Data-Driven Analysis of Coffee Shop Sales in India Using Machine Learning and IoT-Based Operational Insights

Abstract: This study applies data analytics and machine learning techniques to analyze coffee shop sales and operational efficiency in India. The research focuses on identifying patterns in customer behavior, sales trends, and cost optimization using data collected from point-of-sale (POS) systems, inventory records, and IoT-based sensors. A predictive model is developed to forecast daily sales and recommend inventory levels based on factors such as time, weather, and customer footfall. Data preprocessing, feature extraction, and regression-based algorithms are used to evaluate relationships between sales, pricing, and operational factors. The study demonstrates how data-driven insights can improve decision-making, reduce wastage, and enhance profitability for coffee shops. The results highlight the potential of integrating computer science tools—such as machine learning, data visualization, and IoT monitoring—into the coffee retail industry for smarter management and sustainable growth.

Keywords: coffee shop, data analytics, machine learning, IoT, sales forecasting, operational efficiency