Task-2_Linear_Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# To Explore Supervised Machine Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this regression task we will predict the percentage of\n",
    "marks that a student is expected to score based upon the\n",
    "number of hours they studied. This is a simple linear\n",
    "regression task as it involves just two variables.\n",
    "\n",
    "Data can be found <a href=\"http://bit.ly/w-data\">here</a>\n",
    "\n",
    "<b>Problem statement:</b> What will be predicted score if a student study for 9.25 hrs in a\n",
    "day?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What is Supervised Learning ?\n",
    "Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A Supervised learning algorithm can be written simply as <b>y = f(X) </b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What is regression ?\n",
    "\n",
    "Supervised learning can be split into two subcategories: <i>Classification</i> and <i>Regression</i>.\n",
    "\n",
    "Regression is a technique from statistics that is used to predict values of a desired target quantity when the target quantity is continuous. Regression predictive modeling is the task of approximating a mapping function (f) from input variables (X) to a continuous output variable (y).\n",
    "\n",
    "It is mainly used for prediction, forecasting, time series modeling, and determining the causal-effect relationship between variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Importing Done.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd #for data importing\n",
    "\n",
    "import numpy as np #for mathematical operations\n",
    "\n",
    "import matplotlib.pyplot as plt #for visualization\n",
    "%matplotlib inline\n",
    "import seaborn as sns #for visualization\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "print(\"Importing Done.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading data in dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Importing Done.\n"
     ]
    }
   ],
   "source": [
    "path=\"http://bit.ly/w-data\"\n",
    "df=pd.read_csv(path)\n",
    "print(\"Importing Done.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Understanding Data (Data Exploration)\n",
    "Dataset: In this dataset ,there are only two variables:\n",
    "\n",
    "    Hours : Number of hours a student is studing.\n",
    "\n",
    "    Scores : percentage of marks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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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",
       "  </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": [
    "#Print first 5 rows of data\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25, 2)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dimension of our data (rows, columns)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Hours', 'Scores'], dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print all columns names\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hours     float64\n",
       "Scores      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data types of each column\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hours     0\n",
       "Scores    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for missing values or null values\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Describing data in mathematical functions\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>Hours</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.976191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scores</th>\n",
       "      <td>0.976191</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Hours    Scores\n",
       "Hours   1.000000  0.976191\n",
       "Scores  0.976191  1.000000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Correlation between variables of data\n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a5a5f36ee0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr(),annot=True,cmap=\"plasma\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Visualisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1a5a66d19d0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pairplot so we get both Histogram as well as Scatterplot\n",
    "sns.pairplot(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a5a68ed520>"
      ]
     },
     "execution_count": 12,
     "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": [
    "# Understanding the distribution curve of Hours\n",
    "sns.distplot(df['Hours'],bins=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a5a694e280>"
      ]
     },
     "execution_count": 13,
     "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": [
    "# Understanding the distribution curve of Hours\n",
    "sns.distplot(df['Scores'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We can see that the Score value is linearly dependent on Hours\n"
     ]
    }
   ],
   "source": [
    "# Scatterplot \n",
    "sns.scatterplot(x='Hours',y=\"Scores\",data=df,color='black')\n",
    "sns.set(style=\"whitegrid\")\n",
    "plt.title('Hours vs Percentage')  \n",
    "plt.xlabel('Hours Studied')  \n",
    "plt.ylabel('Percentage Score')\n",
    "plt.show()\n",
    "print(\"We can see that the Score value is linearly dependent on Hours\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the regression line to see the fitting\n",
    "sns.regplot(x='Hours',y='Scores',data=df)\n",
    "plt.title(\"Initial Chart\")\n",
    "plt.xlabel(\"Hours Studied\")\n",
    "plt.ylabel(\"Percentage\")\n",
    "plt.grid()\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preparation and Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.iloc[:, :-1].values  \n",
    "y = df.iloc[:, 1].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting Data in Training and Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=0.25, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training the algorithm "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training complete.\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression  \n",
    "regressor = LinearRegression()  \n",
    "regressor.fit(X_train, y_train) \n",
    "\n",
    "print(\"Training complete.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting the regression line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the regression line\n",
    "#y=mx+c\n",
    "line = regressor.coef_*X+regressor.intercept_\n",
    "\n",
    "# Plotting for the test data\n",
    "plt.scatter(X, y,color='red')\n",
    "plt.plot(X, line,color=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = regressor.predict(X_test) # Predicting the scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>16.844722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27</td>\n",
       "      <td>33.745575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>69</td>\n",
       "      <td>75.500624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "      <td>26.786400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>62</td>\n",
       "      <td>60.588106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>35</td>\n",
       "      <td>39.710582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>24</td>\n",
       "      <td>20.821393</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Actual  Predicted\n",
       "0      20  16.844722\n",
       "1      27  33.745575\n",
       "2      69  75.500624\n",
       "3      30  26.786400\n",
       "4      62  60.588106\n",
       "5      35  39.710582\n",
       "6      24  20.821393"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction=pd.DataFrame({\"Actual\":y_test,\"Predicted\":y_pred})\n",
    "prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9367661043365055\n"
     ]
    }
   ],
   "source": [
    "accuracy = regressor.score(X_test, y_test)\n",
    "print(\"Accuracy: \" + str(accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 4.130879918502486\n",
      "Mean Squared Error: 20.33292367497997\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics  \n",
    "print('Mean Absolute Error:',metrics.mean_absolute_error(y_test, y_pred)) \n",
    "print('Mean Squared Error:',metrics.mean_squared_error(y_test, y_pred)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem statement solution (Conclusion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If a student study for 9.25 hours in a day then he will score 93.89 percent.\n"
     ]
    }
   ],
   "source": [
    "hours = [[9.25]]\n",
    "our_prediction = regressor.predict(hours)\n",
    "print(\"If a student study for {} hours in a day then he will score {:.2f} percent.\".format(hours[0][0],our_prediction[0]))"
   ]
  }
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