Data Science Projects with Python

Table of Contents

1. Data Exploration and Cleaning
2. Introduction to Scikit-Learn and Model Evaluation
3. Details of Logistic Regression and Feature Exploration
4. The Bias-Variance Trade-off
5. Decision Trees and Random Forests
6. Gradient Boosting, XGBoost, and SHAP (SHapley Additive exPlanations) Values
7. Test Set Analysis, Financial Insights, and Delivery to the Client