Task1-Prediction using Supervised ML.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import the required libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing data and basic exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The data is imported successfully\n"
     ]
    },
    {
     "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": [
    "#read data from input url\n",
    "url=\"http://bit.ly/w-data\"\n",
    "data=pd.read_csv(url)\n",
    "data1=data\n",
    "print(\"The data is imported successfully\")\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(25, 2)\n"
     ]
    }
   ],
   "source": [
    "#Determine the size of the data\n",
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(data.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "\n",
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       "</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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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R5EJgHXAuEODaJK8HTgHur6qLAJI8f2LfUnoaR+A6LFTVAvBKYBZ4iMXg/jVgV1Xd2B3zrW5a5KeBv+7avszi7/PsCfDrquob3esLu79bgVuAl7EY6HcAFyS5PMnrquqbE/iK0j4cgeuwUVXfBUbAKMkdwLuBcQ865Bk+5vG9jvtwVX18nw9IXgm8Gfhwkm1V9QcHXLh0gByB67CQ5KVJ1j2t6WxgB/CCJK/qjjkuyRHAvwDv7NpeArwQ+MqYj/0H4FeSPLc79rQkpyZ5AfBEVf0N8BHgnKG+l/RMHIHrcPFc4M+SHA/sBr7K4nTKJ7r2Y1ic/74A+AvgL7tR+m7gXd2dKz/wgVW1LcmZwOe79xaAXwReDPxxku8BTwG/PoHvJ+3DR+klqVFOoUhSowxwSWqUAS5JjTLAJalRBrgkNcoAl6RGGeCS1Kj/Ayo1UwCYJ/XsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Check if the data has any missing values (null)\n",
    "data.isna().sum()\n",
    "\n",
    "# Checkfor outliers using multiple boxplots\n",
    "for column in data:\n",
    "    if data[column].dtype in ['int64','float64']:\n",
    "        plt.figure()\n",
    "        data.boxplot(column=[column])\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DATA VISUALIZATION\n",
    "Now let's plot a graph of our data so that it will give us clear idea about data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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",
    "data.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": [
    "# Preparing The Data\n",
    "The next step is to divide the data into \"attributes\" (inputs) and \"labels\" (outputs)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.iloc[:, :-1].values\n",
    "y = data.iloc[:, 1].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Splitting the data into train and test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((20, 1), (5, 1))"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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)\n",
    "X_train.shape , X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building a model on the train data and checking how well the model fits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing required module to build the model\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#building the model\n",
    "model = LinearRegression()\n",
    "model.fit(X_train,y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training complete.\n"
     ]
    }
   ],
   "source": [
    "print(\"Training complete.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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",
    "line = model.coef_*X+model.intercept_\n",
    "# Plotting for the test data\n",
    "plt.scatter(X, y)\n",
    "plt.plot(X, line);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Now that we have trained our algorithm, it's time to make some predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.5]\n",
      " [3.2]\n",
      " [7.4]\n",
      " [2.5]\n",
      " [5.9]]\n"
     ]
    }
   ],
   "source": [
    "### Testing data - In Hours\n",
    "print(X_test)\n",
    "\n",
    "### Predicting the scores\n",
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparing Actual vs Predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Comparing Actual vs Predicted\n",
    "data = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Checking the accuracy scores for training and test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Score (R-Squared):  0.9515510725211553\n",
      "Test Score (R-Squared) 0.9454906892105354\n"
     ]
    }
   ],
   "source": [
    "train_score=model.score(X_train,y_train)\n",
    "test_score=model.score(X_test,y_test)\n",
    "print('Train Score (R-Squared): ',train_score)\n",
    "print('Test Score (R-Squared)',test_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": [
    "### Ploting the line graph to depict the diffrence between the actual and predicted value.\n",
    "data.plot(kind='line', figsize=(8,6))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluate the model's performance on test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train RMSE :  5.558613350226344\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "#root mean square error (RMSE) calculation for train data\n",
    "train_predictions = model.predict(X_train)\n",
    "train_RMSE=mean_squared_error(y_train,train_predictions)**0.5\n",
    "print('Train RMSE : ',train_RMSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test RMSE :  4.647447612100373\n"
     ]
    }
   ],
   "source": [
    "#root mean square error (RMSE) calculation for test data\n",
    "test_predictions = model.predict(X_test)\n",
    "test_RMSE=mean_squared_error(y_test,test_predictions)**0.5\n",
    "print('Test RMSE : ',test_RMSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score of student who studied for 9.25 hours a dat [93.69173249]\n"
     ]
    }
   ],
   "source": [
    "#Let's predict the score for 9.25 hpurs\n",
    "print('Score of student who studied for 9.25 hours a dat', model.predict([[9.25]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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
}