SALES PREDICTION

Abstract

In the modern business environment, accurate sales prediction plays a vital role in decision-making, inventory management, and revenue optimization. Organizations increasingly rely on data-driven approaches to forecast future sales and enhance operational efficiency. However, predicting sales is a complex task due to the influence of multiple factors such as product characteristics, pricing, store location, and customer behavior. This project focuses on developing a Sales Prediction system using machine learning techniques to estimate product sales based on historical data. The dataset includes attributes such as item weight, item type, item visibility, maximum retail price (MRP), outlet size, and outlet location. Data preprocessing techniques such as handling missing values, encoding categorical variables, and feature engineering are applied to improve data quality. Machine learning algorithms including Linear Regression, Decision Tree, and Random Forest are used to build predictive models. The performance of these models is evaluated using metrics such as Root Mean Squared Error (RMSE) and R² Score to ensure accuracy and reliability. Among the models, Random Forest demonstrates better performance due to its ability to capture complex relationships in the data. The results show that machine learning techniques can effectively predict sales and provide valuable insights. This project highlights the importance of predictive analytics in improving business strategies, optimizing inventory, and enhancing overall performance.