ICP_5 - acvc279/Python_Deeplearning GitHub Wiki

Video Link:https://drive.google.com/file/d/1bvgWibncwxgj2HR34uEdWB_owdRXYd3b/view?usp=drivesdk

1. Delete all the outlierdata for the GarageArea field

Import Libraies Read data from data.csv file find the statical detail of data which is mean, percentage max and min values plot Remove anomalies from outlier plot visuvalisaton output:

2. Create Multiple Regression for the “Restaurant Revenue Prediction” dataset.Evaluate the model using RMSE and R2 score.

Import libraries

read the data from rev.csv file Convert string to interger

Then split test train the data Using linear regression model Evaluating the model using RMSE and R2 Score Plot visuvlization for prediction and test set output:

3. Find top 5most correlated features to the target label(revenue) and then build a model on top of those 5features. Evaluate the model using RMSE and R2 score and then compare the result with the RMSEand R2 you achieved in question 2

Same as question 2 with addition finding top correlaion features Then split test train the data Using linear regression model Evaluating the model using RMSE and R2 Score Here the error was reduced for doing top 5 correlation features

Learned For this icp: Regression