RETAIL STORE PRODUCT AFFINITY ANALYSIS AND CROSS-SELLING OPPORTUNITY MAPPING USING PYTHON

Abstract

The Retail Store Product Affinity Analysis and Cross-Selling Opportunity Mapping Using Python project presents the development of an intelligent data analytics system designed to identify product relationships, customer purchasing patterns, and crossselling opportunities in retail environments. In modern retail businesses, understanding how products are purchased together is essential for improving marketing strategies, optimizing product placement, increasing sales revenue, and enhancing customer shopping experiences. Traditional retail analysis methods mainly rely on manual observation, sales summaries, and basic statistical reporting, which are often inefficient and less effective when handling large-scale transactional datasets. This project addresses these limitations by utilizing data analytics and machine learning techniques to discover meaningful product associations and generate actionable business insights automatically. The proposed system utilizes historical retail transaction data including product categories, purchase combinations, transaction frequency, sales volume, customer purchasing behavior, seasonal demand patterns, and revenue trends. Data preprocessing techniques such as handling missing values, normalization, feature encoding, transaction transformation, and feature selection are implemented to improve data quality and analytical accuracy before performing analysis. The system focuses on identifying product affinity relationships, customer buying patterns, and cross-selling opportunities that can support strategic retail decision-making. Various data analytics and machine learning techniques including Association Rule Mining, Market Basket Analysis, Customer Segmentation, Classification, and Pattern Recognition are implemented using algorithms such as Apriori Algorithm, FP-Growth, Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN). These algorithms help identify products that are frequently purchased together and predict cross-selling opportunities effectively.