International Journal of Innovative Research in Computer and Communication Engineering

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TITLE Profit Pulse Analysis for Retail Success
ABSTRACT This paper presents a comprehensive analytical framework for retail sales and profit forecasting from 2019 to 2025 across multiple departments. The study analyzes historical sales performance, departmental contribution, and profit trends to identify growth patterns and future opportunities. Seven annual datasets were consolidated into a unified dataset for structured analysis. Exploratory Data Analysis (EDA) was conducted to evaluate yearly sales comparison, department-wise contribution, and margin performance.To enhance predictive capability, three forecasting models—ARIMA, XGBoost, and LSTM—were implemented using statistical, machine learning, and deep learning techniques. Feature engineering was performed by extracting temporal attributes such as month and year from transaction dates. Model performance was evaluated using R² Score, Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE)-based accuracy. Experimental results demonstrate that XGBoost achieved the highest predictive performance with minimal forecasting error. The findings confirm that multi-model forecasting significantly improves retail decision-making, profit optimization, and long-term business planning.
AUTHOR SABARINATHAN K, SARAVANAN V, ANITHA M UG Student, Department of Computer Science with Data Analytics, Dr. N.G.P. Arts and Science College, Coimbatore, India Assistant Professor, Department of Computer Science with Data Analytics, Dr. N.G.P. Arts and Science College Coimbatore, India
VOLUME 182
DOI DOI: 10.15680/IJIRCCE. 2026.1403108
PDF pdf/108_Profit Pulse Analysis for Retail Success.pdf
KEYWORDS
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