Data Science Project | Part 2 | Customer Segmentation

Welcome back to our Data Science Project series!

In this second part, we continue our deep dive into Customer Segmentation. This session will build upon the foundations we laid in Part 1, taking you through more advanced techniques and practical applications. Perfect for data science enthusiasts, students, and professionals looking to refine their skills.

What You’ll Learn:

- Advanced Segmentation Techniques: Dive into more sophisticated methods for segmenting customers.
- Model Evaluation: Learn how to evaluate the effectiveness of your segmentation model.
- Practical Applications: Discover how to apply customer segmentation insights to real-world business scenarios.
- Case Study: A detailed case study demonstrating the end-to-end process of customer segmentation.

Key Sections:
Recap of Part 1:

- Quick overview of data collection, preprocessing, and basic K-means clustering.
Advanced Segmentation Techniques:

-Hierarchical clustering
-DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
-Gaussian Mixture Models (GMM)
-Model Evaluation:

-Silhouette score
-Davies-Bouldin index
-Evaluating cluster stability and consistency
-Applying Segmentation Insights:

-Targeted marketing strategies
-Personalized customer experiences
-Improving customer retention and satisfaction


Stay Tuned:
We have more exciting content coming up! In future parts, we'll cover even more advanced data science techniques and their applications. Make sure to subscribe and hit the notification bell so you don’t miss out on the next parts of this series.

Get Involved:

Have questions or suggestions? Drop them in the comments below! We’d love to hear your thoughts and help you with any queries.

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