Financial Transaction Analysis and Forecasting

This application provides financial transaction analysis, forecasting, and recommendations based on your transaction history. Upload your CSV file with financial transactions, and the system will analyze your spending patterns, predict future expenses, and provide personalized recommendations.

Features

• CSV file upload for transaction data
• Expense forecasting
• Transaction analysis and visualization # TODO
• Personalized financial recommendations # TODO
• Cash flow prediction # TODO

Project Structure

├── app/                    # Web application
│   ├── static/            # CSS, JavaScript, and other static files
│   ├── templates/         # HTML templates
│   └── routes.py          # Web routes
├── model/                 # Machine learning model directory
│   ├── capstone-models.ipynb   # Model training script
│   └── forecast.py        # Prediction functions
├── data/                  # Data processing and utilities
│   └── processor.py      # Data processing functions
├── requirements.txt       # Python dependencies
└── config.py             # Configuration settings

Setup and Installation

1. Create a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

2. Install dependencies:

pip install -r requirements.txt

3. Run the application:

python run.py

4. Open your browser and navigate to http://localhost:5000

Development

• The model/ directory is where was developed and trained machine learning model
• The web interface is built with Flask

Dataset

https://www.kaggle.com/datasets/computingvictor/transactions-fraud-datasets?select=transactions_data.csv

Overview

This comprehensive financial dataset combines transaction records, customer information, and card data from a banking institution, spanning across the 2010s decade. The dataset is designed for multiple analytical purposes, including synthetic fraud detection, customer behavior analysis, and expense forecasting.

Dataset Components

1. Transaction Data (transactions_data.csv) Detailed transaction records including amounts, timestamps, and merchant details Covers transactions throughout the 2010s Features transaction types, amounts, and merchant information Perfect for analyzing spending patterns and building fraud detection models

2. Card Information (cards_dat.csv) Credit and debit card details Includes card limits, types, and activation dates Links to customer accounts via card_id Essential for understanding customer financial profiles

3. Merchant Category Codes (mcc_codes.json) Standard classification codes for business types Enables transaction categorization and spending analysis Industry-standard MCC codes with descriptions

4. Fraud Labels (train_fraud_labels.json) Binary classification labels for transactions Indicates fraudulent vs. legitimate transactions Ideal for training supervised fraud detection models

5. User Data (users_data) Demographic information about customers Account-related details Enables customer segmentation and personalized analysis Use Cases and Applications