task1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task-1 Prediction using Supervised ML"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# By - Saivineeth Vuppala"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Here we are going to predict the percentage of marks of an student based on the number of study hours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import all the required 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": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv('https://raw.githubusercontent.com/AdiPersonalWorks/Random/master/student_scores%20-%20student_scores.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25, 2)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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.plot(data['Hours'],data['Scores'],'ro')\n",
    "plt.title('Hours vs Percentage')\n",
    "plt.xlabel('Hours Studied')\n",
    "plt.ylabel('Percentage Score')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Preparing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = score_data.iloc[:, :-1].values\n",
    "y = score_data.iloc[:, 1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.018160041434683 , 9.910656480642237\n"
     ]
    }
   ],
   "source": [
    "print(regressor.intercept_,\",\",regressor.coef_[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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 scatterplot to visualize the intercept and slope\n",
    "line = regressor.coef_*X+regressor.intercept_\n",
    "\n",
    "# Plotting for the test data\n",
    "plt.scatter(X, y)\n",
    "plt.plot(X, line);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Making predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Evaluating the algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 4.183859899002975\n",
      "Mean Squared Error: 21.5987693072174\n",
      "Root Mean Squared Error: 4.6474476121003665\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))\n",
    "print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))\n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of Hours = 9.25\n",
      "Predicted Score = 93.69173248737538\n"
     ]
    }
   ],
   "source": [
    "# We can test with some new data \n",
    "# lets find the predicted score if a student studies for 9.25 hrs/ day?\n",
    "hours = 9.25\n",
    "test = np.array([hours])\n",
    "test = test.reshape(-1, 1)\n",
    "pred = regressor.predict(test)\n",
    "print(\"No of Hours = {}\".format(hours))\n",
    "print(\"Predicted Score = {}\".format(pred[0]))"
   ]
  }
 ],
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