FeatureEngineering

1. Missing Data (Imputation Technique) - Missing Data within Features of the dataset for both Numerical and Categorical Features
2. Categorical Variables - Machine Learning Model requires numbers instead of String or Object
3. Variable Transformation - Linear Model assumes Features follow Normal or Gaussian Distribution
4. Discretisation - Converting Continuous numbers into Discrete numbers.
5. Outliers - outliers have huge impact on Linear Models mainly looks for continous features.
6. Feature Scaling - All Features need to be scaled to one scale

DataSet: https://www.kaggle.com/c/house-prices-advanced-regression-techniques