Task 1 .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Percentage of a student based on no. of study hours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing all libraries\n",
    "import pandas as pd\n",
    "import numpy as np  \n",
    "import matplotlib.pyplot as plt  \n",
    "%matplotlib inline\n",
    "import seaborn as sns"
   ]
  },
  {
   "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": [
    "df = pd.read_csv(\"student_scores - student_scores.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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",
    "df.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": "markdown",
   "metadata": {},
   "source": [
    "**From the graph above, we can clearly see that there is a positive linear relation between the number of hours studied and percentage of score.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.iloc[::-1].values\n",
    "y = df.iloc[:, 1].values  \n",
    "from sklearn.model_selection import train_test_split  \n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=0.2, random_state=0) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Done.\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression  \n",
    "lr = LinearRegression()  \n",
    "lr.fit(X_train, y_train) \n",
    "\n",
    "print(\"Training Done.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**making predictions**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = lr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>48.544652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27</td>\n",
       "      <td>62.114519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>69</td>\n",
       "      <td>66.315878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "      <td>42.825728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>62</td>\n",
       "      <td>56.207433</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Actual  Predicted\n",
       "0      20  48.544652\n",
       "1      27  62.114519\n",
       "2      69  66.315878\n",
       "3      30  42.825728\n",
       "4      62  56.207433"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})  \n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Testing with the given data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of Hours = [[9.25, 9.25]]\n",
      "Predicted Score = 92.90104289680458\n"
     ]
    }
   ],
   "source": [
    "# You can also test with your own data\n",
    "new_input = [[9.25, 9.25]]\n",
    "#hours.reshape(1, -1)\n",
    "new_pred = lr.predict(new_input)\n",
    "print(\"No of Hours = {}\".format(new_input))\n",
    "print(\"Predicted Score = {}\".format(new_pred[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Model Evaluation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 16.99231741063414\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics  \n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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    "name": "ipython",
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