TASK_1_prediction_using_supervised_learning.ipynb

{
    "cells": [
        {
            "cell_type": "markdown",
            "metadata": {
                "collapsed": true
            },
            "source": "# TASK 1 Prediction using Supervised ML\n## Submitted By : Deepak Kumar Saxena\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "# Linear Regression with Python Scikit Learn\n\nIn this section we will see how the Python Scikit-Learn library for machine learning can be used to implement regression functions. We will start with simple linear regression involving two variables."
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "# Student's Percentage Prediction Model"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "\n## To Explore Supervised Machine Learning\n\nIn this regression task we will predict the percentage of marks that a student is expected to score based upon the number of hours they studied.\nThis is a simple linear regression task as it involves just two variables. Data can be found at http://bit.ly/w-data \n\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "# Importing the Required Libraries"
        },
        {
            "cell_type": "code",
            "execution_count": 2,
            "metadata": {},
            "outputs": [],
            "source": "#importing the libraries\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt \nimport seaborn as sns\n%matplotlib inline"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Reading the data"
        },
        {
            "cell_type": "code",
            "execution_count": 6,
            "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    <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\n0     2.5      21\n1     5.1      47\n2     3.2      27\n3     8.5      75\n4     3.5      30\n5     1.5      20\n6     9.2      88\n7     5.5      60\n8     8.3      81\n9     2.7      25\n10    7.7      85\n11    5.9      62\n12    4.5      41\n13    3.3      42\n14    1.1      17\n15    8.9      95\n16    2.5      30\n17    1.9      24\n18    6.1      67\n19    7.4      69\n20    2.7      30\n21    4.8      54\n22    3.8      35\n23    6.9      76\n24    7.8      86"
                    },
                    "execution_count": 6,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "# Reading data from remote link\nurl = \"http://bit.ly/w-data\"\ndata = pd.read_csv(url)\nprint(\"Data imported successfully\")\n\ndata"
        },
        {
            "cell_type": "code",
            "execution_count": 7,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "Hours     0\nScores    0\ndtype: int64"
                    },
                    "execution_count": 7,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "#checking for null values\ndata.isnull().sum()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "\n\n### There are no null values and hence data cleaning is not required\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "#### Let's plot this data that we have on 2-D graph to eyeball our dataset and see if we can manually find any kind of relationship between the two parameters- Hours and Scores."
        },
        {
            "cell_type": "code",
            "execution_count": 8,
            "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\ndata.plot(x='Hours', y='Scores', style='o')  \nplt.title('Study Hours vs Percentage Scores')  \nplt.xlabel('Hours Studied')  \nplt.ylabel('Percentage Score')  \nplt.show()\n"
        },
        {
            "cell_type": "code",
            "execution_count": 9,
            "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 regressor plot to determine the relationship between feature and target\nsns.regplot(x=data['Hours'],y=data['Scores'],data=data)\nplt.title('Study Hours vs Percentage Scores')\nplt.xlabel('Study Hours')\nplt.ylabel('Percentage')\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### From the graph above, it can be clearly seen that there is a positive linear relation between the number of hours studied and percentage of score."
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "\n## Preparing our data\n\nNext is to define our \"attributes\"(input) variable and \"labels\"(output)\n"
        },
        {
            "cell_type": "code",
            "execution_count": 10,
            "metadata": {},
            "outputs": [],
            "source": "X = data.iloc[:, :-1].values  #Attribute\ny = data.iloc[:, 1].values    #Labels\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Now that we have the attributes and labels defined, the next step is to split this data into training and test sets."
        },
        {
            "cell_type": "code",
            "execution_count": 11,
            "metadata": {},
            "outputs": [],
            "source": "# Using Scikit-Learn's built-in train_test_split() method:\n\nfrom sklearn.model_selection import train_test_split  \nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "We have the training and testing sets ready for training our model."
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "\n## Training the Algorithm\n\nFirst I will be making our linear regression algorithm from scratch and then I will compare it with the built-in function sklearn.linear_model.LinearRegression()\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Making the linear regression from scratch"
        },
        {
            "cell_type": "code",
            "execution_count": 12,
            "metadata": {},
            "outputs": [],
            "source": "y_train_new = y_train.reshape(-1,1)  \nones = np.ones([X_train.shape[0], 1]) # create a array containing only ones \nX_train_new = np.concatenate([ones, X_train],1) # concatenate the ones to X matrix"
        },
        {
            "cell_type": "code",
            "execution_count": 14,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "[[1. 1.]]\n"
                }
            ],
            "source": "# creating the theta matrix\n# notice small alpha value\nalpha = 0.01\niters = 5000\ntheta = np.array([[1.0, 1.0]])\nprint(theta)\n\n"
        },
        {
            "cell_type": "code",
            "execution_count": 15,
            "metadata": {},
            "outputs": [],
            "source": "# Cost Function\ndef computeCost(X, y, theta):\n    inner = np.power(((X @ theta.T) - y), 2)\n    return np.sum(inner) / (2 * len(X))\n"
        },
        {
            "cell_type": "code",
            "execution_count": 16,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "1259.1955"
                    },
                    "execution_count": 16,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "computeCost(X_train_new, y_train_new, theta)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "The value of 1259.1955 is the initial value. The aim will be to minimise this to as small as possible"
        },
        {
            "cell_type": "code",
            "execution_count": 17,
            "metadata": {},
            "outputs": [],
            "source": "# Gradient Descent\ndef gradientDescent(X, y, theta, alpha, iters):\n    m = len(X)\n    for i in range(iters):\n        theta = theta - (alpha/m) * np.sum(((X @ theta.T) - y) * X, axis=0)\n        cost = computeCost(X, y, theta)\n        #if i % 10 == 0:\n            #print(cost)\n    return (theta, cost)"
        },
        {
            "cell_type": "code",
            "execution_count": 18,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Intercept - 2.8268715728809677\nCoefficient-  9.682081515927122\nThe final cost obtained after optimisation -  15.727417763727209\n"
                }
            ],
            "source": "g, cost = gradientDescent(X_train_new, y_train_new, theta, alpha, iters)  \nprint(\"Intercept -\", g[0][0])\nprint(\"Coefficient- \", g[0][1])\nprint(\"The final cost obtained after optimisation - \", cost)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "\n\nFrom \"1259.1955\" to \"15.727417763727209\" that is a huge decrease in cost.\n\n#### Now Let's Plot our result\n"
        },
        {
            "cell_type": "code",
            "execution_count": 19,
            "metadata": {
                "scrolled": true
            },
            "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 scatter points\nplt.scatter(X, y, label='Scatter Plot')\naxes = plt.gca()\n\n# Plotting the Line\nx_vals = np.array(axes.get_xlim()) \ny_vals = g[0][0] + g[0][1]* x_vals #the line equation\n\nplt.plot(x_vals, y_vals, color='red', label='Regression Line')\nplt.legend()\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "So, the above method was building our regression algorithm from scratch and implementing it on the data-set. However, as you can see this is in crude form, a more elegant would be to make a class of it."
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "The hyper-parameters such \"alpha\" also known as the learning rate is optimised by hit and trial method, which should not be the practice."
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "So, instead of writing this long code, python has an-inbuilt library for the same. Let's try that too."
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Using Scikit-Learn library"
        },
        {
            "cell_type": "code",
            "execution_count": 20,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Training complete.\n"
                }
            ],
            "source": "from sklearn.linear_model import LinearRegression  \nregressor = LinearRegression()  \nregressor.fit(X_train, y_train) \n\nprint(\"Training complete.\")"
        },
        {
            "cell_type": "code",
            "execution_count": 21,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Coefficient - [9.68207815]\nIntercept -  2.826892353899737\n"
                }
            ],
            "source": "print (\"Coefficient -\", regressor.coef_)\nprint (\"Intercept - \", regressor.intercept_)"
        },
        {
            "cell_type": "code",
            "execution_count": 22,
            "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\nline = regressor.coef_*X + regressor.intercept_\n\n# Plotting for the test data\nplt.scatter(X, y)\nplt.plot(X, line,color='red', label='Regression Line')\nplt.legend()\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "As we can see that both the graph are identical and even the intercepts and coefficient of the line are same. So my effort of making linear regression algorithm from scratch has been a success!!"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "But as you can see, it is also pretty easy to use the built-in function. Few lines of code and your work is done.Viola!!"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "\n## Making Predictions\n\nNow that we have trained our algorithm, it's time to make some predictions.\n"
        },
        {
            "cell_type": "code",
            "execution_count": 23,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "[[8.3]\n [2.5]\n [2.5]\n [6.9]\n [5.9]]\n"
                }
            ],
            "source": "print(X_test) # Testing data - In Hours\ny_pred = regressor.predict(X_test) # Predicting the scores\n"
        },
        {
            "cell_type": "code",
            "execution_count": 24,
            "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>81</td>\n      <td>83.188141</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>30</td>\n      <td>27.032088</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>21</td>\n      <td>27.032088</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>76</td>\n      <td>69.633232</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>62</td>\n      <td>59.951153</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "   Actual  Predicted\n0      81  83.188141\n1      30  27.032088\n2      21  27.032088\n3      76  69.633232\n4      62  59.951153"
                    },
                    "execution_count": 24,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "# Comparing Actual vs Predicted\ndf = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})  \ndf"
        },
        {
            "cell_type": "code",
            "execution_count": 25,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Training Score: 0.9491209376364417\nTest Score: 0.9678055545167994\n"
                }
            ],
            "source": "#Estimating training and test score\nprint(\"Training Score:\",regressor.score(X_train,y_train))\nprint(\"Test Score:\",regressor.score(X_test,y_test))"
        },
        {
            "cell_type": "code",
            "execution_count": 26,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": "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\n",
                        "text/plain": "<Figure size 504x504 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "#plotting the grid to depict the actual and predicted value\ndf.plot(kind='bar',figsize=(7,7))\nplt.grid(which='major', linewidth='0.5', color='green')\nplt.grid(which='minor', linewidth='0.5', color='black')\nplt.show()"
        },
        {
            "cell_type": "code",
            "execution_count": 27,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "No of Hours = 9.25\nPredicted Score = 92.38611528261494\n"
                }
            ],
            "source": "# Testing with some new data\nhours = 9.25\ntest = np.array([hours])\ntest = test.reshape(-1, 1)\nown_pred = regressor.predict(test)\nprint(\"No of Hours = {}\".format(hours))\nprint(\"Predicted Score = {}\".format(own_pred[0]))"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "\n## Evaluating the model\n\nThe final step is to evaluate the performance of algorithm. This step is particularly important to compare how well different algorithms perform on a particular dataset.\n"
        },
        {
            "cell_type": "code",
            "execution_count": 28,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Mean Absolute Error: 3.9207511902099244\nMean Squared Error: 18.943211722315272\nRoot Mean Squared Error: 4.352380006653288\nR-2: 0.9678055545167994\n"
                }
            ],
            "source": "from sklearn import metrics  \nprint('Mean Absolute Error:',metrics.mean_absolute_error(y_test, y_pred)) \nprint('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))\nprint('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))\nprint('R-2:', metrics.r2_score(y_test, y_pred))"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "R-squared gives the goodness of the fit and as can be seen it is 96.7% which is really good. This means that the algorithm has proven to be good for the given data."
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "It can also be said that the model's accuracy is 96.78%"
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": ""
        }
    ],
    "metadata": {
        "kernelspec": {
            "display_name": "Python 3.6",
            "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": "3.6.9"
        }
    },
    "nbformat": 4,
    "nbformat_minor": 1
}