[Free] Customer Segmentation &Amp; Recommendations Systems With Python
Unlock Data-Driven Marketing: Analyze, Segment & Boost Sales with K-Means & Recommendation Systems – Free Course
What you’ll learn
- Data Cleaning & Transformation
- Feature Engineering for Customer Analytics
- K-Means Clustering for Customer Segmentation
- Cluster Profiling & Evaluation
- Recommendation System Development
Requirements
- Python
Description
Title:
Customer Segmentation & Recommendations Systems with Python
Subtitle:
Unlock Data-Driven Marketing: Analyze, Segment & Boost Sales with K-Means & Recommendation SystemsCourse Description:
In the competitive world of online retail, understanding customer behavior is crucial for optimizing marketing strategies and driving sales. This hands-on course equips you with the skills to transform raw transactional data into actionable insights using Python.
We analyze a real-world dataset from a UK-based retailer (2010-2011), guiding you through data cleaning, feature engineering, and preprocessing techniques essential for effective customer segmentation. You’ll master K-means clustering to categorize customers into distinct groups based on purchasing behavior, enabling personalized marketing strategies.
But we won’t stop there! You’ll also develop a recommendation system that suggests top-selling products to customers within each segment, enhancing cross-selling and maximizing revenue.By the end of this course, you’ll have a solid foundation in data-driven marketing analytics, allowing you to extract valuable customer insights and implement intelligent recommendation systems. Whether you’re a data analyst, business intelligence professional, or aspiring data scientist, this course provides practical skills you can apply immediately to real-world business problems.
Key Topics Covered:
Data Cleaning & Transformation
Feature Engineering for Customer Analytics
K-Means Clustering for Customer Segmentation
Cluster Profiling & Evaluation
Recommendation System Development
Join now and elevate your data analytics skills!
Author(s): Amine Baïna
