Predicting Customer Satisfaction in Supermarket Sales Using Random Forest Regression

This project applied supervised machine learning techniques to analyze transactional data from a supermarket sales dataset and attempted to predict customer satisfaction ratings. It combined exploratory data analysis, feature engineering, and modeling using Random Forest regression to evaluate the feasibility of forecasting customer sentiment based on sales and operational data. Dataset Source: Kaggle: Supermarket Sales Dataset 1,000 records | 17 features (both categorical and numerical) Objectives: Understand key sales and revenue patterns by branch, product line, and payment method Predict customer satisfaction ratings based on measurable transactional variables Assess which features, if any, strongly influence ratings Tools and Technologies: R Programming (randomForest, caret, tidyverse) Modeling Technique: Random Forest Regression Evaluation Metrics: RMSE, MAE, R² Key Analytical Steps: Data Preparation: Selected predictive features: Unit Price, Quantity, Total, COGS, Gross Income Target variable: Customer Rating (1 to 10 scale) 80/20 train-test split for modeling Modeling Approach: Trained a Random Forest Regression model (100 trees) Evaluated using standard metrics: RMSE: 1.867 MAE: 1.579 R²: 0.00095 (indicating extremely low explanatory power) Visualization & Insights: Explored payment method distributions (Cash, E-wallet, Credit Card) Analyzed sales performance by product line using boxplots Tracked total sales trends across date/time Key Takeaways: Sales metrics alone are insufficient to predict customer ratings Ratings are likely influenced by non-quantitative factors such as customer experience, staff interaction, or brand trust The model struggled to generalize, suggesting a mismatch between available predictors and the complexity of human satisfaction Recommendations: Enrich the dataset with qualitative data such as customer reviews or service feedback Leverage Natural Language Processing (NLP) to analyze textual customer sentiment Explore alternative machine learning approaches, such as: Sentiment classification Customer segmentation Hybrid models combining quantitative and qualitative inputs This project demonstrates your ability to: Apply end-to-end predictive analytics workflows Use Random Forest modeling effectively and interpret its limitations Translate modeling performance into strategic business recommendations Communicate technical findings in an accessible, data storytelling format