Untitled18.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Data Science & Business Analytics Internship (GRIPMAY21)<h1>\n",
    "<h2>Deepak Kumar Sial<h2>\n",
    "<h3>Task1 - Prediction using Supervised ML <h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing required libraries\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt  \n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data imported successfully\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>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": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reading data from remote link\n",
    "\n",
    "url = \"http://bit.ly/w-data\"\n",
    "data = pd.read_csv(url)\n",
    "print(\"Data imported successfully\")\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot to see the distribution of data\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "sns.scatterplot(x=data.Hours,y=data.Scores)\n",
    "plt.xlabel('Hours Studied')  \n",
    "plt.ylabel('Marks Scored')  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data['Hours'].values.reshape(-1,1)\n",
    "y = data['Scores']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2.5],\n",
       "       [5.1],\n",
       "       [3.2],\n",
       "       [8.5],\n",
       "       [3.5],\n",
       "       [1.5],\n",
       "       [9.2],\n",
       "       [5.5],\n",
       "       [8.3],\n",
       "       [2.7],\n",
       "       [7.7],\n",
       "       [5.9],\n",
       "       [4.5],\n",
       "       [3.3],\n",
       "       [1.1],\n",
       "       [8.9],\n",
       "       [2.5],\n",
       "       [1.9],\n",
       "       [6.1],\n",
       "       [7.4],\n",
       "       [2.7],\n",
       "       [4.8],\n",
       "       [3.8],\n",
       "       [6.9],\n",
       "       [7.8]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     21\n",
       "1     47\n",
       "2     27\n",
       "3     75\n",
       "4     30\n",
       "5     20\n",
       "6     88\n",
       "7     60\n",
       "8     81\n",
       "9     25\n",
       "10    85\n",
       "11    62\n",
       "12    41\n",
       "13    42\n",
       "14    17\n",
       "15    95\n",
       "16    30\n",
       "17    24\n",
       "18    67\n",
       "19    69\n",
       "20    30\n",
       "21    54\n",
       "22    35\n",
       "23    76\n",
       "24    86\n",
       "Name: Scores, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Spliting the X,y into train and test \n",
    "\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 = 0.20, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing LinearRegression from sklearn\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# Creating object and fitting the model\n",
    "lin_reg = LinearRegression()\n",
    "model = lin_reg.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "intercept is: 2.018160041434683\n",
      "Liner coefficient is: [9.91065648]\n"
     ]
    }
   ],
   "source": [
    "print(\"intercept is:\",model.intercept_)\n",
    "print(\"Liner coefficient is:\",model.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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",
    "best_fitline = model.coef_*x+model.intercept_\n",
    "\n",
    "# Plotting for the data\n",
    "plt.scatter(x, y)\n",
    "plt.plot(x, best_fitline, color = 'r');\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicting for test dataset\n",
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>5</th>\n",
       "      <td>20</td>\n",
       "      <td>16.884145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>27</td>\n",
       "      <td>33.732261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>69</td>\n",
       "      <td>75.357018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>30</td>\n",
       "      <td>26.794801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>62</td>\n",
       "      <td>60.491033</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Actual  Predicted\n",
       "5       20  16.884145\n",
       "2       27  33.732261\n",
       "19      69  75.357018\n",
       "16      30  26.794801\n",
       "11      62  60.491033"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating Actual and Predicted dataset \n",
    "df1 = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 4.183859899002975\n",
      "RMSE: 4.6474476121003665\n",
      "R-Square: 0.9454906892105356\n"
     ]
    }
   ],
   "source": [
    "# Model Evaluation \n",
    "\n",
    "# Importing metrics from sklearn \n",
    "from sklearn.metrics import mean_squared_error,r2_score,mean_absolute_error\n",
    "\n",
    "# To find Mean Absolute Error(mse)\n",
    "mse = (mean_absolute_error(y_test, y_pred))\n",
    "print(\"MAE:\",mse)\n",
    "\n",
    "# To find Root Mean Squared Error(rmse)\n",
    "rmse = (np.sqrt(mean_squared_error(y_test, y_pred)))\n",
    "print(\"RMSE:\",rmse)\n",
    "\n",
    "# To find coefficient of determination\n",
    "r2 =  r2_score(y_test, y_pred)\n",
    "print(\"R-Square:\",r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of Hours = 9.25\n",
      "Predicted Score = 93.69\n"
     ]
    }
   ],
   "source": [
    "# Testing with your own data\n",
    "hours = np.array([9.25]) # No. of hours should be mentioned inside array\n",
    "hours = hours.reshape(-1,1)\n",
    "own_pred = model.predict(hours)\n",
    "print(\"No of Hours = {}\".format(float(hours)))\n",
    "print(\"Predicted Score = {}\".format(round(own_pred[0],2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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