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ML Financial Analyzer

Machine Learning Financial Analysis Dashboard This project is an end-to-end financial analytics platform that automatically evaluates the financial performance and risk profile of publicly listed companies using real-time financial statement data. The system integrates API-based data ingestion, financial feature engineering, explainable machine learning logic, database storage, and a dynamic web dashboard to generate actionable insights such as growth trends, profitability indicators, and strengths and weaknesses for each company. Instead of performing manual spreadsheet analysis, the platform automates the entire workflow by transforming raw financial statements into structured metrics and human-readable insights within seconds. The project demonstrates how modern financial technology and investment research platforms are architected in production environments. Key Features Fetches live financial data through an external API Processes Balance Sheet, Profit & Loss, and Cash Flow statements Calculates financial metrics including Sales Growth, Profit Growth, Return on Equity, and Debt-to-Equity ratio Applies explainable rule-based machine learning to evaluate company performance Automatically generates Pros and Cons insights Stores analysis results in MySQL Displays results through a dynamic PHP dashboard Supports batch analysis for multiple companies Tech Stack Python (data processing and analytics) Pandas and NumPy Rule-based machine learning logic MySQL database PHP, HTML, CSS frontend Apache server (XAMPP) Architecture Financial API → Python Processing & ML → MySQL Database → Web Dashboard Objective The goal of this project was to design and implement a complete data pipeline that connects real-world financial data, analytics, and visualization in a single system. It demonstrates practical skills in backend development, data engineering, financial analysis, database management, and full-stack integration.