Paper Title
OPTIMIZING DATA ANALYSIS FOR FUTURE SALES FORECASTING USING ARTIFICIAL INTELLIGENCE
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Registration ID: IJNRD_306630
Published ID: IJNRD2505126
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Keywords
Sales Prediction, Machine Learning, Random Forest, ARIMA, Prophet, Time-Series Forecasting. Retail Analytics, Demand Forecasting. Predictive Modeling, Data Visualization.
Abstract
We analyze and predict sales patterns using machine learning and time-series forecasting techniques. We utilize a structured dataset containing customer demographics, product categories, and purchase amounts. The dataset undergoes preprocessing, including categorical encoding and exploratory data analysis (EDA) to visualize purchasing trends across different variables. A Random Forest Regressor is implemented for purchase prediction, achieving a Mean Absolute Error (MAE) of 2192.53 and a Root Mean Squared Error (RMSE) of 2972.10, indicating moderate predictive performance. For time-series forecasting, we employ ARIMA and Prophet models to predict future sales trends. The ARIMA model forecasts aggregated purchase values per user, showing stable trends, while the Prophet model generates predictions with uncertainty intervals. The Prophet model yields an MAE of 591532.62 and an RMSE of 846649.20, suggesting room for optimization. Model performance is evaluated through error analysis and visualizations, highlighting the strengths and limitations of each approach. The results demonstrate that Random Forest is effective for customer-level purchase prediction, whereas ARIMA and Prophet provide insights into overall sales trends. The study underscores the importance of machine learning in retail analytics, showcasing how predictive models can help businesses anticipate demand and optimize marketing strategies. Future work can explore hybrid modeling techniques, feature engineering, and deep learning approaches for improved accuracy.
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How To Cite (APA)
Srinivasan E & Gowsalya M (May-2025). OPTIMIZING DATA ANALYSIS FOR FUTURE SALES FORECASTING USING ARTIFICIAL INTELLIGENCE. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 10(5), b284-b293. https://ijnrd.org/papers/IJNRD2505126.pdf
Issue
Volume 10 Issue 5, May-2025
Pages : b284-b293
Other Publication Details
Paper Reg. ID: IJNRD_306630
Published Paper Id: IJNRD2505126
Research Area: Science and Technology
Author Type: Indian Author
Country: Vellore, Tamil Nadu, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2505126.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2505126
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