Task 1 - Prediction using Supervised Learning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "df93c053",
   "metadata": {},
   "source": [
    "### Aditya Raj Singh Chauhan - GRIPFebruary23"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89aa1c0a",
   "metadata": {},
   "source": [
    "### Task 1 - Prediction using Supervised ML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a7844043",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import math\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3a987a4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('https://raw.githubusercontent.com/AdiPersonalWorks/Random/master/student_scores%20-%20student_scores.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "061223c8",
   "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b09b0722",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df.iloc[:,[False,True]].values\n",
    "x = df.iloc[:, [True, False]].values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "477dc5ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(x = 'Hours', y = 'Scores', style = 'o')\n",
    "plt.title('Hours vs Scores')    \n",
    "plt.xlabel('Hours Studied')    \n",
    "plt.ylabel('Scores')    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ffdfe7fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(x, y, test_size = 0.20, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "686970f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "reg = LinearRegression()\n",
    "reg.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "796c5d47",
   "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": [
    "line = reg.coef_*x+reg.intercept_  \n",
    "plt.scatter(x, y)  \n",
    "plt.plot(x, line);  \n",
    "plt.show()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6192d4a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_pred = reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4ff7c2cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.6474476121003665"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "rmse = math.sqrt(mean_squared_error(Y_test,Y_pred))\n",
    "rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a8cd3d3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[93.69173249]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_ans = reg.predict([[9.25]])\n",
    "y_ans"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "badbd1dd",
   "metadata": {},
   "source": [
    "Hence, predicted score for 9.25hrs/day is \"93.69\"."
   ]
  }
 ],
 "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}