Our second application, developing a reliable house price prediction model, kept housing as a core part of the data world. Data munging including handling of missing values, outliers and categorical variables is a first step of this process. Using these steps, data is explored intuitively, new features like price per square meter are created and further visualization and statistical analysis is conducted. For pattern selection, we use Ridge and Lasso regression, with GridSearchCV being run to tune and optimize parameters. We assess model performance, model assumptions by examining residuals using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and R squared metrics. Finally built, the model can be used by real estate professionals, investors and policymakers to accurately predict housing prices based on factors such as location, area type, square footage, total number of buckets and offers a highly valuable statistical analysis of these values. A number of future work directions could include further expanding the model by adding new variables, using more sophisticated algorithms, or carrying out similar analyses in other regions of the developing world to achieve higher accuracy.