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@article{187706,
        author = {Prajwal Waghmare and Mansi Katare and Mrunali Durugkar and Mrunali Jadhav},
        title = {Development of WealthWise Analytics: A Hybrid Machine Learning Approach for Enhanced Financial Forecasting and Investor Decision Support},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {6099-6102},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=187706},
        abstract = {In today's information-intensive financial world, retail investors are at a considerable disadvantage in terms of the complexity of market information analysis and strategic investment decisions. Traditional investment methodologies often rely on intuition or piecemeal data, leading to suboptimal portfolio performance and elevated risk exposure. This paper presents the design and empirical validation of WealthWise Analytics, an intelligent, data-driven platform that simplifies financial analytics related to mutual funds and stocks. In turn, this will allow strategic investment decisions. The hybrid system integrates the ARIMA model with XGBoost for the precise forecasting of financials. An Isolation Forest algorithm has also been implemented for identifying abnormal market volatility or performance deviation in real time, with respect to risk management. The architecture is based on Streamlit, allowing for an interactive, user-centric dashboard that translates complex streams of data, risk metrics, including Sharpe and Sortino Ratios, and model predictions into actionable visual insights and personalized recommendations. Performance evaluation based on metrics such as MSE and RMSE demonstrates that this hybrid model is robust, along with exhibiting superior predictive accuracy compared to conventional statistical methods. The proposed WealthWise Analytics closes the gap between sophisticated financial modeling and intuitive investor intelligence successfully, empowering novice and experienced investors alike to make intelligent, data-driven decisions in investment.},
        keywords = {},
        month = {November},
        }