Nitish kumar

Jun 2026 – Aug 2026

Short Project Description – Market Basket Analysis Market Basket Analysis is an AI/ML and data analytics-based project developed to identify relationships and purchasing patterns among products frequently bought together by customers. The system analyzes transaction data to discover associations between different products using techniques such as Association Rule Mining, Apriori, and FP-Growth algorithms. The insights generated can help businesses improve product recommendations, optimize product placement, design promotional offers, and increase sales. Nitish's Role During Internship Collecting and preparing retail transaction datasets Understanding transaction data and product purchasing patterns Cleaning and preprocessing transaction data Performing exploratory data analysis (EDA) Analyzing customer purchasing behavior Transforming transaction data into a suitable format for analysis Implementing association rule mining algorithms Applying the Apriori and FP-Growth algorithms Identifying frequent itemsets and product associations Generating association rules using support, confidence, and lift Comparing the results of different algorithms Visualizing product associations and purchasing trends Generating market basket analysis results Documenting findings and business insights Managing project files using Git/GitHub Suggested Project Modules for the Internship Report Phase 1: Requirement Analysis & Problem Definition Phase 2: Dataset Collection & Data Understanding Phase 3: Data Cleaning & Preprocessing Phase 4: Exploratory Data Analysis Phase 5: Transaction Data Transformation & Preparation Phase 6: Frequent Itemset Mining Using Apriori and FP-Growth Phase 7: Association Rule Generation & Evaluation Phase 8: Product Association Analysis & Result Interpretation Phase 9: Data Visualization & Business Insights Phase 10: Testing, Documentation & Final Deployment