TSF - Task-2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task 2: Score Prediction using Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import all necessary libraries\n",
    "import pandas as pd\n",
    "import numpy as np  \n",
    "import matplotlib.pyplot as plt  \n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#import data\n",
    "url = \"http://bit.ly/w-data\"\n",
    "s_data = pd.read_csv(url)\n",
    "s_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": [
    "# Plotting the distribution of scores\n",
    "s_data.plot(x='Hours', y='Scores', style='o')  \n",
    "plt.title('Hours vs Percentage')  \n",
    "plt.xlabel('Hours Studied')  \n",
    "plt.ylabel('Percentage Score')  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.array(s_data[[\"Hours\"]])  \n",
    "y = np.array(s_data[\"Scores\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Splitting data into train data and test data\n",
    "from sklearn.model_selection import train_test_split  \n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
    "                            test_size=0.2, random_state=0) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Training Data\n",
    "from sklearn.linear_model import LinearRegression\n",
    "model = LinearRegression()\n",
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coefficient of determination: 0.9515510725211553\n"
     ]
    }
   ],
   "source": [
    "r_sq = model.score(X_train, y_train)\n",
    "print('coefficient of determination:', r_sq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept:  2.0181600414346974\n"
     ]
    }
   ],
   "source": [
    "print(\"Intercept: \", model.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Slope: [9.91065648]\n"
     ]
    }
   ],
   "source": [
    "print(\"Slope:\",model.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": [
    "# Plotting the regression line\n",
    "line = model.coef_*X+model.intercept_\n",
    "\n",
    "plt.scatter(X, y)\n",
    "plt.plot(X, line);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>16.884145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27</td>\n",
       "      <td>33.732261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>69</td>\n",
       "      <td>75.357018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "      <td>26.794801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>62</td>\n",
       "      <td>60.491033</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Actual  Predicted\n",
       "0      20  16.884145\n",
       "1      27  33.732261\n",
       "2      69  75.357018\n",
       "3      30  26.794801\n",
       "4      62  60.491033"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Predicting Test Data\n",
    "y_pred = model.predict(X_test)\n",
    "# Comparing Actual vs Predicted\n",
    "df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})  \n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Score: 93.69173248737535\n"
     ]
    }
   ],
   "source": [
    "#Prediction on other data\n",
    "hours = [[9.25]]\n",
    "pred = model.predict(hours)\n",
    "print(\"Predicted Score: {}\".format(pred[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 4.183859899002975\n"
     ]
    }
   ],
   "source": [
    "#Evaluating the Model\n",
    "from sklearn import metrics  \n",
    "print('Mean Absolute Error:', \n",
    "      metrics.mean_absolute_error(y_test, y_pred)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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