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@article{204335,
        author = {Narendra Narayan Bharambe and Tanuja Sunil Patil and Minal jitendra Waykole and Aishwarya Siddharth bavaskar and Dr. Dinesh. D. Patil},
        title = {Customer Behaviour Analysis and Spending Prediction using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {1718-1722},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=204335},
        abstract = {In today’s digital business environment, organizations generate large amounts of customer shopping data through e-commerce platforms, retail stores, mobile applications, and online payment systems. Analysing customer purchasing behaviour manually has become difficult due to the continuously increasing volume of customer data. This paper presents a machine learning-based system for Customer Behaviour Analysis and Spending Prediction using data analytics techniques. The proposed system analyses customer shopping patterns and predicts customer spending amounts based on factors such as age, gender, product category, season, payment method, previous purchases, and purchase frequency. The project integrates data preprocessing, Exploratory Data Analysis (EDA), SQL-based analysis, Power BI dashboard visualization, machine learning model development, and Streamlit web application deployment into one complete workflow. The Random Forest Regressor algorithm is used for prediction because of its high accuracy and reduced overfitting capability. The machine learning model is evaluated using R² Score, Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). The results demonstrate that the developed system generates accurate spending predictions and meaningful business insights. The Streamlit application provides real-time prediction functionality through an interactive user interface. The proposed system helps businesses improve customer understanding, targeted marketing, decision-making, and overall business profitability.},
        keywords = {Customer Behaviour Analysis, Machine Learning, Random Forest Regressor, Spending Prediction, Data Analytics, Power BI, SQL, Streamlit.},
        month = {June},
        }