Data Science for Marketing Analytics

Table of Contents

1. Data Preparation and Cleaning
2. Data Exploration and Visualization
3. Unsupervised Learning: Customer Segmentation
4. Choosing the Best Segmentation Approach
5. Predicting Customer Revenue Using Linear Regression
6. Other Regression Techniques and Tools for Evaluation
7. Supervised Learning: Predicting Customer Churn
8. Fine-Tuning Classification Algorithms
9. Modeling Customer Choice