Sales Prediction for Imported Fruits using a Linear Regression Model

Forecasting fruit sales is vital for the retail sector to establish an efficient supply chain, control inventory levels effectively, and satisfy customer needs. This research employs a machine learning algorithm to predict upcoming fruit sales by analyzing past data. We aim to create a dependable prediction system by examining various data elements like past month's sales, seasonal patterns, promotions, holidays, and weather conditions. Linear regression, decision trees, and neural networks are being evaluated for their ability to forecast sales volumes. The model is created and coached with sales data, then evaluated for accuracy using different performance measures. Our findings in the fruit retail industry demonstrate that employing advanced forecasting methods decreases wastage and improves decision-making (resulting in increased profits). This research demonstrates that implementing data-driven sales forecasting within the fresh produce industry may result in more effective and environmentally friendly business operations.