Prediction using Supervised ML.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prediction using Supervised ML (Task 1) - The Sparks Foundation "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Task 1:\n",
    "\n",
    "Task is to predict a percentage of students based on the number of study hours. In this task, simple linear regression is used for the predictions as the data has only **Two Variables**.\n",
    "1. Hours : Number of hours student studies \n",
    "2. Scores: Scores obtained by the student.\n",
    "\n",
    "Linear Regression:\n",
    "\n",
    "In simple words, Linear regression is a straight line that can predict any relationship between two points(variables). This is an statistical method that is commonly used for predictive analysis. \n",
    "\n",
    "There is two types of linear regression: Simple linear - Single independent variable is used for predicting the value of dependent variable. Multiple linear regression - Two or more independent variable is used for predicting the value of dependent variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing all necessary packages \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"C:/Users/jeevi/OneDrive/Documents/spark/student_scores - student_scores.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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",
       "      <td>0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>8.5</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>3.5</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>9.2</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>5.5</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>8.3</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>2.7</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>7.7</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>5.9</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>4.5</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>3.3</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>1.1</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>8.9</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>2.5</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>1.9</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>6.1</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>7.4</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>2.7</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>4.8</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>3.8</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>6.9</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# printing the data\n",
    "data"
   ]
  },
  {
   "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",
      "Hours     25 non-null float64\n",
      "Scores    25 non-null int64\n",
      "dtypes: float64(1), int64(1)\n",
      "memory usage: 528.0 bytes\n"
     ]
    }
   ],
   "source": [
    "# Looking at the concise summary of the data\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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",
       "      <td>count</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>5.012000</td>\n",
       "      <td>51.480000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>2.525094</td>\n",
       "      <td>25.286887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>4.800000</td>\n",
       "      <td>47.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>7.400000</td>\n",
       "      <td>75.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\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": [
    "# Looking at the statistical details of the data\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Assumptions of Linear Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are few assumptions for linear regression that can be checked in this task.\n",
    "1. Two variables should be in a linear relationship (In this task we are using scatter plot to identify the relationship between the variables)\n",
    "2. Variable should be normally distributed(Multivariate normal)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting the data"
   ]
  },
  {
   "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": [
    "# Plotting the data gives betetr understanding of the distribution\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": [
    "From the above ploting we can clearly see that there is a **Positive relationship** between two variables. That is the more hours a student studies, higher the score he/she will secure. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting independent and dependent variable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are splitting the data into independent variable(attributes) or dependent variable(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data.iloc[:, :-1].values\n",
    "y = data.iloc[:, 1].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting train and test data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next step is to split the data into train and test where the model is fitted to train dataset and predicted using the test dataset. To split the data we are using train_test_split() method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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=0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the linear regression package from Scikit-Learn\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assigning the linear regression function to variable (lm)\n",
    "lm = LinearRegression(normalize = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=True)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Training the model using training data\n",
    "lm.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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 = lm.coef_*x+lm.intercept_\n",
    "\n",
    "# Plotting for the test data\n",
    "plt.scatter(x, y)\n",
    "plt.plot(x, line);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predictions using test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicting the scores using text data and saving it to a variable(predictions)\n",
    "predictions = lm.predict( X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>17.053665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>33.694229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>69</td>\n",
       "      <td>74.806209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "      <td>26.842232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>62</td>\n",
       "      <td>60.123359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "      <td>39.567369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>24</td>\n",
       "      <td>20.969092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>86</td>\n",
       "      <td>78.721636</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Actual  Predicted\n",
       "0      20  17.053665\n",
       "1      27  33.694229\n",
       "2      69  74.806209\n",
       "3      30  26.842232\n",
       "4      62  60.123359\n",
       "5      35  39.567369\n",
       "6      24  20.969092\n",
       "7      86  78.721636"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Comparing Actual vs Predicted\n",
    "df = pd.DataFrame({'Actual': y_test, 'Predicted': predictions})  \n",
    "df "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### The main question is to predict the score if a student studies for 9.25 hrs/ day?\n",
    "\n",
    "To find this we are assinging 9.25 to a variable names hours and passing it to the predict function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of Hours = 9.25\n",
      "Predicted Score = 92.91505723477056\n"
     ]
    }
   ],
   "source": [
    "hours = 9.25\n",
    "pred = lm.predict([[hours]])\n",
    "print(\"No of Hours = {}\".format(hours))\n",
    "print(\"Predicted Score = {}\".format(pred[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are different methods to evaluate the model. In this task three metrics are used to evaluate the model.\n",
    "\n",
    "1. MAE: Mean Absolute Error - This metrics shows whether the model is perfect predictor or not but does not show underperformance or overperformance of the model. If MAE is closer to 0 then the model is good predictor.\n",
    "\n",
    "2. MSE: Mean Square Error - The square difference between the actual and predicted value before summing them all will give MSE value. MSE value can range beteen 0 to infinity. If the MSE value is close to 0 then the model is not a good predictor.\n",
    "\n",
    "3. RMSE: Root Mean Square Error - RMSE can be used as an alternative metrics for MSE. Because MSE value can be bigger and interpretation can be harder. USing RMSE we can overcome interpretation issue. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 4.4197278080276545\n",
      "MSE: 22.96509721270044\n",
      "RMSE: 4.792191274636316\n"
     ]
    }
   ],
   "source": [
    "## Calculating MAE, MSE and RMSE\n",
    "from sklearn import metrics\n",
    "\n",
    "print('MAE:', metrics.mean_absolute_error(y_test, predictions))\n",
    "print('MSE:', metrics.mean_squared_error(y_test, predictions))\n",
    "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, predictions)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above metrics we can see that the model is doing a good predictions but it is not a perfect predictor. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A simple linear regression model to predict the score based on the student study hours is successfully completed.\n",
    "\n",
    "Thank you!!"
   ]
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}