.65</td>\n    </tr>\n    <tr>\n      <th>9143</th>\n      <td>67.0</td>\n      <td>65.49</td>\n    </tr>\n    <tr>\n      <th>2522</th>\n      <td>51.0</td>\n      <td>47.46</td>\n    </tr>\n  </tbody>\n</table>\n<p>1975 rows × 2 columns</p>\n</div>"
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({'Actual Performance': y_test, 'Predicted Performance': y_pred})"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-19T20:48:07.210037600Z",
     "start_time": "2024-03-19T20:48:07.169801300Z"
    }
   },
   "id": "52e87addfa74339b"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   },
   "id": "b8dbc39f5ad7a856"
  }
 ],
 "metadata": {
  "kernelspec": {
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
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