The project includes building seven different machine learning classifiers (including Linear Regression, Decision Tree, Bagging, Random Forest, Gradient Boost, AdaBoost, and XGBoost) using Original, OverSampled, and UnderSampled data of ReneWind case study, tuning hyperparameters of the models, performance comparisons, and pipeline development for productionizing the final model.

Outline

1. Data Overview

2. Exploratory Data Analysis (EDA)

3. Data Preprocessing

4. Model Evaluation Criterion

5. Model Building with Original Data

6. Model Building with OverSampled Data

7. Model Building with Undersampled data

8. Model Selection for Tuning

9. Hyperparameter Tuning

10. Comparing all Models

11. The Final Model

12. Pipelines for Productionizing the Final Model