MS Bussiness and Economics

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Browsing MS Bussiness and Economics by Author "Muhammad Haseeb Imran"

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    Enhancing Sales Forecasting Accuracy Using Machine Learning Techniques
    (UMT.Lahore, 2024-07-26) Muhammad Haseeb Imran
    Precise sales predictions are critical for successfully managing supply chains, controlling inventory and making informed strategic decisions. Conventional prediction algorithms sometimes fail to capture complex sales trends, resulting in less accurate forecasts. This thesis examines how sophisticated machine learning techniques Linear regression, random forest, and k-means clustering can be used to increase the precision of sales forecasts. The study begins by doing a thorough assessment of current sales forecasting systems, identifying their strengths and flaws. The chosen machine learning algorithms are then developed and used to previous sales records from a retail organization, demonstrating the importance of data pretreatment procedures like as cleaning, normalization, and feature engineering in improving model performance. Hyperparameter optimization and cross-validation are used to improve models and reduce the risk of overfitting. The findings suggest that machine learning technologies outperform traditional sales forecasting methodologies. The random forest technique had the highest accuracy among all the models considered, followed by linear regression and k-means clustering. This thesis adds substantial value by giving a comparative examination of various machine learning models that leverage multiple independent variables for sales forecasting, as well as practical recommendations for firms wanting to use these methods. In the future, researchers will examine integrating sophisticated deep learning models and including other data sources to further boost the accuracy of projections