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<speak> We use s k learn library for machine learning algorithms. <break strength="x-strong"/> We are going to use standard scaler, mean squared error, and r squared score. <break strength="x-strong"/>And we are also going to import math <break strength="strong"/>and various other modules. <break strength="x-strong"/> Standard scaler is useful for Standardizing features, by removing the mean,and scaling to unit variance<break strength="x-strong"/> Mean squared Error is used to check, how close estimates, or forecasts are to actual values.<break strength="x-strong"/> R2 score or R-squared is a statistical measure, that represents the goodness of fit of a regression model<break strength="x-strong"/> So in this case study, we are also going to implement K Fold, and Grid search CV.<break strength="x-strong"/> K-Folds is useful to maximize the use of the available data, for training and then testing a model.<break strength="x-strong"/> We are going to implement Random Search CV.<break strength="x-strong"/> Random Search CV is useful for Randomized search, on hyper parameters.<break strength="x-strong"/> We are also importing pretty table. <break strength="x-strong"/> Pretty Table is a Python library for generating, simple ASCII tables<break strength="x-strong"/> Let's run the cell. <break strength="x-strong"/> We have data set in this folder.<break strength="x-strong"/> The data set is in excel format.<break strength="x-strong"/> Now we need to know the file path, or location of the data in computer.<break strength="x-strong"/> This is the file path.<break strength="x-strong"/> So here click on this icon and copy the path.<break strength="x-strong"/> Copy this file path.<break strength="x-strong"/> So this is the entire path to access data_train.xlsx file.<break strength="x-strong"/> <break strength="x-strong"/> </speak>