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@article{178934,
        author = {JIl Porasbhai Bhatti and Maru Janak and Paneri Devangi},
        title = {Sales Forecasting Using Machine Learning Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {4933-4938},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178934},
        abstract = {Sales forecasting plays a critical role in the success of businesses across industries, providing essential insights for inventory management, financial planning, and strategic decision-making. Traditional sales forecasting methods often rely on historical data analysis and basic statistical models, which can be limited in accuracy and adaptability. In recent years, machine learning (ML) techniques have emerged as powerful tools for improving the accuracy and efficiency of sales forecasting by capturing complex patterns and relationships within large datasets. This paper explores the application of machine learning algorithms such as decision trees, neural networks, random forests, and support vector machines in predicting future sales. By leveraging features such as historical sales data, market trends, customer behavior, and external factors (e.g., economic conditions and seasonal variations), ML models can provide more robust and dynamic forecasts. The study highlights key challenges, including data preprocessing, model selection, and evaluation metrics, while demonstrating the potential of ML to enhance sales forecasting accuracy compared to traditional methods. The findings suggest that machine learning can significantly improve forecasting precision, enable real-time insights, and facilitate better decision-making processes, ultimately driving operational efficiency and competitiveness in a rapidly changing market environment.},
        keywords = {Sales Forecasting, Machine Learning, XGBoost, Time Series Forecasting, Data Preprocessing, Feature Engineering, Model Evaluation, Predictive Modeling, Historical Sales Data, Forecast Accuracy, Model Deployment},
        month = {May},
        }