Sales Prediction and Fraud Detection Using Machine Learning Techniques

Abstract

The advancement of machine learning techniques has paved the way for the development of sophisticated models capable of addressing various business challenges. This project focuses on two important aspects of business operations: sales prediction and fraud detection. By leveraging machine learning algorithms and historical data, we aim to predict future sales trends accurately and identify potentially fraudulent activities in real-time. For sales prediction we utilise a range of features such as historical sales, marketing campaigns, seasonality, pricing, and other relevant factors. By employing regression algorithms, such as decision trees, linear regression or ensemble methods like gradient boosting or random forests, we create a predictive model capable of forecasting future sales with a high degree of accuracy. The fraud detection component focuses on identifying anomalous or suspicious activities within transactional data. By applying algorithms related to classification, such as decision trees, support vector machines or logistic regression, we develop a fraud detection model that can differentiate between legitimate and fraudulent transactions. This model is trained on labelled data, comprising both genuine and fraudulent transactions, to learn patterns and anomalies that indicate potential fraudulent behaviour. The outcomes of this project can provide valuable insights to businesses in terms of sales forecasting and fraud prevention. Accurate sales predictions enable companies to optimise their operations, manage inventory effectively, and plan marketing campaigns more efficiently. Real-time fraud detection capabilities help businesses minimise financial losses and maintain the trust and confidence of their customers.