Linear-Regression-for-Energy-Prediction

We will implement two algorithms namely least square and least mean square for linear regression of the data. The data set used here is Appliances energy prediction which has more than one dependent variable 'Appliances' and 28 independent variables which will be used for linear regression.We preprocessed the data to check for any null values then we have explained and implemented least square and least mean squares code also visualized the predicted and the actual values of the regression,after which we have compared the two algorithms by calculating their RMSE values respectively.Finally, we have found the most and the least significant feature variables and observed the predicted values after removing them and plotted a residual plot for the dataset.