International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Forecasting Dairy Sales Estimates: A Comparative Analysis of Logistic Regression and Random Forest Algorithm

Author(s) T. Amalraj Victoire, M.Vasuki, V. Srividhya
Country India
Abstract This study examines the effectiveness of logistic regression and random forest algorithms in forecasting sales estimates for dairy products. Using a comprehensive data set that includes factors such as product features, pricing dynamics, promotional efforts and consumer demographics, the models are trained to accurately predict future sales figures. Logistic regression provides a transparent framework for estimating probabilities, while random forest uses ensemble learning to capture complex relationships between variables. Through careful evaluation and comparison, the research aims to identify the strengths and weaknesses of each algorithm in producing reliable sales forecasts for dairy products. The results show that while logistic regression offers interpretability and simplicity, random forest excels in handling non-linear relationships and achieving higher prediction accuracy. Insights gathered from this analysis can help dairy industry stakeholders make informed decisions, optimize resource allocation and improve sales forecasting strategies.
Keywords Sales Forecasting, Logistic Regression, Random Forest, Dairy Products, Forecasting Algorithms, Data Analysis, Predictive Modeling, Ensemble Learning, Pricing Dynamics
Field Computer Applications
Published In Volume 6, Issue 3, May-June 2024
Published On 2024-05-18
Cite This Forecasting Dairy Sales Estimates: A Comparative Analysis of Logistic Regression and Random Forest Algorithm - T. Amalraj Victoire, M.Vasuki, V. Srividhya - IJFMR Volume 6, Issue 3, May-June 2024. DOI 10.36948/ijfmr.2024.v06i03.20242
DOI https://doi.org/10.36948/ijfmr.2024.v06i03.20242
Short DOI https://doi.org/gtvt2z
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