TASK 1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <span style='background :yellow' > ***The Spark Foundation - Task-1*** </span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  ***Prediction using supervised learning ,Predict the percentage of an student based on the no. of study hours.***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ***Import The Required Libraries***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_absolute_error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ***Reading The CSV file***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.5</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9.2</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.5</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8.3</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.7</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>7.7</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5.9</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.5</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3.3</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.1</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>8.9</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2.5</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.9</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>6.1</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>7.4</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2.7</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>4.8</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3.8</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>6.9</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>7.8</td>\n",
       "      <td>86</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\n",
       "5     1.5      20\n",
       "6     9.2      88\n",
       "7     5.5      60\n",
       "8     8.3      81\n",
       "9     2.7      25\n",
       "10    7.7      85\n",
       "11    5.9      62\n",
       "12    4.5      41\n",
       "13    3.3      42\n",
       "14    1.1      17\n",
       "15    8.9      95\n",
       "16    2.5      30\n",
       "17    1.9      24\n",
       "18    6.1      67\n",
       "19    7.4      69\n",
       "20    2.7      30\n",
       "21    4.8      54\n",
       "22    3.8      35\n",
       "23    6.9      76\n",
       "24    7.8      86"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url=\"https://raw.githubusercontent.com/AdiPersonalWorks/Random/master/student_scores%20-%20student_scores.csv\"\n",
    "df=pd.read_csv(url)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ***Checking how many null values are there in dataset***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hours     0\n",
       "Scores    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ***Descriptive analysis of the dataset using describe function***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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",
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      ],
      "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ***information about the dataset***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": "markdown",
   "metadata": {},
   "source": [
    "### ***Plotting a scatter plot showing relationship between No of Hours vs scores***  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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.xlabel('Hours')\n",
    "plt.ylabel('Scores')\n",
    "plt.title('Hours VS Scores')\n",
    "plt.scatter(df.Hours,df.Scores,color='green')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ***Two variables for the regression***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=np.array(df.Hours)\n",
    "y=np.array(df.Scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ***Reshaping the numpy array for vertical output***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=x.reshape(-1,1)\n",
    "y=y.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ***Splitting the data into test data and train data***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ***Calling the linear function and reshaping all the data and fitting it to the model***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg=LinearRegression()\n",
    "X_test=X_test.reshape(-1,1)\n",
    "y_test=y_test.reshape(-1,1)\n",
    "X_train=X_train.reshape(-1,1)\n",
    "y_train=y_train.reshape(-1,1)\n",
    "reg.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ***Formula of simple linear regression***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "line1=reg.coef_*X_train+reg.intercept_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ***Plotting a scatter plot for training dataset***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'TRAINING DATA')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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.scatter(X_train,y_train,color='green')\n",
    "plt.plot(X_train,line1,color='red',linewidth=2)\n",
    "plt.title('TRAINING DATA')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "line2=reg.coef_*X_test+reg.intercept_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ***Plotting  a scatter plot for testing dataset***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'TESTING DATA')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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.scatter(X_test,y_test,color='red')\n",
    "plt.plot(X_test,line2,color='green',linewidth=2)\n",
    "plt.title('TESTING DATA')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ***predicting the test value***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[16.88414476],\n",
       "       [33.73226078],\n",
       "       [75.357018  ],\n",
       "       [26.79480124],\n",
       "       [60.49103328]])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predict=reg.predict(X_test)\n",
    "y_predict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ***Comparing the actuals with the prediction to see the difference***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Comparing the actuals with prediction: \n"
     ]
    },
    {
     "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>prediction</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  prediction\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": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=y_test.flatten()\n",
    "b=y_predict.flatten()\n",
    "compare={\"ACTUAL\":a,\"prediction\":b}\n",
    "label={0,1,2,3,4}\n",
    "print(\"Comparing the actuals with prediction: \")\n",
    "df=pd.DataFrame(compare,index=label)\n",
    "df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ***Evaluating the model***\n",
    "#### ***Evaluation is a very impotant step for knowing the accuracy of the model  by using MAE***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.183859899002975"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mae=mean_absolute_error(y_test,y_predict)\n",
    "mae"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ***prediction***\n",
    "### ***What will be predicted score if a student studies for 9.25 hrs/ day?***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[93.69173249]])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_predict=reg.predict([[9.25]])\n",
    "data_predict"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}