A linear regression analysis is performed on california houshing price(1990). Data were collected from kaggle. The motivation of this analysis is:
- to study the impact of various Normalization technique.
- First order Ablation study
- Can we use weights of particular feature as a parameter for importance of that feature.
- Feature selection techniques
Raw Dataset consist of total 10 columns and 20640 examples.
Target feature = median_house_value
Input features = ['longitude', 'latitude', 'housing_median_age', 'total_rooms', 'total_bedrooms', 'population', 'households', 'median_income', 'median_house_value', 'ocean_proximity']
1.1 : Remove outliers
1.2 : Variance Inflation Factor (VIF) analysis
1.3 : Pairwise Correlation analysis using Heatmap
2.1 : Without Normalization
2.2 : Standardization of input features
2.3 : Max-Min Normalization of input features
2.4 : log transformation of target feature + without Normalization
2.5 : log transformation of target feature + with Standardization
2.6 : First Order Ablation study
2.7 : Weight as parameter for Importance of features?