1059 lines
309 KiB
Plaintext
1059 lines
309 KiB
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{
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"cells": [
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"cell_type": "code",
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"execution_count": 78,
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"id": "initial_id",
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"metadata": {
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"collapsed": true,
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"ExecuteTime": {
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"end_time": "2024-03-19T20:47:50.665341800Z",
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"start_time": "2024-03-19T20:47:42.485262800Z"
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}
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},
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"outputs": [
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"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: tqdm in c:\\users\\arden\\pycharmprojects\\ium_s464980\\venv\\lib\\site-packages (from opendatasets) (4.66.2)\n",
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"Requirement already satisfied: kaggle in c:\\users\\arden\\pycharmprojects\\ium_s464980\\venv\\lib\\site-packages (from opendatasets) (1.6.6)\n",
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"Requirement already satisfied: click in c:\\users\\arden\\pycharmprojects\\ium_s464980\\venv\\lib\\site-packages (from opendatasets) (8.1.7)\n",
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"Requirement already satisfied: colorama in c:\\users\\arden\\pycharmprojects\\ium_s464980\\venv\\lib\\site-packages (from click->opendatasets) (0.4.6)\n",
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"Requirement already satisfied: certifi in c:\\users\\arden\\pycharmprojects\\ium_s464980\\venv\\lib\\site-packages (from kaggle->opendatasets) (2024.2.2)\n",
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"Requirement already satisfied: python-dateutil in c:\\users\\arden\\pycharmprojects\\ium_s464980\\venv\\lib\\site-packages (from kaggle->opendatasets) (2.9.0.post0)\n",
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"Requirement already satisfied: requests in c:\\users\\arden\\pycharmprojects\\ium_s464980\\venv\\lib\\site-packages (from kaggle->opendatasets) (2.31.0)\n",
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"Requirement already satisfied: python-slugify in c:\\users\\arden\\pycharmprojects\\ium_s464980\\venv\\lib\\site-packages (from kaggle->opendatasets) (8.0.4)\n",
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"Requirement already satisfied: urllib3 in c:\\users\\arden\\pycharmprojects\\ium_s464980\\venv\\lib\\site-packages (from kaggle->opendatasets) (2.2.1)\n",
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"Requirement already satisfied: bleach in c:\\users\\arden\\pycharmprojects\\ium_s464980\\venv\\lib\\site-packages (from kaggle->opendatasets) (6.1.0)\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",
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"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",
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"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",
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"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"
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"text": [
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"\n",
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"[notice] A new release of pip available: 22.3.1 -> 24.0\n",
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"[notice] To update, run: python.exe -m pip install --upgrade pip\n"
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"text": [
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"Requirement already satisfied: pandas in c:\\users\\arden\\pycharmprojects\\ium_s464980\\venv\\lib\\site-packages (2.2.1)\n",
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"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",
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"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",
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"Requirement already satisfied: pytz>=2020.1 in c:\\users\\arden\\pycharmprojects\\ium_s464980\\venv\\lib\\site-packages (from pandas) (2024.1)\n",
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"Requirement already satisfied: tzdata>=2022.7 in c:\\users\\arden\\pycharmprojects\\ium_s464980\\venv\\lib\\site-packages (from pandas) (2024.1)\n",
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"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"
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"text": [
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"\n",
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"[notice] A new release of pip available: 22.3.1 -> 24.0\n",
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"[notice] To update, run: python.exe -m pip install --upgrade pip\n"
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]
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},
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Requirement already satisfied: seaborn in c:\\users\\arden\\pycharmprojects\\ium_s464980\\venv\\lib\\site-packages (0.13.2)\n",
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"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",
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"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: matplotlib!=3.6.1,>=3.4 in c:\\users\\arden\\pycharmprojects\\ium_s464980\\venv\\lib\\site-packages (from seaborn) (3.8.3)\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",
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"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",
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"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",
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"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",
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"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",
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"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",
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"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",
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"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",
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"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",
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"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",
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"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"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"\n",
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"[notice] A new release of pip available: 22.3.1 -> 24.0\n",
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"[notice] To update, run: python.exe -m pip install --upgrade pip\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Requirement already satisfied: scikit-learn in c:\\users\\arden\\pycharmprojects\\ium_s464980\\venv\\lib\\site-packages (1.4.1.post1)\n",
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"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",
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"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",
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"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",
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"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"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"\n",
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|||
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"[notice] A new release of pip available: 22.3.1 -> 24.0\n",
|
|||
|
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"[notice] To update, run: python.exe -m pip install --upgrade pip\n"
|
|||
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]
|
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}
|
|||
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],
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|||
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"source": [
|
|||
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"!pip install opendatasets\n",
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|||
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"!pip install pandas\n",
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"!pip install seaborn\n",
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|||
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"!pip install scikit-learn"
|
|||
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]
|
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},
|
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{
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|||
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"cell_type": "code",
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"execution_count": 79,
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"outputs": [],
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"source": [
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|||
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"import opendatasets as od\n",
|
|||
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"import pandas as pd\n",
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"import seaborn as sns\n",
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"from sklearn import preprocessing\n",
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"from sklearn.model_selection import train_test_split"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2024-03-19T20:47:50.666770400Z",
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"start_time": "2024-03-19T20:47:50.658428100Z"
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}
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},
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"id": "93a5ba2f9a7b31"
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [
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"od.download(\"https://www.kaggle.com/datasets/nikhil7280/student-performance-multiple-linear-regression/code\")"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"start_time": "2024-03-19T19:43:55.173000400Z"
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}
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},
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"id": "588b4898babf70ab"
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},
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{
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"cell_type": "code",
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"execution_count": 80,
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"outputs": [],
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"source": [
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"data = pd.read_csv(\"student-performance-multiple-linear-regression/Student_Performance.csv\")"
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],
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"metadata": {
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"collapsed": false,
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"end_time": "2024-03-19T20:47:50.677033600Z",
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"start_time": "2024-03-19T20:47:50.661824200Z"
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},
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"id": "38e0dd5adceeabd5"
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},
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{
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"cell_type": "code",
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"execution_count": 81,
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"outputs": [
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{
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"data": {
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"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 ",
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"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>"
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},
|
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"execution_count": 81,
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|||
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"metadata": {},
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|||
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"output_type": "execute_result"
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}
|
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],
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"source": [
|
|||
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"data.head()"
|
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],
|
|||
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"metadata": {
|
|||
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"collapsed": false,
|
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"ExecuteTime": {
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"end_time": "2024-03-19T20:47:50.689553400Z",
|
|||
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|
"start_time": "2024-03-19T20:47:50.673502800Z"
|
|||
|
|
}
|
|||
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},
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"id": "df598eeed0ebd2d4"
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{
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"cell_type": "code",
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"execution_count": 82,
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"outputs": [
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{
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"data": {
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"text/plain": "(10000, 6)"
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},
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"execution_count": 82,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"data.shape"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2024-03-19T20:47:50.690555200Z",
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"start_time": "2024-03-19T20:47:50.681856700Z"
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}
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},
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"id": "446893ffa34c6f4c"
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},
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{
|
|||
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"cell_type": "markdown",
|
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"source": [
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"Remove duplicates"
|
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],
|
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"metadata": {
|
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"collapsed": false
|
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},
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"id": "7addf40a8a02f327"
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},
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{
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"cell_type": "code",
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"execution_count": 83,
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"outputs": [],
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"source": [
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"data.drop_duplicates(inplace=True)"
|
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],
|
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"metadata": {
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"collapsed": false,
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"start_time": "2024-03-19T20:47:50.686556400Z"
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|||
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},
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|||
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"id": "fbde2e19a3ac1408"
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|||
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|||
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{
|
|||
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|
"cell_type": "code",
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|||
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|
"execution_count": 84,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": "(9873, 6)"
|
|||
|
|
},
|
|||
|
|
"execution_count": 84,
|
|||
|
|
"metadata": {},
|
|||
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|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"data.shape"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
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|
"collapsed": false,
|
|||
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|||
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|
"end_time": "2024-03-19T20:47:50.728434300Z",
|
|||
|
|
"start_time": "2024-03-19T20:47:50.693387600Z"
|
|||
|
|
}
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|||
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},
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|||
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|
"id": "603085c31658e13c"
|
|||
|
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},
|
|||
|
|
{
|
|||
|
|
"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,
|
|||
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|
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|||
|
|
"end_time": "2024-03-19T20:47:50.816169300Z",
|
|||
|
|
"start_time": "2024-03-19T20:47:50.697956300Z"
|
|||
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|
}
|
|||
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|
},
|
|||
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|
"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>",
|
|||
|
|
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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": "iVBORw0KGgoAAAANSUhEUgAAAjsAAAGwCAYAAABPSaTdAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/H5lhTAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAxBElEQVR4nO3deXQUZb7/8U9CVgidEJYOKAn7EmQRUGjUcYRAwFwHr5kRNWJERr3cgCyKyE824TJwGQcdZwJe5zAEBURxQc1FtrDIErYgOyIoGhSSCBgCAgkkz++PufShISgJHTp5fL/OqXPS9Txd9a3n9Awfq56q8jPGGAEAAFjK39cFAAAAVCTCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1QJ8XUBlUFJSoiNHjqhmzZry8/PzdTkAAOAaGGN06tQpNWjQQP7+Vz9/Q9iRdOTIETVs2NDXZQAAgHI4fPiwbr755qu2E3Yk1axZU9K/BsvhcPi4GgAAcC0KCgrUsGFD97/jV0PYkdyXrhwOB2EHAIAq5pemoDBBGQAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqAb4uACiP7OxsHTt2zOvbrVOnjqKjo72+XQCA7xB2UOVkZ2erVavWOnv2jNe3HRpaXV98sY/AAwAWIeygyjl27JjOnj2jLk+Ml6N+I69tt+DoN9r0z5d07Ngxwg4AWISwgyrLUb+RIqNb+roMAEAlxwRlAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAVgvwdQEAyic7O1vHjh3z6jbr1Kmj6Ohor24TAHyNsANUQdnZ2WrVqrXOnj3j1e2GhlbXF1/sI/AAsAphB6iCjh07prNnz6jLE+PlqN/IK9ssOPqNNv3zJR07doywA8AqhB2gCnPUb6TI6Ja+LgMAKjUmKAMAAKsRdgAAgNW4jAUAVVBF3I0nVa078ipiDAoLCxUcHOzVbUpVa1xtRNgBgCqmou7Gk6rOHXkVNgZ+fpIx3t2mqs642oqwAwBVTEXcjSdVrTvyKmIMju7K1O6P31CHR0apbuNWXtmmVLXG1VaEHQCoorgbz7tjUHD0G0lSWL3oChnXffv2eXV7XBq7doQdAAAq0NmTxyX56dFHH/Xqdrk0du0IO0AFq4hJlN7+L0QAFef8mVOSjFcvj3FprGwIO0AFqsiJpJJ0vrCoQrYLwPsq6vIYfplPw86ECRP00ksveaxr2bKlvvjiC0nSuXPn9Oyzz2rBggUqLCxUfHy8ZsyYIafT6e6fnZ2tQYMGadWqVQoLC1NycrKmTJmigAByHHyvoiaSXpxIeeHCBa9tEwBs5fNE0KZNG61YscL9+dKQMnz4cP3v//6vFi5cqPDwcA0ePFgPPPCA1q9fL0kqLi5WQkKCoqKitGHDBh09elSPPfaYAgMD9ac//emGHwtwNd6eSHpxIiUA4Jf5POwEBAQoKirqivUnT57UrFmzNH/+fHXv3l2SNHv2bLVu3VobN25U165dtWzZMu3du1crVqyQ0+lUhw4dNGnSJI0aNUoTJkxQUFDQjT4cAABQyfg87Bw4cEANGjRQSEiIXC6XpkyZoujoaGVlZen8+fOKi4tz923VqpWio6OVmZmprl27KjMzU23btvW4rBUfH69BgwZpz549uvXWW0vdZ2FhoQoLC92fCwoKKuz4qtJTTqtSrQAAXCufhp0uXbooLS1NLVu21NGjR/XSSy/prrvu0u7du5WTk6OgoCBFRER4fMfpdConJ0eSlJOT4xF0LrZfbLuaKVOmXDFXqCJUpaecVqVaAQAoC5+GnT59+rj/bteunbp06aKYmBi9++67Cg0NrbD9jh49WiNGjHB/LigoUMOGDb2+n6r0lNOqVCsAAGXh88tYl4qIiFCLFi108OBB9ezZU0VFRcrPz/c4u5Obm+ue4xMVFaXNmzd7bCM3N9fddjXBwcEV8qK3q6lKTzmtSrUCAHAt/H1dwKVOnz6tr776SvXr11enTp0UGBiojIwMd/v+/fuVnZ0tl8slSXK5XNq1a5fy8vLcfZYvXy6Hw6HY2NgbXj8AAKh8fHpm57nnntN9992nmJgYHTlyROPHj1e1atX08MMPKzw8XAMHDtSIESMUGRkph8OhIUOGyOVyqWvXrpKkXr16KTY2Vv3799e0adOUk5OjMWPGKCUl5YaeuQFwdUx8B+BrPg073333nR5++GEdP35cdevW1Z133qmNGzeqbt26kqRXXnlF/v7+SkxM9Hio4EXVqlVTenq6Bg0aJJfLpRo1aig5OVkTJ0701SEBuAQT3wFUBj4NOwsWLPjZ9pCQEKWmpio1NfWqfWJiYrR48WJvlwbAC5j4DqAyqFQTlFE23nwZJC+WREVi4jsAXyLsVEFnTx6X5KdHH33U69vmxZIAANsQdqqg82dOSTLq8Mgo1W3cyivb5MWSAABbEXaqsLB60V67NMCLJQEAtiLsAPDAXDDg160iHhfh60dFEHYASGIuGICKe1yErx8VQdgBIIm5YAAq5nERleFREYQdAB6YCwbAtsdFVKp3YwEAAHgbYQcAAFiNy1gAAFRR3r7j0dY7KAk7APB/eEM7qoqKvHtSsu8OSsIOAIg3tKNqqYi7JyV776Ak7ACAeEM7qiZv3j0p2XsHJWEHAC5h2y23ALgbCwAAWI6wAwAArEbYAQAAViPsAAAAqzFBGQDgwdsPluM5Q/A1wg4AQFLFPaiO5wzB1wg7AABJFfOgOp4zhMqAsAMAN4A3Lw1V9PuLvP2gOsDXCDsAUIEq8h1Gtr2/CKgohB0AqEAVcWnI1vcXARWFsAMAN4A3Lw3Z+v4ioKLwnB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNV4zg5uiKr0qHwAgF0IO6hQPCofAOBrhB1UKB6VDwDwNcIObggelQ8A8BUmKAMAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYrdKEnalTp8rPz0/Dhg1zrzt37pxSUlJUu3ZthYWFKTExUbm5uR7fy87OVkJCgqpXr6569epp5MiRunDhwg2uHgAAVFaVIuxs2bJF//M//6N27dp5rB8+fLg++eQTLVy4UGvWrNGRI0f0wAMPuNuLi4uVkJCgoqIibdiwQXPmzFFaWprGjRt3ow8BAABUUj4PO6dPn1ZSUpL+8Y9/qFatWu71J0+e1KxZszR9+nR1795dnTp10uzZs7VhwwZt3LhRkrRs2TLt3btXc+fOVYcOHdSnTx9NmjRJqampKioq8tUhAQCASsTnYSclJUUJCQmKi4vzWJ+VlaXz5897rG/VqpWio6OVmZkpScrMzFTbtm3ldDrdfeLj41VQUKA9e/ZcdZ+FhYUqKCjwWAAAgJ0CfLnzBQsWaNu2bdqyZcsVbTk5OQoKClJERITHeqfTqZycHHefS4POxfaLbVczZcoUvfTSS9dZPQAAqAp8dmbn8OHDGjp0qObNm6eQkJAbuu/Ro0fr5MmT7uXw4cM3dP8AAODG8VnYycrKUl5enjp27KiAgAAFBARozZo1eu211xQQECCn06mioiLl5+d7fC83N1dRUVGSpKioqCvuzrr4+WKf0gQHB8vhcHgsAADATj4LOz169NCuXbu0fft299K5c2clJSW5/w4MDFRGRob7O/v371d2drZcLpckyeVyadeuXcrLy3P3Wb58uRwOh2JjY2/4MQEAgMrHZ3N2atasqVtuucVjXY0aNVS7dm33+oEDB2rEiBGKjIyUw+HQkCFD5HK51LVrV0lSr169FBsbq/79+2vatGn
|
|||
|
|
},
|
|||
|
|
"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": "iVBORw0KGgoAAAANSUhEUgAAAkQAAAGwCAYAAABIC3rIAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/H5lhTAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAwqklEQVR4nO3de1xVZb7H8e8WBbxwEZFbIiJNKCbeI6xMRwLRMTs6NSqleZ8OakljRscLYilpx2zKsWOT2kWzM2fMzMoU7ymmYoi3GDWNSsG8bjFDLvv8MS/3mX3QVAQ28Hzer9d6vVjP8+y1fmuNvfZ3nvXsvS02m80mAAAAg9VxdgEAAADORiACAADGIxABAADjEYgAAIDxCEQAAMB4BCIAAGA8AhEAADBeXWcXUBOUlpbqxIkT8vDwkMVicXY5AADgJthsNl28eFFBQUGqU+fX54AIRDfhxIkTCg4OdnYZAACgHL7//ns1a9bsV8cQiG6Ch4eHpH/eUE9PTydXAwAAbobValVwcLD9ffzXEIhuwtXHZJ6engQiAABqmJtZ7sKiagAAYDwCEQAAMB6BCAAAGI9ABAAAjEcgAgAAxiMQAQAA4xGIAACA8QhEAADAeAQiAABgPAIRAAAwHoEIAAAYj0AEAACMRyACAADGIxABAADj1XV2AQAAOENubq5Onz5dqefw9fVV8+bNK/UcqBgEIgCAcXJzc9WqVWtdvvxzpZ6nfv0G+uabQ4SiGoBABAAwzunTp3X58s+KGj5NnoEtKuUc1pPH9dWi6Tp9+jSBqAYgEAEAjOUZ2EI+zcOdXQaqAacuqt6yZYv69u2roKAgWSwWrVy50qHfYrFcc5szZ459TIsWLcr0p6WlORwnOztbDzzwgNzd3RUcHKzZs2dXxeUBAIAawqmB6NKlS2rXrp3mz59/zf6TJ086bIsWLZLFYtGAAQMcxqWmpjqMGzdunL3ParUqNjZWISEhyszM1Jw5c5SSkqKFCxdW6rUBAICaw6mPzOLj4xUfH3/d/oCAAIf9jz/+WD169FDLli0d2j08PMqMvWrp0qW6cuWKFi1aJFdXV7Vp00ZZWVmaO3euRo8efc3XFBYWqrCw0L5vtVpv9pIAAEANVGO+hyg/P1+ffvqpRowYUaYvLS1NTZo0UYcOHTRnzhwVFxfb+zIyMtStWze5urra2+Li4pSTk6Nz585d81yzZs2Sl5eXfQsODq74CwIAANVGjQlE77zzjjw8PNS/f3+H9vHjx2v58uXauHGjxowZo5kzZ+q5556z9+fl5cnf39/hNVf38/Lyrnmu5ORkXbhwwb59//33FXw1AACgOqkxnzJbtGiREhIS5O7u7tCelJRk/zsyMlKurq4aM2aMZs2aJTc3t3Kdy83NrdyvBQAANU+NmCHaunWrcnJyNHLkyBuOjYqKUnFxsY4fPy7pn+uQ8vPzHcZc3b/euiMAAGCWGhGI3n77bXXq1Ent2rW74disrCzVqVNHfn5+kqTo6Ght2bJFRUVF9jHr1q1TeHi4GjduXGk1AwCAmsOpgaigoEBZWVnKysqSJB07dkxZWVnKzc21j7Farfrb3/52zdmhjIwMzZs3T3v37tW3336rpUuXasKECXr88cftYWfw4MFydXXViBEjdODAAX344Yd67bXXHB61AQAAszl1DdHu3bvVo0cP+/7VkDJ06FAtWbJEkrR8+XLZbDYNGjSozOvd3Ny0fPlypaSkqLCwUKGhoZowYYJD2PHy8tLatWuVmJioTp06ydfXV1OnTr3uR+4BAIB5nBqIunfvLpvN9qtjRo8efd3w0rFjR+3YseOG54mMjNTWrVvLVSMAAKj9asQaIgAAgMpEIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYDwCEQAAMB6BCAAAGI9ABAAAjEcgAgAAxiMQAQAA4zn1t8wAwBS5ubk6ffp0pZ/H19dXzZs3r/TzALUNgQhAuVXFm3xteIPPzc1Vq1atdfnyz5V+rvr1G+ibbw7V+HsGVDUCEYByqao3+drwBn/69GldvvyzooZPk2dgi0o7j/XkcX21aLpOnz5do+8X4AwEIgDlUhVv8rXtDd4zsIV8moc7uwwA10AgAnBbeJMHUBvwKTMAAGA8AhEAADAegQgAABiPQAQAAIzHoupqgO9yAQBUZya8TxGInIzvcgEAVGemvE8RiJyM73IBAFRnprxPEYiqCb7LBQBQndX29ykWVQMAAOMxQ4QaxYSFfQCAqkcgQo1hysI+AEDVIxChxjBlYR8AoOoRiFDj1PaFfQCAqseiagAAYDwCEQAAMB6BCAAAGI9ABAAAjEcgAgAAxiMQAQAA4xGIAACA8QhEAADAeAQiAABgPKcGoi1btqhv374KCgqSxWLRypUrHfqffPJJWSwWh61Xr14OY86ePauEhAR5enrK29tbI0aMUEFBgcOY7OxsPfDAA3J3d1dwcLBmz55d2ZcGAABqEKcGokuXLqldu3aaP3/+dcf06tVLJ0+etG8ffPCBQ39CQoIOHDigdevWafXq1dqyZYtGjx5t77darYqNjVVISIgyMzM1Z84cpaSkaOHChZV2XQAAoGZx6m+ZxcfHKz4+/lfHuLm5KSAg4Jp9hw4d0po1a7Rr1y517txZkvT666+rd+/eeuWVVxQUFKSlS5fqypUrWrRokVxdXdWmTRtlZWVp7ty5DsEJAACYq9qvIdq0aZP8/PwUHh6up556SmfOnLH3ZWRkyNvb2x6GJCkmJkZ16tTRV199ZR/TrVs3ubq62sfExcUpJydH586du+Y5CwsLZbVaHTYAAFB7VetA1KtXL7377rtav369Xn75ZW3evFnx8fEqKSmRJOXl5cnPz8/hNXXr1pWPj4/y8vLsY/z9/R3GXN2/Oub/mzVrlry8vOxbcHBwRV8aAACoRpz6yOxGBg4caP+7bdu2ioyMVFhYmDZt2qSePXtW2nmTk5OVlJRk37darYQiAABqsWo9Q/T/tWzZUr6+vjpy5IgkKSAgQKdOnXIYU1xcrLNnz9rXHQUEBCg/P99hzNX9661NcnNzk6enp8MGAABqrxoViH744QedOXNGgYGBkqTo6GidP39emZmZ9jEbNmxQaWmpoqKi7GO2bNmioqIi+5h169YpPDxcjRs3rtoLAAAA1ZJTA1FBQYGysrKUlZUlSTp27JiysrKUm5urgoICTZw4UTt27NDx48e1fv169evXT3feeafi4uIkSa1bt1avXr00atQo7dy5U9u2bdPYsWM1cOBABQUFSZIGDx4sV1dXjRgxQgcOHNCHH36o1157zeGRGAAAMJtTA9Hu3bvVoUMHdejQQZKUlJSkDh06aOrUqXJxcVF2drYefvhh3XXXXRoxYoQ6deqkrVu3ys3NzX6MpUuXqlWrVurZs6d69+6t+++/3+E7hry8vLR27VodO3ZMnTp10rPPPqupU6fykXsAAGDn1EXV3bt3l81mu27/F198ccNj+Pj4aNmyZb86JjIyUlu3br3l+gAAgBlq1BoiAACAykAgAgAAxiMQAQAA4xGIAACA8QhEAADAeAQiAABgPAIRAAAwHoEIAAAYj0AEAACMRyACAADGIxABAADjEYgAAIDxCEQAAMB4BCIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYDwCEQAAMB6BCAAAGI9ABAAAjEcgAgAAxiMQAQAA4xGIAACA8QhEAADAeAQiAABgPAIRAAAwHoEIAAAYj0AEAACMRyACAADGIxABAADjEYgAAIDxCEQAAMB4BCIAAGA8pwaiLVu2qG/fvgoKCpLFYtHKlSvtfUVFRZo0aZLatm2rhg0bKigoSEOGDNGJEyccjtGiRQtZLBaHLS0tzWFMdna2HnjgAbm7uys4OFizZ8+uissDAAA1hFMD0aVLl9SuXTvNnz+/TN/PP/+sPXv2aMqUKdqzZ49WrFihnJwcPfzww2XGpqam6uTJk/Zt3Lhx9j6r1arY2FiFhIQoMzNTc+bMUUpKihYuXFip1wYAAGqOus48eXx8vOLj46/Z5+XlpXX
|
|||
|
|
},
|
|||
|
|
"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": "iVBORw0KGgoAAAANSUhEUgAAAkQAAAGwCAYAAABIC3rIAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/H5lhTAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA0Q0lEQVR4nO3de1hVVf7H8Q8oV7mJJpdEIMdr5RVz0KYsSSxrtJxGGyotx3wcsJTJRme8UmqZt1BHs195mdGxnMpxzCzD1FJSQ8k0s5qx8JcBmRfyBgjr90eP+9cRNCXgHF3v1/Oc53Gvtfbe33UOwod19j54GWOMAAAALObt7gIAAADcjUAEAACsRyACAADWIxABAADrEYgAAID1CEQAAMB6BCIAAGC9uu4u4HJQXl6ugwcPKjg4WF5eXu4uBwAAXARjjL7//ntFR0fL2/vCa0AEootw8OBBxcTEuLsMAABQBQcOHFDjxo0vOIZAdBGCg4Ml/fCEhoSEuLkaAABwMYqKihQTE+P8HL8QAtFFOPs2WUhICIEIAIDLzMVc7sJF1QAAwHoEIgAAYD0CEQAAsB6BCAAAWI9ABAAArEcgAgAA1iMQAQAA6xGIAACA9QhEAADAegQiAABgPQIRAACwHoEIAABYj0AEAACsRyACAADWq+vuAgAAniUvL0+HDh2qkWM3bNhQTZo0qZFjAz8HgQgA4MjLy1PLlq106tTJGjl+QECgPv10L6EIHodABABwHDp0SKdOnVTnh8crJCquWo9d9M2X2vrSRB06dIhAdJm6klcPCUQAgApCouIU3qSFu8uAB7nSVw8JRAAA4Cdd6auHBCIAAHDRrtTVQ267BwAA1iMQAQAA6/GWGeDBruQ7OgDAkxCIAA91pd/RYYuaCrUEWqB6EYgAD3Wl39Fhg5oMtQTayx8rwJ6FQAR4uCv1jg4b1FSoJdBe/lgB9jwEIgCoYYRanIsVYM/j1rvMNm3apLvuukvR0dHy8vLSypUrXfqNMRo3bpyioqIUEBCgpKQkff755y5jDh8+rJSUFIWEhCgsLEyDBg3S8ePHXcbs2rVLv/rVr+Tv76+YmBhNnTq1pqcGAMBPOhuWq/NR3QHLFm4NRCdOnFDbtm01d+7cSvunTp2qzMxMzZ8/X1u3blW9evWUnJys06dPO2NSUlK0Z88erVu3TqtXr9amTZv0yCOPOP1FRUXq0aOHYmNjlZOTo2effVYTJkzQggULanx+AADg8uDWt8xuv/123X777ZX2GWM0a9YsjRkzRr1795YkLVmyRBEREVq5cqX69++vvXv3au3atdq+fbsSEhIkSbNnz9Ydd9yhadOmKTo6WkuXLlVJSYleeukl+fr66tprr1Vubq5mzJjhEpx+rLi4WMXFxc52UVFRNc8cAAB4Eo/9YMb9+/crPz9fSUlJTltoaKg6d+6s7OxsSVJ2drbCwsKcMCRJSUlJ8vb21tatW50xN910k3x9fZ0xycnJ2rdvn44cOVLpuadMmaLQ0FDnERMTUxNTBAAAHsJjA1F+fr4kKSIiwqU9IiLC6cvPz1ejRo1c+uvWravw8HCXMZUd48fnONfo0aN17Ngx53HgwIGfPyEAAOCxuMusEn5+fvLz83N3GQAAoJZ47ApRZGSkJKmgoMClvaCgwOmLjIxUYWGhS/+ZM2d0+PBhlzGVHePH5wAAAHbz2EAUHx+vyMhIZWVlOW1FRUXaunWrEhMTJUmJiYk6evSocnJynDHr169XeXm5Onfu7IzZtGmTSktLnTHr1q1TixYtVL9+/VqaDQAA8GRuDUTHjx9Xbm6ucnNzJf1wIXVubq7y8vLk5eWl4cOH66mnntKqVav08ccf68EHH1R0dLT69OkjSWrVqpV69uypwYMHa9u2bdq8ebPS0tLUv39/RUdHS5J+97vfydfXV4MGDdKePXv08ssv67nnnlN6erqbZg0AADyNW68h+vDDD3XLLbc422dDyoABA7Ro0SI98cQTOnHihB555BEdPXpUN954o9auXSt/f39nn6VLlyotLU3du3eXt7e3+vbtq8zMTKc/NDRUb7/9tlJTU9WxY0c1bNhQ48aNO+8t9wAAwD5uDUTdunWTMea8/V5eXsrIyFBGRsZ5x4SHh2vZsmUXPE+bNm303nvvVblOAABwZfPYa4gAAABqC4EIAABYj88hQpXk5eXp0KFDNXLshg0b8heaAVwSvifh5yIQ4ZLl5eWpZctWOnXqZI0cPyAgUJ9+updvQAAuCt+TUB0IRLhkhw4d0qlTJ9X54fEKiYqr1mMXffOltr40UYcOHeKbD4CLwvckVAcCEaosJCpO4U1auLsMAJDE9yT8PFxUDQAArEcgAgAA1iMQAQAA6xGIAACA9bioGkC14vNgAFyOCEQAqg2fBwPgckUg8gA19Rs1v02jtvF5MAAuVwQiN6vJ36j5bRruwufBALjcEIjcrKZ+o+a3aQAALh6ByEPwGzUAAO7DbfcAAMB6BCIAAGA9AhEAALAe1xDBGnxgIADgfAhEsAIfGAgAuBACEazABwYCAC6EQASr8PEGAIDKcFE1AACwHoEIAABYj0AEAACsRyACAADWIxABAADrEYgAAID1CEQAAMB6BCIAAGA9AhEAALAegQgAAFiPQAQAAKxHIAIAANYjEAEAAOsRiAAAgPUIRAAAwHoEIgAAYD0CEQAAsB6BCAAAWI9ABAAArEcgAgAA1iMQAQAA6xGIAACA9QhEAADAegQiAABgPQIRAACwHoEIAABYj0AEAACsRyACAADWIxABAADrEYgAAID1CEQAAMB6BCIAAGA9AhEAALCeRweisrIyjR07VvHx8QoICFDTpk315JNPyhjjjDHGaNy4cYqKilJAQICSkpL0+eefuxzn8OHDSklJUUhIiMLCwjRo0CAdP368tqcDAAA8lEcHomeeeUbz5s3TnDlztHfvXj3zzDOaOnWqZs+e7YyZOnWqMjMzNX/+fG3dulX16tVTcnKyTp8+7YxJSUnRnj17tG7dOq1evVqbNm3SI4884o4pAQAAD1TX3QVcyJYtW9S7d2/16tVLkhQXF6d//OMf2rZtm6QfVodmzZqlMWPGqHfv3pKkJUuWKCIiQitXrlT//v21d+9erV27Vtu3b1dCQoIkafbs2brjjjs0bdo0RUdHu2dyAADAY3j0ClGXLl2UlZWlzz77TJL00Ucf6f3339ftt98uSdq/f7/y8/OVlJTk7BMaGqrOnTsrOztbkpSdna2wsDAnDElSUlKSvL29tXXr1krPW1xcrKKiIpcHAAC4cnn0CtGoUaNUVFSkli1bqk6dOiorK9OkSZOUkpIiScrPz5ckRUREuOwXERHh9OXn56tRo0Yu/XXr1lV4eLgz5lxTpkzRxIkTq3s6AADAQ3n0CtErr7yipUuXatmyZdqxY4cWL16sadOmafHixTV63tGjR+vYsWPO48CBAzV6PgAA4F4evUI0cuRIjRo1Sv3795ckXX/99frqq680ZcoUDRgwQJGRkZKkgoICRUVFOfsVFBSoXbt2kqTIyEgVFha6HPfMmTM6fPiws/+5/Pz85OfnVwMzAgAAnsijV4hOnjwpb2/XEuvUqaPy8nJJUnx8vCIjI5WVleX0FxUVaevWrUpMTJQkJSYm6ujRo8rJyXHGrF+/XuXl5ercuXMtzAIAAHg6j14huuuuuzRp0iQ1adJE1157rXbu3KkZM2bo4YcfliR5eXlp+PDheuqpp9SsWTPFx8dr7Nixio6OVp8+fSRJrVq1Us+ePTV48GDNnz9fpaWlSktLU//+/bnDDAAASPLwQDR79myNHTtWf/jDH1RYWKjo6GgNGTJE48aNc8Y88cQTOnHihB555BEdPXpUN954o9auXSt/f39nzNKlS5WWlqbu3bvL29tbffv2VWZmpjumBAAAPJBHB6Lg4GDNmjVLs2bNOu8YLy8vZWRkKCMj47xjwsPDtWzZshqoEAAAXAk8+hoiAACA2kAgAgAA1iMQAQAA6xGIAACA9QhEAADAegQiAABgPQIRAACwHoEIAABYj0AEAACsRyACAADWIxABAADrEYgAAID1CEQ
|
|||
|
|
},
|
|||
|
|
"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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
|
|||
|
|
},
|
|||
|
|
"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>"
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}
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|||
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"correlation_matrix = data[['Hours Studied', 'Previous Scores', 'Extracurricular Activities', 'Sleep Hours', 'Sample Question Papers Practiced', 'Performance Index']].corr()\n",
|
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|
|
"\n",
|
|||
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"sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=\".2f\")"
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],
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"X = data.drop(\"Performance Index\", axis=1)\n",
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],
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"source": [
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{
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"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]])"
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},
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}
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"source": [
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"X_train_scaled"
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{
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"cell_type": "markdown",
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"source": [
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|||
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"# Evaluation"
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|||
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],
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{
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"execution_count": 108,
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"outputs": [
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{
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"data": {
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"text/plain": "LinearRegression()",
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|
|
"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
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},
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"execution_count": 108,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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|
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"from sklearn.linear_model import LinearRegression\n",
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"\n",
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"regressor = LinearRegression()\n",
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"regressor.fit(X_train_scaled, y_train)"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2024-03-19T20:48:04.831605300Z",
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"start_time": "2024-03-19T20:48:04.821478500Z"
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}
|
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},
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"id": "2d534985eacd2276"
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},
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{
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"cell_type": "code",
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"execution_count": 109,
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"outputs": [],
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"source": [
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"import numpy\n",
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"\n",
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"# Predicting Test Set Results\n",
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"y_pred = regressor.predict(X_test_scaled)\n",
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"y_pred = numpy.round(y_pred, decimals = 2)"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
|
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|
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"end_time": "2024-03-19T20:48:05.677281Z",
|
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"start_time": "2024-03-19T20:48:05.664556500Z"
|
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}
|
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},
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"id": "4e710bfcbee84c00"
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},
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{
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"cell_type": "code",
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"execution_count": 110,
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"outputs": [
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{
|
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Mean Squared Error : 4.213380000000001\n",
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"R Square : 0.9888499650063397\n"
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]
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}
|
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],
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"source": [
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|
|
"from sklearn.metrics import r2_score, mean_squared_error\n",
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"r2 = r2_score(y_test, y_pred)\n",
|
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|
|
"mean_er = mean_squared_error(y_test, y_pred)\n",
|
|||
|
|
"print('Mean Squared Error : ', mean_er)\n",
|
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|
|
"print('R Square : ', r2)"
|
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|
],
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"metadata": {
|
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|
"collapsed": false,
|
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|
"ExecuteTime": {
|
|||
|
|
"end_time": "2024-03-19T20:48:05.978638600Z",
|
|||
|
|
"start_time": "2024-03-19T20:48:05.967924800Z"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"id": "40f77178deb0dbee"
|
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|
|
},
|
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|
|
{
|
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|
"cell_type": "code",
|
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|
|
"execution_count": 111,
|
|||
|
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"outputs": [
|
|||
|
|
{
|
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|
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"data": {
|
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"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]",
|
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"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": [],
|
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|
|
"metadata": {
|
|||
|
|
"collapsed": false
|
|||
|
|
},
|
|||
|
|
"id": "b8dbc39f5ad7a856"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"kernelspec": {
|
|||
|
|
"display_name": "Python 3",
|
|||
|
|
"language": "python",
|
|||
|
|
"name": "python3"
|
|||
|
|
},
|
|||
|
|
"language_info": {
|
|||
|
|
"codemirror_mode": {
|
|||
|
|
"name": "ipython",
|
|||
|
|
"version": 2
|
|||
|
|
},
|
|||
|
|
"file_extension": ".py",
|
|||
|
|
"mimetype": "text/x-python",
|
|||
|
|
"name": "python",
|
|||
|
|
"nbconvert_exporter": "python",
|
|||
|
|
"pygments_lexer": "ipython2",
|
|||
|
|
"version": "2.7.6"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"nbformat": 4,
|
|||
|
|
"nbformat_minor": 5
|
|||
|
|
}
|