💹 Financial KPI Analytics Dashboard 📌 Overview
This project demonstrates a full-cycle data analytics and machine learning workflow for evaluating the financial health of over 260 publicly listed companies. It includes data cleaning and transformation with Python, KPI calculation and validation using Excel, ML model building with PyTorch, and the creation of an interactive HTML dashboard for visualization.
🌐 Interactive Dashboard
https://akira23456.github.io/Financial_KPI/
The dashboard allows users to explore historical financial metrics and machine learning predictions across multiple stocks — including ROE (Return on Equity), Debt-to-Equity Ratio, Current Ratio, and other key performance indicators.
🔎 Project Workflow
- Data Cleaning (Python + Pandas)
Imported and combined multiple financial CSV files.
Handled missing values, standardized data types, and removed invalid entries.
Calculated essential financial ratios such as:
current_ratio = current_assets / current_liabilities
debt_to_equity = total_debt / total_equity
roe = net_income / shareholders_equity
debt_to_assets = total_debt / total_assets
cash_to_assets = cash / total_assets
inventory_to_assets = inventory / total_assets
receivables_to_assets = net_receivables / total_assets
Applied IQR-based outlier removal and validated consistency across time periods.
- Data Organization (Excel)
Used Excel for data review and quick KPI comparisons across companies.
Verified ratios and calculations through pivot tables and filters.
Prepared cleaned CSVs for ML model input and dashboard integration.
- Machine Learning (PyTorch)
Built and trained a PyTorch regression model to predict financial KPIs, including ROE and Debt-to-Equity ratios.
Evaluated performance using Mean Absolute Error (MAE) across 200+ predictions.
Generated predicted vs. actual comparison datasets for dashboard visualization.
- Visualization (Interactive HTML Dashboard)
Used Claude to use my data and create a fully interactive web-based dashboard using HTML, JavaScript, Recharts, and Tailwind CSS.
Added dynamic views for:
Average financial ratios across companies
Prediction accuracy comparisons
Asset composition breakdowns
Debt vs. equity and liquidity trends
Integrated CSV-based data loading with PapaParse for real-time updates.
🛠 Tools Used
Python & Pandas → Data cleaning, ratio calculations, and transformation
Excel → KPI organization and manual validation
PyTorch → Machine learning model for ROE and Debt/Equity predictions
HTML + Recharts + Tailwind CSS → Interactive dashboard development
Git & GitHub Pages → Version control and live project hosting
🔑 Key Insights
Financial performance varies significantly — firms with higher Current Ratios generally show better ROE.
Debt-to-Equity serves as a major differentiator between high- and low-performing companies.
ROE predictions achieved low mean absolute error, showing strong model generalization.
Interactive visualizations enable quick insights into trends across 260+ stocks, helping identify both risk and growth opportunities.
📈 Dashboard Highlights
Overview Tab → Company-wide KPIs, trends, and ratio distributions.
Predictions Tab → ML model performance and prediction accuracy plots.
Assets Tab → Asset composition, debt levels, and liquidity indicators.
🚀 Future Enhancements
Real-time financial data integration via APIs
Additional KPIs (e.g., EPS, P/E Ratio, EBITDA Margin)
Sector-based and time-period filtering
Export options for dashboard visuals and data
Enhanced ML model comparison (XGBoost, LSTM, etc.)