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@article{194821,
        author = {D.Kanaka Satya and K.Ravi Nandan and G.Chandra Sekhar and D.Akshay and K.Subramanyam Varma},
        title = {Profit Prediction: Machine Learning Regression for Business Profit Estimation},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {8048-8054},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=194821},
        abstract = {In the rapidly evolving business landscape, accurate profit estimation has become increasingly important for strategic planning, financial management, and competitive advantage. Organizations must make informed decisions regarding investments, budgeting, marketing strategies, and operational expenses to ensure longterm sustainability. However, predicting profit is a complex task due to the influence of multiple interconnected factors such as research and development (R&D) expenditure, administrative costs, marketing investments, and regional variations. Traditional statistical forecasting techniques often rely on linear assumptions and limited analytical capabilities, which may not effectively capture nonlinear relationships present in real-world business data.This research proposes a machine learning regression-based approach for business profit estimation. The study involves data preprocessing, feature selection, and implementation of various regression algorithms, including Linear Regression, Multiple Linear Regression, and Random Forest Regression. The models are trained and tested using historical business data, and their performance is evaluated using standard metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R² score. 
The comparative analysis demonstrates that ensemble based regression models, particularly Random Forest Regression, achieve higher prediction accuracy and better generalization performance than traditional linear methods.},
        keywords = {Profit Estimation, Machine Learning Techniques, Regression Models, Business Forecasting, Financial Data Analysis, Predictive Analytics, Business Decision Support.},
        month = {March},
        }