ICP5 - narhirep/Python-Deep-Learning GitHub Wiki

Welcome to In Class Programming 5!

Description: This assignment is regarding various regression techniques like Linear Regression and Multiple Regression.

Objective: First objective of this ICP is to learn and use scatter plot to delete outlier data from particular field. Second objective of this ICP is to create multiple regression for the provided 'Restaurant Revenue Prediction' dataset and to evaluate this model using RMSE and R2 score.

Implementation:

Below are the questions for ICP5.

For question 1 we are deleting all the outlier data for the GarageArea field by plotting scatter plot between GaurageArea field and SalePrice.

With outliers:

Without outliers:

For question 2 We are creating multiple regression model for the “Restaurant Revenue Prediction” dataset. And we are gonna evaluate this model using R2 and RMSE score.

For question 3 we need to find top 5 most correlated features to the 'revenue' and then build a model on top of those 5 features. Then we need to evaluate this model using RMSE and R2 score and then compare the result with the RMSE and R2 we achieved in question 2.

Video Link: ICP5

Conclusion: In this ICP I have learned about various regression techniques like Linear Regression and Multiple Regression. I also learnt about plotting a graph using matplotlib.pyplot, which was new and interesting stuff for me.