💰 FinTrack – Financial Analysis & Reporting System

FinTrack is a Python-based financial analytics system designed to process transaction data, perform analysis, generate reports, and visualize insights. It provides a modular pipeline for handling financial datasets in an organized and extensible manner.

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🚀 Features

• 📥 Data Ingestion

  • Load and preprocess structured transaction datasets

• 📊 Data Analysis

  • Perform statistical and logical analysis on financial data

• 📈 Visualization

  • Generate charts and visual insights from processed data

• 📄 Report Generation

  • Create structured reports (PDF/output files)

• ⚙️ Modular Design

  • Clean separation of components for scalability and maintenance

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🧱 Project Structure

PL-2-Project/
│
├── fintrack/
│   ├── analysis.py          # Core data analysis logic
│   ├── models.py            # Data models
│   ├── ingestion.py         # Data loading & preprocessing
│   ├── visualization.py     # Graph generation
│   ├── report.py            # Report creation
│   └── exceptions.py        # Custom exceptions
│
├── static/                  # Frontend assets
├── templates/               # HTML templates
│
├── sample_data/             # Sample datasets
├── reports/                 # Generated reports (ignored)
├── uploads/                 # Runtime uploads (ignored)
│
├── main.py                  # Entry point
├── server.py                # Backend/server logic
├── generate_pdf_sample.py   # Sample report generator
├── smoke_test.py            # Basic testing
├── requirements.txt         # Dependencies
├── .gitignore
└── README.md

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⚙️ Installation & Setup

1. Clone the repository

git clone https://github.com/BhardwajYash18/PL-2-Project.git
cd PL-2-Project

2. Install dependencies

pip install -r requirements.txt

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▶️ How to Run

Run main application

python main.py

Run backend server

python server.py

Generate a sample report

python generate_pdf_sample.py

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📊 Typical Workflow

1. Load dataset from sample_data/
2. Process and analyze using analysis.py
3. Generate visualizations
4. Export reports to reports/

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👥 Contributors

• Yash Bhardwaj
• Amey Shendre
• Nidhi Dharme

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🧠 Future Enhancements

• Machine learning-based anomaly detection
• Interactive dashboard (web UI)
• Real-time data processing
• Advanced financial analytics

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⚠️ Notes

• reports/, uploads/, and __pycache__/ are excluded via .gitignore
• This project is intended for academic and learning purposes

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📜 License

For educational use only.