_TASK 1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fc7e476f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split \n",
    "from sklearn.linear_model import LinearRegression\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "39b64ca2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Import Succesful!\n"
     ]
    }
   ],
   "source": [
    "url = r\"https://raw.githubusercontent.com/AdiPersonalWorks/Random/master/student_scores%20-%20student_scores.csv\"\n",
    "student_data = pd.read_csv(url)\n",
    "print(\"Import Succesful!\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7eeea551",
   "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>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",
       "  </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"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "student_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "113e4e38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "student_data.plot(x='Hours', y='Scores', style='.')  \n",
    "plt.title('Scores Obtained vs Hours Studied')  \n",
    "plt.xlabel('Hours Spent Studying')  \n",
    "plt.ylabel('Percentage Score Obtained')  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "23cad602",
   "metadata": {},
   "outputs": [],
   "source": [
    "X= student_data.iloc[:, -1].values\n",
    "y= student_data.iloc[:, 1].values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "7893f232",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training complete.\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0) \n",
    "regressor = LinearRegression()  \n",
    "regressor.fit(X_train.reshape(-1,1), y_train) \n",
    "\n",
    "print(\"Training complete.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "73fe6aa7",
   "metadata": {},
   "outputs": [],
   "source": [
    "line = regressor.coef_*X + regressor.intercept_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "1c383e9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X,y)\n",
    "plt.plot(X, line, color='red')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "e0521fee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([20., 27., 69., 30., 62.])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "y_pred=regressor.predict(X_test.reshape(-1,1))\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "6d76b6ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=regressor.predict(X_test.reshape(-1,1), )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "94e727a5",
   "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</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>69</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>62</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Actual  Predicted\n",
       "0      20       20.0\n",
       "1      27       27.0\n",
       "2      69       69.0\n",
       "3      30       30.0\n",
       "4      62       62.0"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred}) \n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "a22cb18f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Score: 1.0\n",
      "Test Score: 1.0\n"
     ]
    }
   ],
   "source": [
    "print(\"Training Score:\",regressor.score(X_train.reshape(-1,1),y_train.reshape(-1,1)))\n",
    "print(\"Test Score:\",regressor.score(X_test.reshape(-1,1),y_test.reshape(-1,1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "25fd4e0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='bar',figsize=(8,5))\n",
    "plt.grid(which='major', linewidth='1', color='red')\n",
    "plt.grid(which='minor', linewidth='0.5', color='green')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "c5054a7e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of Hours = 9.25\n",
      "Predicted Score = 9.250000000000012\n"
     ]
    }
   ],
   "source": [
    "hours = 9.25\n",
    "test = np.array([hours])\n",
    "test = test.reshape(-1, 1)\n",
    "own_pred = regressor.predict(test)\n",
    "print(\"No of Hours = {}\".format(hours))\n",
    "print(\"Predicted Score = {}\".format(own_pred[0]))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}