Task-1 Prediction Using Supervised ML.ipynb

{"cells":[{"metadata":{"colab_type":"text","id":"X6A8Hm86UUZ-"},"cell_type":"markdown","source":"# #GRIP JANUARY''22  THE SPARKS FOUNDATION\n\n#   **DATA SCIENCE AND BUSSINESS ANALYTICS INTERN **\n\nAuthor: Yassmen Youssef\n"},{"metadata":{},"cell_type":"markdown","source":"# # Task-1 Prediction using supervised machine learning\n   \n# ** Linear Regression with Python Scikit Learn**"},{"metadata":{"colab_type":"text","id":"GzfdMfk10NE6"},"cell_type":"markdown","source":"## **Linear Regression with Python Scikit Learn**\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.\n\n### **Simple Linear Regression**\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. This is a simple linear regression task as it involves just two variables."},{"metadata":{"colab":{},"colab_type":"code","id":"V9QN2ZxC38pB","trusted":true},"cell_type":"code","source":"# Importing all libraries required in this notebook\nimport pandas as pd\nimport numpy as np  \nimport matplotlib.pyplot as plt  \n%matplotlib inline","execution_count":24,"outputs":[]},{"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":376},"colab_type":"code","executionInfo":{"elapsed":2534,"status":"ok","timestamp":1544113345787,"user":{"displayName":"A M Aditya","photoUrl":"https://lh3.googleusercontent.com/-WI8p7JNWLic/AAAAAAAAAAI/AAAAAAAAAfs/vS8ElgH0p0c/s64/photo.jpg","userId":"15341571102300750919"},"user_tz":-480},"id":"LtU4YMEhqm9m","outputId":"5b4b36af-1545-497e-a6dc-7658bab71dbc","trusted":true},"cell_type":"code","source":"# Reading data from remote link\nurl = \"http://bit.ly/w-data\"\ns_data = pd.read_csv(url)\nprint(\"Data imported successfully\")\n\ns_data.head(12)","execution_count":26,"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  </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"},"execution_count":26,"metadata":{},"output_type":"execute_result"}]},{"metadata":{"colab_type":"text","id":"RHsPneuM4NgB"},"cell_type":"markdown","source":"Let's plot our data points on 2-D graph to eyeball our dataset and see if we can manually find any relationship between the data. We can create the plot with the following script:"},{"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":294},"colab_type":"code","executionInfo":{"elapsed":718,"status":"ok","timestamp":1544113350499,"user":{"displayName":"A M Aditya","photoUrl":"https://lh3.googleusercontent.com/-WI8p7JNWLic/AAAAAAAAAAI/AAAAAAAAAfs/vS8ElgH0p0c/s64/photo.jpg","userId":"15341571102300750919"},"user_tz":-480},"id":"qxYBZkhAqpn9","outputId":"37264af1-786d-4e0c-a668-383264d1ddd1","trusted":true},"cell_type":"code","source":"# Plotting the distribution of scores\ns_data.plot(x='Hours', y='Scores', style='x')  \nplt.title('Hours vs Percentage')  \nplt.xlabel('Hours Studied')  \nplt.ylabel('Percentage Score')  \nplt.show()","execution_count":33,"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"}]},{"metadata":{"colab_type":"text","id":"fiQaULio4Rzr"},"cell_type":"markdown","source":"**From the graph above, we can clearly see that there is a positive linear relation between the number of hours studied and percentage of score.**"},{"metadata":{"colab_type":"text","id":"WWtEr64M4jdz"},"cell_type":"markdown","source":"### **Preparing the data**\n\nThe next step is to divide the data into \"attributes\" (inputs) and \"labels\" (outputs)."},{"metadata":{"colab":{},"colab_type":"code","id":"LiJ5210e4tNX","trusted":true},"cell_type":"code","source":"X = s_data.iloc[:, :-1].values\ny = s_data.iloc[:, 1].values  ","execution_count":45,"outputs":[]},{"metadata":{"colab_type":"text","id":"Riz-ZiZ34fO4"},"cell_type":"markdown","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:"},{"metadata":{"colab":{},"colab_type":"code","id":"udFYso1M4BNw","trusted":true},"cell_type":"code","source":"from sklearn.model_selection import train_test_split  \nX_train, X_test, y_train, y_test = train_test_split(X, y, \n                            test_size=0.2, random_state=0) ","execution_count":46,"outputs":[]},{"metadata":{"colab_type":"text","id":"a6WXptFU5CkC"},"cell_type":"markdown","source":"### **Training the Algorithm**\nWe have split our data into training and testing sets, and now is finally the time to train our algorithm. "},{"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":34},"colab_type":"code","executionInfo":{"elapsed":701,"status":"ok","timestamp":1544113358086,"user":{"displayName":"A M Aditya","photoUrl":"https://lh3.googleusercontent.com/-WI8p7JNWLic/AAAAAAAAAAI/AAAAAAAAAfs/vS8ElgH0p0c/s64/photo.jpg","userId":"15341571102300750919"},"user_tz":-480},"id":"qddCuaS84fpK","outputId":"befbd977-772c-4bd1-bb48-ee5dd6bae73c","trusted":true},"cell_type":"code","source":"from sklearn.linear_model import LinearRegression  \nregressor = LinearRegression()  \nregressor.fit(X_train, y_train) \n\nprint(\"Training complete.\")","execution_count":47,"outputs":[{"name":"stdout","output_type":"stream","text":"Training complete.\n"}]},{"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":265},"colab_type":"code","executionInfo":{"elapsed":985,"status":"ok","timestamp":1544113360867,"user":{"displayName":"A M Aditya","photoUrl":"https://lh3.googleusercontent.com/-WI8p7JNWLic/AAAAAAAAAAI/AAAAAAAAAfs/vS8ElgH0p0c/s64/photo.jpg","userId":"15341571102300750919"},"user_tz":-480},"id":"J61NX2_2-px7","outputId":"d20ec1fd-3e2d-4eae-84a2-a0df57d31009","trusted":true},"cell_type":"code","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);\nplt.show()","execution_count":50,"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"}]},{"metadata":{"colab_type":"text","id":"JCQn-g4m5OK2"},"cell_type":"markdown","source":"### **Making Predictions**\nNow that we have trained our algorithm, it's time to make some predictions."},{"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":102},"colab_type":"code","executionInfo":{"elapsed":698,"status":"ok","timestamp":1544113363729,"user":{"displayName":"A M Aditya","photoUrl":"https://lh3.googleusercontent.com/-WI8p7JNWLic/AAAAAAAAAAI/AAAAAAAAAfs/vS8ElgH0p0c/s64/photo.jpg","userId":"15341571102300750919"},"user_tz":-480},"id":"Tt-Fmzu55EGM","outputId":"46f1acf8-91ac-4984-cfbe-e614aa9ea849","trusted":true},"cell_type":"code","source":"print(X_test) # Testing data - In Hours\ny_pred = regressor.predict(X_test) # Predicting the scores\nprint(\"predicted scores:\",y_pred)","execution_count":52,"outputs":[{"name":"stdout","output_type":"stream","text":"[[1.5]\n [3.2]\n [7.4]\n [2.5]\n [5.9]]\npredicted scores: [16.88414476 33.73226078 75.357018   26.79480124 60.49103328]\n"}]},{"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":204},"colab_type":"code","executionInfo":{"elapsed":753,"status":"ok","timestamp":1544113366918,"user":{"displayName":"A M Aditya","photoUrl":"https://lh3.googleusercontent.com/-WI8p7JNWLic/AAAAAAAAAAI/AAAAAAAAAfs/vS8ElgH0p0c/s64/photo.jpg","userId":"15341571102300750919"},"user_tz":-480},"id":"6bmZUMZh5QLb","outputId":"8ea11a9e-c1b7-4fab-ab62-4dcbd2c8607b","trusted":true},"cell_type":"code","source":"# Comparing Actual vs Predicted\ndf = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})  \ndf ","execution_count":53,"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\n0      20  16.884145\n1      27  33.732261\n2      69  75.357018\n3      30  26.794801\n4      62  60.491033"},"execution_count":53,"metadata":{},"output_type":"execute_result"}]},{"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":51},"colab_type":"code","executionInfo":{"elapsed":862,"status":"ok","timestamp":1544113370494,"user":{"displayName":"A M Aditya","photoUrl":"https://lh3.googleusercontent.com/-WI8p7JNWLic/AAAAAAAAAAI/AAAAAAAAAfs/vS8ElgH0p0c/s64/photo.jpg","userId":"15341571102300750919"},"user_tz":-480},"id":"KAFO8zbx-AH1","outputId":"fcb3830f-3cda-4dcb-f122-84b71f101fae","trusted":true},"cell_type":"code","source":"# You can also test with your own data\nhours = [[1.5],[5],[9.25]]\nown_pred = regressor.predict(hours)\nprint(\"No of Hours = {}\".format(hours))\nprint(\"Predicted Score = {}\".format(own_pred))","execution_count":84,"outputs":[{"name":"stdout","output_type":"stream","text":"No of Hours = [[1.5], [5], [9.25]]\nPredicted Score = [16.88414476 51.57144244 93.69173249]\n"}]},{"metadata":{"colab_type":"text","id":"0AAsPVA_6KmK"},"cell_type":"markdown","source":"### **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. For simplicity here, we have chosen the mean square error. There are many such metrics."},{"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":34},"colab_type":"code","executionInfo":{"elapsed":834,"status":"ok","timestamp":1544113374919,"user":{"displayName":"A M Aditya","photoUrl":"https://lh3.googleusercontent.com/-WI8p7JNWLic/AAAAAAAAAAI/AAAAAAAAAfs/vS8ElgH0p0c/s64/photo.jpg","userId":"15341571102300750919"},"user_tz":-480},"id":"r5UOrRH-5VCQ","outputId":"7b9ddcf1-2848-408f-d81f-7a60652c381e","trusted":true},"cell_type":"code","source":"from sklearn import metrics  \nprint('Mean Absolute Error:', \n      metrics.mean_absolute_error(y_test, y_pred)) ","execution_count":86,"outputs":[{"name":"stdout","output_type":"stream","text":"Mean Absolute Error: 4.183859899002982\n"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"metadata":{"colab":{"collapsed_sections":[],"name":"Linear Regression.ipynb","provenance":[{"file_id":"1wzD9Aa7cc7kRwyXq8DeJ8H56mJInOMZN","timestamp":1544113281508}],"version":"0.3.2"},"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.6.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":1}