Enhancing Sales Forecasting Accuracy Using Machine Learning Techniques
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Date
2024-07-26
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Publisher
UMT.Lahore
Abstract
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