EDA and comparing different learning algorithms on Car features dataset.

Preprocessing of the dataset includes the step:

-Deleting attributes with large amount of missing data.

-Deleting duplicate data.

-Removing outliers.

-Imputing the missing data.

-Encoding the categorical data.

-Scaling the dataset.

After performing the above preprocessing, the dataset will be ready to be fed into a learning model.

The different models tested here are:

-Linear Regression

-KNN Regression

-Support Vector Regression (SVR)

-Decision Tree Regression

-Random Forest Regression

-XGBoost

XGBoost is found to be having the best R2 score among the models used here.