Task 1 - Prediction using Supervised ML.ipynb

{"cells":[{"cell_type":"markdown","id":"77dc9c5b","metadata":{"id":"77dc9c5b"},"source":["# **Sparks Foundation**\n","**Graduate Rotational Internship Program**\n"]},{"cell_type":"markdown","id":"313c11f3","metadata":{"id":"313c11f3"},"source":["**Author : Rutuja Naik**\n","\n","\n","**Data Science & Business Analytics Tasks**\n","\n","**Task 1** : Prediction using Supervised ML.\n","\n","\n","**Statement** : Predict the percentage of an student based on the no. of study hours\n","\n"]},{"cell_type":"markdown","id":"RgfbtUes_Xz5","metadata":{"id":"RgfbtUes_Xz5"},"source":["**Importing required python library**"]},{"cell_type":"code","execution_count":39,"id":"4b5b2087","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":831,"status":"ok","timestamp":1631353505703,"user":{"displayName":"Kartik Patil","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgF3fgE-yQ0OM2ZZBT8dnuRWdeI7NysPLY1VLZjtA=s64","userId":"17997917176248330241"},"user_tz":-330},"id":"4b5b2087","outputId":"242de08c-adc3-4687-8a16-248a06eb6423"},"outputs":[{"name":"stdout","output_type":"stream","text":["Imported Successfully\n"]}],"source":["import pandas as pd \n","%matplotlib inline \n","import matplotlib.pyplot as plt \n","import numpy as np \n","print(\"Imported Successfully\")"]},{"cell_type":"markdown","id":"XiKGt-dON9lu","metadata":{"id":"XiKGt-dON9lu"},"source":["**Import the required data using url**"]},{"cell_type":"code","execution_count":40,"id":"e6303ce8","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":819},"executionInfo":{"elapsed":945,"status":"ok","timestamp":1631353522312,"user":{"displayName":"Kartik Patil","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgF3fgE-yQ0OM2ZZBT8dnuRWdeI7NysPLY1VLZjtA=s64","userId":"17997917176248330241"},"user_tz":-330},"id":"e6303ce8","outputId":"9eeda4cd-ab10-4366-b8f7-76ccf91d3562"},"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>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":40,"metadata":{},"output_type":"execute_result"}],"source":["students_data = pd.read_csv(\"http://bit.ly/w-data\")\n","students_data\n"]},{"cell_type":"markdown","id":"AWiY5UcVOKZI","metadata":{"id":"AWiY5UcVOKZI"},"source":["**Plot graph of given data**"]},{"cell_type":"code","execution_count":41,"id":"2329bbb9","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":312},"executionInfo":{"elapsed":970,"status":"ok","timestamp":1631353541379,"user":{"displayName":"Kartik Patil","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgF3fgE-yQ0OM2ZZBT8dnuRWdeI7NysPLY1VLZjtA=s64","userId":"17997917176248330241"},"user_tz":-330},"id":"2329bbb9","outputId":"983e1ea9-4886-4956-b543-134130f72df7","scrolled":false},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Graph Plotted\n"]}],"source":["students_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()\n","print(\"Graph Plotted\")"]},{"cell_type":"markdown","id":"ae0d2796","metadata":{"id":"ae0d2796"},"source":["**From the above graph we clearly see that there is a positive relation between the number of hours studied and the percentage scores**"]},{"cell_type":"markdown","id":"1ebbc2fa","metadata":{"id":"1ebbc2fa"},"source":["## Preparing the data\n","The next steps is to divide the data into \"Attributes\"(inputs) and \"Labels\"(output)"]},{"cell_type":"code","execution_count":42,"id":"b1b305f0","metadata":{"executionInfo":{"elapsed":876,"status":"ok","timestamp":1631353567134,"user":{"displayName":"Kartik Patil","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgF3fgE-yQ0OM2ZZBT8dnuRWdeI7NysPLY1VLZjtA=s64","userId":"17997917176248330241"},"user_tz":-330},"id":"b1b305f0"},"outputs":[],"source":["# Here Hours are the attributes (inputs)\n","X = students_data.iloc[:,:-1].values\n","# Scores are the labels (outputs)\n","y = students_data.iloc[:,1:].values"]},{"cell_type":"markdown","id":"ec4549cb","metadata":{"id":"ec4549cb"},"source":["Now that we have our attributes and labels , the next step is to split this data into training and test sets . We'll do this by using scikit learn's  built-in train_test_split() method:"]},{"cell_type":"code","execution_count":43,"id":"01b452e1","metadata":{"executionInfo":{"elapsed":5,"status":"ok","timestamp":1631353583218,"user":{"displayName":"Kartik Patil","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgF3fgE-yQ0OM2ZZBT8dnuRWdeI7NysPLY1VLZjtA=s64","userId":"17997917176248330241"},"user_tz":-330},"id":"01b452e1"},"outputs":[],"source":["from sklearn.model_selection import train_test_split\n","X_train, X_test , y_train , y_test = train_test_split(X,y,\n","                                        test_size = 0.2 ,random_state=0)"]},{"cell_type":"markdown","id":"a851f7af","metadata":{"id":"a851f7af"},"source":["## Training Algorithum\n","We have split our data into training and testing sets, \n","Now train our algorithum"]},{"cell_type":"code","execution_count":44,"id":"f0e1fccd","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":829,"status":"ok","timestamp":1631353600526,"user":{"displayName":"Kartik Patil","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgF3fgE-yQ0OM2ZZBT8dnuRWdeI7NysPLY1VLZjtA=s64","userId":"17997917176248330241"},"user_tz":-330},"id":"f0e1fccd","outputId":"22071688-bba5-4cad-eecd-a86e9a0cff7f"},"outputs":[{"name":"stdout","output_type":"stream","text":["Training completed\n"]}],"source":["from sklearn.linear_model import LinearRegression\n","regressor = LinearRegression()\n","regressor.fit(X_train,y_train)\n","\n","print(\"Training completed\")"]},{"cell_type":"code","execution_count":45,"id":"4c88921c","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":265},"executionInfo":{"elapsed":869,"status":"ok","timestamp":1631353608245,"user":{"displayName":"Kartik Patil","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgF3fgE-yQ0OM2ZZBT8dnuRWdeI7NysPLY1VLZjtA=s64","userId":"17997917176248330241"},"user_tz":-330},"id":"4c88921c","outputId":"bf442ecc-fe1a-4711-a218-9ad25175533b"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["# Plotting regression line\n","line = regressor.coef_*X+regressor.intercept_\n","\n","# Plotting for the test data\n","plt.scatter(X,y)\n","plt.plot(X,line)\n","plt.show()"]},{"cell_type":"markdown","id":"4b076d72","metadata":{"id":"4b076d72"},"source":["## Making Predictions\n","It's time to makes some prediction "]},{"cell_type":"code","execution_count":46,"id":"09cffe9b","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":926,"status":"ok","timestamp":1631353629090,"user":{"displayName":"Kartik Patil","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgF3fgE-yQ0OM2ZZBT8dnuRWdeI7NysPLY1VLZjtA=s64","userId":"17997917176248330241"},"user_tz":-330},"id":"09cffe9b","outputId":"eb8c7d44-f5f8-4fbe-cbef-c668f725a714"},"outputs":[{"name":"stdout","output_type":"stream","text":["[[1.5]\n"," [3.2]\n"," [7.4]\n"," [2.5]\n"," [5.9]]\n"]}],"source":["#print testing data(study Hours)\n","print(X_test)\n","# Making prediction of score\n","y_pred = regressor.predict(X_test)"]},{"cell_type":"code","execution_count":47,"id":"5e576a0c","metadata":{"executionInfo":{"elapsed":3,"status":"ok","timestamp":1631353632775,"user":{"displayName":"Kartik Patil","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgF3fgE-yQ0OM2ZZBT8dnuRWdeI7NysPLY1VLZjtA=s64","userId":"17997917176248330241"},"user_tz":-330},"id":"5e576a0c","scrolled":true},"outputs":[],"source":["# data must be one-dimensional\n","# flatten() = covert two dimensional array into one dimensional \n","y_test = y_test.flatten()\n","y_pred = y_pred.flatten()"]},{"cell_type":"code","execution_count":48,"id":"d979ef12","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":203},"executionInfo":{"elapsed":8,"status":"ok","timestamp":1631353636439,"user":{"displayName":"Kartik Patil","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgF3fgE-yQ0OM2ZZBT8dnuRWdeI7NysPLY1VLZjtA=s64","userId":"17997917176248330241"},"user_tz":-330},"id":"d979ef12","outputId":"cfca6477-3e03-4b95-974c-c066915d3f3b"},"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":48,"metadata":{},"output_type":"execute_result"}],"source":["df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})  \n","df "]},{"cell_type":"markdown","id":"l2U5HL4aKXzN","metadata":{"id":"l2U5HL4aKXzN"},"source":["**You can also test your own data**"]},{"cell_type":"markdown","id":"dSYQCufnKd9f","metadata":{"id":"dSYQCufnKd9f"},"source":["**Example 1** Let's no. of hours studied = 9.25"]},{"cell_type":"code","execution_count":49,"id":"dbb39c7b","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":806,"status":"ok","timestamp":1631353654388,"user":{"displayName":"Kartik Patil","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgF3fgE-yQ0OM2ZZBT8dnuRWdeI7NysPLY1VLZjtA=s64","userId":"17997917176248330241"},"user_tz":-330},"id":"dbb39c7b","outputId":"7b03b8f5-66df-4d0e-bdbf-437207e54078"},"outputs":[{"name":"stdout","output_type":"stream","text":["No. of Hours = 9.25\n","Predicted score is = 93.69\n"]}],"source":["hour = [9.25]\n","pred = regressor.predict([hour])\n","print(\"No. of Hours = {}\".format(hour[0]))\n","print(\"Predicted score is = {}\".format(round(pred[0][0],2)))"]},{"cell_type":"markdown","id":"pMRKhKmSLR28","metadata":{"id":"pMRKhKmSLR28"},"source":["**Example 2** Let's no. of hours studied = 5.25\n","\n","\n"]},{"cell_type":"code","execution_count":50,"id":"1PnzHlIYLYue","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":965,"status":"ok","timestamp":1631353669993,"user":{"displayName":"Kartik Patil","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgF3fgE-yQ0OM2ZZBT8dnuRWdeI7NysPLY1VLZjtA=s64","userId":"17997917176248330241"},"user_tz":-330},"id":"1PnzHlIYLYue","outputId":"75a80c85-8067-446a-d339-b344a1926c33"},"outputs":[{"name":"stdout","output_type":"stream","text":["No. of Hours = 5.25\n","Predicted score is = 54.05\n"]}],"source":["hour = [5.25]\n","pred = regressor.predict([hour])\n","print(\"No. of Hours = {}\".format(hour[0]))\n","print(\"Predicted score is = {}\".format(round(pred[0][0],2)))"]},{"cell_type":"markdown","id":"8b5cde60","metadata":{"id":"8b5cde60"},"source":["## Evaluating the Model\n"]},{"cell_type":"code","execution_count":51,"id":"8617b93a","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":869,"status":"ok","timestamp":1631353682894,"user":{"displayName":"Kartik Patil","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgF3fgE-yQ0OM2ZZBT8dnuRWdeI7NysPLY1VLZjtA=s64","userId":"17997917176248330241"},"user_tz":-330},"id":"8617b93a","outputId":"707aa2a6-35be-4ce5-d065-651d57fada8d"},"outputs":[{"name":"stdout","output_type":"stream","text":["Mean Absolute Error: 4.183859899002982\n"]}],"source":["from sklearn import metrics\n","print('Mean Absolute Error:',metrics.mean_absolute_error(y_test,y_pred))"]}],"metadata":{"colab":{"collapsed_sections":[],"name":"Prediction using Supervised ML.ipynb","provenance":[]},"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":"3.9.6"}},"nbformat":4,"nbformat_minor":5}