Kaggle Datasets (over 80 features!)
A lot of feature engineering before the training and predicting:
- manual feature selection
- data types handling (assigning either categorical or numerical values)
- missing values analysis and replacing
- backward elimination of features
- feature scaling
Training and tuning ML regression algorithms:
- Logistic Regression
- Decision Tree
- Random Forest
- XGBoost
- Gradient Boosting