The first linear regression model used was between Installs and Rating with a score of 0.2233, Our Reviews and Rating linear regression model gave us an MSE of 0.2107, and the combined linear regression model Reviews, Installs vs Rating gave us an MSE of 0.214.We also used a KNeighborsRegressor model because it gave one Kaggle user’s kernel a good mean squared error, a KNeighborsRegressor model with Reviews as a predictor gave us a mean squared error of 0.19948..The model is pictured below.ConclusionFor this project, we took the Google Play Store Data sets and analyzed and processed the data..After the data was transformed into a usable set, we used plots and functions to understand the correlations between features..We then used this knowledge to build the best model we could for finding ratings based on the cleaned data set.We thought finding a decent model would not be too difficult and that we would be working on making a very accurate model..Instead, we learned that creating a model to find the rating was not a simple task..We were able to better understand the challenges that comes from using the models we did with a complex data set.We could have tried to:Create a separate model for each genreCreate new features from android versions like we did with datesTrain a CNN as we had many categorical and numerical data pointsParse and clean data from the Google App Store ourselvesGoogleAppStoreRatingPrediction. More details