Linear regression is used when you are predicting a continuous number, more information about this algorithm can be found here. The below code will help to create a linear regression model.
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#### Creating Gym Data for regression in Python #### import pandas as pd import numpy as np ColumnNames=['Hours','Calories', 'Weight'] DataValues=[[ 1.0, 2500, 95], [ 2.0, 2000, 85], [ 2.5, 1900, 83], [ 3.0, 1850, 81], [ 3.5, 1600, 80], [ 4.0, 1500, 78], [ 5.0, 1500, 77], [ 5.5, 1600, 80], [ 6.0, 1700, 75], [ 6.5, 1500, 70]] # Create the Data Frame GymData=pd.DataFrame(data=DataValues,columns=ColumnNames) GymData.head() # Separate Target Variable and Predictor Variables TargetVariable='Weight' Predictors=['Hours','Calories'] X=GymData[Predictors].values y=GymData[TargetVariable].values # Split the data into training and testing set from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) ################################################################ ###### Linear Regression in Python ####### import pandas as pd from sklearn.linear_model import LinearRegression RegModel = LinearRegression() #Printing all the parameters of Linear regression print(RegModel) #Creating the model on Training Data LREG=RegModel.fit(X_train,y_train) prediction=LREG.predict(X_test) from sklearn import metrics #Measuring Goodness of fit in Training data print('R2 Value:',metrics.r2_score(y_train, LREG.predict(X_train))) #Measuring accuracy on Testing Data print('Accuracy',100- (np.mean(np.abs((y_test - prediction) / y_test)) * 100)) #Printing some sample values of prediction TestingDataResults=pd.DataFrame(data=X_test, columns=Predictors) TestingDataResults[TargetVariable]=y_test TestingDataResults[('Predicted'+TargetVariable)]=prediction TestingDataResults.head() |
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