Olatomiwa001/nigeria-market-basket-analysis

Market Basket Analysis using Apriori & FP-Growth to uncover product associations for Nigerian retailers. # Market Basket Analysis for Nigerian Retail ## Project Overview This data science project applies association rule mining to analyze customer purchasing patterns in Nigerian retail markets. ### Business Problem Small and medium retailers lack insights into customer purchasing behavior, leading to suboptimal inventory management and marketing strategies. ### Methodology - Data Source: Simulated Nigerian retail transactions - Techniques: - Apriori Algorithm - Association Rule Mining - Data Visualization ### Key Insights - Identified frequently purchased item combinations - Generated actionable recommendations for product bundling - Created visualizations to understand purchasing patterns ### Technologies Used - Python - Pandas - MLxtend - Matplotlib - Seaborn ### How to Run 1. Create virtual environment 2. Install requirements: `pip install -r requirements.txt` 3. Run script: `python market_basket_analysis.py` ### Future Improvements - Integrate real-world transaction data - Develop more sophisticated visualization - Create machine learning recommendations system ### Business Recommendations 1. Create targeted product bundles 2. Design store layout based on item associations 3. Develop marketing campaigns focusing on frequently co-purchased items"# Market-Basket-Analysis" "# Market-Basket-Analysis" "# Market-Basket-Analysis-" "# Market-Basket-Analysis-" "# Market_Basket-Analysis" "# Market_Basket-Analysis" "# Market_Basket-Analysis" "# nigeria-market-basket-analysis"