- Part 1. Data Analysis and Preprocessing:
- Data Analysis
- Transformation of Categorical Features (manually replaced some features with low values, used one-hotcoding for the remaining ones, and continued the analysis)
- Checking Dependencies between Features (checked and eliminated dependencies between some highly correlated features)
- Part 2. Model Training:
- Applying Linear Models
- Applying Gradient Boosting
- Part 3. Predictions (applying the transformation to the test data and obtaining predictions)
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