Advanced International Journal for Research

E-ISSN: 3048-7641   •   Impact Factor: 9.11

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Volume 7 Issue 3
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AI-Powered Stock Price Forecasting and Sentiment Analysis Dashboard for Tech

Author(s) Prerna Choudhary, Digeshwari Kurre, Shrutika Talware
Country India
Abstract Forecasting stock prices is a long-standing challenge that requires understanding technicalpatterns, market sentiment, and predictive modelling. This paper introduces an interactivemulti-stock analysis dashboard that examines five leading technology companies—AAPL,MSFT, AMZN, GOOGL, and META—using historical OHLCV data enriched with engineeredtechnical indicators such as MA5, MA20, RSI, Price Change, and Volume Change. In addition,simulated sentiment scores were incorporated to approximate market psychology. RandomForest regression models were developed for next-day price prediction, and the entire systemwas deployed using Streamlit and Plotly to provide real-time interactive visualizations.The dashboard enables users to explore actual vs predicted prices, sentiment–pricerelationships, RSI signals, and prediction residuals. Across all five stocks, the models achievedan average RMSE of $5.52 and a correlation coefficient greater than 0.95, placing the systemon par with existing computational intelligence approaches. A built-in AI chatbot supports morethan eight types of stock-related queries, making the platform accessible for both beginnersand advanced users. Overall, this work bridges the gap between academic modelling andpractical financial analysis through a scalable and user-friendly architecture suitable forportfolio monitoring and educational purposes.
Keywords Stock price forecasting, Random Forest regression, Technical indicators, RSI, Sentiment analysis, Streamlit dashboard, Interactive visualization, Multi-stock analysis, Computational intelligence, Financial analytics.
Published In Volume 7, Issue 3, May-June 2026
Published On 2026-05-28
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E-ISSN 3048-7641
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