SUPER_REG_BEGINNER.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "09a29b60",
   "metadata": {},
   "source": [
    "# Importing Required libraries\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "690b876e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split,GridSearchCV \n",
    "from sklearn import metrics \n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0819544",
   "metadata": {},
   "source": [
    "# Loading Data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f2e94ef1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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</th>\n",
       "      <th>Scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.5</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.1</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.2</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.5</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.5</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.5</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9.2</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.5</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8.3</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.7</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>7.7</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5.9</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.5</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3.3</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.1</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>8.9</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2.5</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.9</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>6.1</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>7.4</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2.7</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>4.8</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3.8</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>6.9</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>7.8</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Hours  Scores\n",
       "0     2.5      21\n",
       "1     5.1      47\n",
       "2     3.2      27\n",
       "3     8.5      75\n",
       "4     3.5      30\n",
       "5     1.5      20\n",
       "6     9.2      88\n",
       "7     5.5      60\n",
       "8     8.3      81\n",
       "9     2.7      25\n",
       "10    7.7      85\n",
       "11    5.9      62\n",
       "12    4.5      41\n",
       "13    3.3      42\n",
       "14    1.1      17\n",
       "15    8.9      95\n",
       "16    2.5      30\n",
       "17    1.9      24\n",
       "18    6.1      67\n",
       "19    7.4      69\n",
       "20    2.7      30\n",
       "21    4.8      54\n",
       "22    3.8      35\n",
       "23    6.9      76\n",
       "24    7.8      86"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = 'https://raw.githubusercontent.com/AdiPersonalWorks/Random/master/student_scores%20-%20student_scores.csv' \n",
    "df = pd.read_csv(url)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30d37544",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ced00b22",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 25 entries, 0 to 24\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Hours   25 non-null     float64\n",
      " 1   Scores  25 non-null     int64  \n",
      "dtypes: float64(1), int64(1)\n",
      "memory usage: 528.0 bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0ba77188",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Hours</th>\n",
       "      <th>Scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.012000</td>\n",
       "      <td>51.480000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.525094</td>\n",
       "      <td>25.286887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.100000</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.700000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.800000</td>\n",
       "      <td>47.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.400000</td>\n",
       "      <td>75.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.200000</td>\n",
       "      <td>95.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Hours     Scores\n",
       "count  25.000000  25.000000\n",
       "mean    5.012000  51.480000\n",
       "std     2.525094  25.286887\n",
       "min     1.100000  17.000000\n",
       "25%     2.700000  30.000000\n",
       "50%     4.800000  47.000000\n",
       "75%     7.400000  75.000000\n",
       "max     9.200000  95.000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3413fdf5",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 1500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,4))\n",
    "sns.lineplot(data=df , x='Hours',y='Scores')\n",
    "plt.xlabel('Number of Hours')\n",
    "plt.ylabel('Score')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e9af760a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,4))\n",
    "sns.scatterplot(data=df , x='Hours',y='Scores')\n",
    "plt.xlabel('Number of Hours')\n",
    "plt.ylabel('Score')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "968171f3",
   "metadata": {},
   "source": [
    "# Splitting the Data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5525ff18",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df['Hours'].values\n",
    "x = x.reshape(-1,1)\n",
    "y = df['Scores'].values \n",
    "x_train ,x_test , y_train , y_test = train_test_split(x,y,test_size=0.2,random_state=60)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "513cd298",
   "metadata": {},
   "source": [
    "# Data Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2cdfb6ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Actual Values</th>\n",
       "      <th>Predicted Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17</td>\n",
       "      <td>13.765605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30</td>\n",
       "      <td>29.271268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>62</td>\n",
       "      <td>60.282595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21</td>\n",
       "      <td>27.333060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25</td>\n",
       "      <td>29.271268</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Actual Values  Predicted Values\n",
       "0             17         13.765605\n",
       "1             30         29.271268\n",
       "2             62         60.282595\n",
       "3             21         27.333060\n",
       "4             25         29.271268"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "regression_model = model.fit(x_train,y_train)\n",
    "yhat = regression_model.predict(x_test)\n",
    "frame = {'Actual Values':y_test,'Predicted Values':yhat}\n",
    "frame = pd.DataFrame(frame)\n",
    "frame\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "85285481",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,4))\n",
    "sns.regplot(data=df , x='Hours',y='Scores')\n",
    "plt.xlabel('Number of Hours')\n",
    "plt.ylabel('Score')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd635dec",
   "metadata": {},
   "source": [
    "# Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ff6797c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.944131972730691"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.r2_score(y_test,yhat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee02b975",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}