Project Title: Customer Insight and Targeted Recommendations

This project leverages machine learning to enhance customer understanding and product recommendations for retailers. It includes data cleaning, feature engineering, outlier detection, customer segmentation using K-Means clustering, and the development of a recommendation system tailored to each segment.

Problem Statement

Retailers often struggle to understand customer behavior deeply and personalize product recommendations, leading to missed sales opportunities.

Solution

This project aims to analyze customer purchase data to identify distinct customer segments and develop a recommendation system that suggests top-selling products within each segment, boosting customer engagement and sales.

Methodology

Data Cleaning & Preprocessing

• Removal of invoices with cancellations or returns (indicated by 'C' prefixes).
• Handling missing values.

Feature Engineering

• Calculating metrics like total transactions, spending, average purchase values, purchase frequency.
• Extracting buying patterns (seasonal trends, spending increases/decreases).

Outlier Detection

• Employing Isolation Forest algorithm to identify and remove potential outliers that could skew the analysis.

Data Transformation

• Standardization using StandardScaler for better model performance.
• Dimensionality reduction with Principal Component Analysis (PCA) to capture essential patterns.

Customer Segmentation

• Applying K-Means clustering to the transformed data to group customers with similar purchasing behaviors.
• Using the Elbow method and metrics like Silhouette score to determine the optimal number of clusters.

Recommendation System

• Identifying top-selling products within each cluster.
• Suggesting top products to individual customers based on their cluster, excluding items they've already purchased.

Target Audience

• Retailers: Optimize marketing strategies and product placement.
• E-commerce Businesses: Enhance customer experience and increase conversions.
• Data analysts: Interested in customer segmentation and recommendation techniques.

Potential Applications

• Targeted Promotions: Tailor offers and discounts based on customer segments.
• Personalized Product Displays: Adjust website layouts to feature relevant products.
• Inventory Management: Forecast demand based on segment preferences.

Dataset

• Chen,Daqing. (2015). Online Retail. UCI Machine Learning Repository. https://doi.org/10.24432/C5BW33.