Finovera — AI Stock Portfolio Advisor

Individual investors struggle to make informed stock picks without professional tools. Finovera was built to bridge this gap using AI-driven analysis of market data and news sentiment.

How to provide personalized, daily stock recommendations by combining financial metrics with real-time news sentiment, tailored to each investor's risk profile?

Historical stock/ETF data (open/close prices, volume, daily variations) via Yahoo Finance API. Press article sentiment scores from NewsAPI using VADER sentiment analysis.

Built a full data pipeline fusing market data with sentiment scores. Trained a Random Forest classifier (tested against LogisticRegression, XGBoost, LSTM) to predict daily asset performance. The system selects the 5 highest-probability assets daily for personalized buy recommendations.

Functional app with a Streamlit interface and Swift mobile frontend. 44 commits, full ML pipeline from data ingestion to daily recommendations.

End-to-end AI project demonstrating data engineering, ML modeling, NLP sentiment analysis, and product design — from API integration to user-facing app.