Task 1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9bc571af",
   "metadata": {},
   "source": [
    "# GRIP: The Sparks Foundation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5630e7a",
   "metadata": {},
   "source": [
    "# gripaugust21"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d3c006a",
   "metadata": {},
   "source": [
    "Author: Netra Kulkarni\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "136f851b",
   "metadata": {},
   "source": [
    "Data Science and Business Analytics Intern"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "430ac769",
   "metadata": {},
   "source": [
    "Task 1: Prediction using supervised ML\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46159b1d",
   "metadata": {},
   "source": [
    "To Predict the percentage of an student based on the no. of study hours."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0a27198f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing required libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "80ac5d37",
   "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": [
    "# reading the dataset\n",
    "url = 'http://bit.ly/w-data'\n",
    "df = pd.read_csv(url)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "0b4400c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 25 entries, 0 to 24\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Hours   25 non-null     float64\n",
      " 1   Scores  25 non-null     int64  \n",
      "dtypes: float64(1), int64(1)\n",
      "memory usage: 528.0 bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "fe7fd734",
   "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": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77ca9144",
   "metadata": {},
   "source": [
    "to plot a scatter plot to see the distribution of scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a5b87dc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "sns.scatterplot(data=df,x='Hours',y='Scores')\n",
    "plt.title('Study hours vs Percentage')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "690cdbd5",
   "metadata": {},
   "source": [
    "From the graph above we can notice that there is positive linear relation between no. of hours studied and percentage scores"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96430f23",
   "metadata": {},
   "source": [
    "Now we will seperate the data as features and labels that is x and y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "1a335540",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.drop('Scores',axis=1)\n",
    "y = df.Scores"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3d30d29",
   "metadata": {},
   "source": [
    "Now we will fit a linear regression model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "2cdbb5ba",
   "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=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "63f9bd28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "model = LinearRegression()\n",
    "model.fit(x_train,y_train)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4597240",
   "metadata": {},
   "source": [
    "To plot the regression line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "042a29fd",
   "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": [
    "reg_line = model.coef_*x+model.intercept_\n",
    "\n",
    "plt.scatter(x,y)\n",
    "plt.plot(x,reg_line,color='red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bc92e8a",
   "metadata": {},
   "source": [
    "Making predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "282318ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "4c969339",
   "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>16</th>\n",
       "      <td>30</td>\n",
       "      <td>26.845397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>85</td>\n",
       "      <td>77.458594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>35</td>\n",
       "      <td>39.498696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>69</td>\n",
       "      <td>74.538601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>60</td>\n",
       "      <td>56.045318</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Actual  Predicted\n",
       "16      30  26.845397\n",
       "10      85  77.458594\n",
       "22      35  39.498696\n",
       "19      69  74.538601\n",
       "7       60  56.045318"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# comparing actual vs predicted\n",
    "df_new = pd.DataFrame({'Actual':y_test, 'Predicted':y_pred})\n",
    "df_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "b06bdc96",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No. of hours = 9.25\n",
      "Percentage score = 92.5452195402996\n"
     ]
    }
   ],
   "source": [
    "# What will be score if a student studies for 9.25 hours/day?\n",
    "hours = np.array(9.25).reshape(1,-1)\n",
    "score = model.predict(hours)\n",
    "score\n",
    "print(f'No. of hours = {hours[0][0]}')\n",
    "print(f'Percentage score = {score[0]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e13c272",
   "metadata": {},
   "source": [
    "Evaluating the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "18f3d94c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.164846812544612"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Root Mean Squared Error\n",
    "from sklearn.metrics import mean_squared_error\n",
    "rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
    "rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0260f79a",
   "metadata": {},
   "source": [
    "As mean value of scores is 51, error in predicted score 5.16 is 10.12% which states that fitting of our model is good."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e024a1ff",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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