First of all, we’ll explore and preprocess the data: clean them, fix the errors, convert to appropriate type, etc. Then we will more thoroughly analyze the data: its correlations, relationships, and distribution of variables. After that, we will get rid of the least useful features and try to engineer new ones. Finally, you will train different machine learning models and see how data processing and feature engineering affect the model accuracy and training time.

1. Primary data analysis and Preprocessing   
2. Exploratory Data Analysis  
3. Feature Engineering     
4. Machine Learning model training and evaluation