Forecasting and Transactions Analysis

This project is designed to analyze transactions and generate forecasts using machine learning models. It includes both a command-line interface and a graphical user interface (GUI).

Project Structure

.gitignore
forecasts.csv               # Contains forecasted data
main.py                     # Main script to run the analysis
gui.py                      # Graphical user interface
transactions.csv            # Contains transaction data
exports/                    # Directory for exported results
    forecast_comparison.png # Visualization of forecast comparisons
models/                     # Directory for saved machine learning models
    arima_model.pkl         # ARIMA model
    metadata.pkl            # Metadata for models
    ml_models.pkl           # General ML models
    prophet_model.pkl       # Prophet model

Requirements

To run this project, you need the following Python libraries:

• pandas
• numpy
• matplotlib
• scikit-learn
• statsmodels
• fbprophet (or prophet for newer versions)
• PySide6 (for the GUI)

Install the dependencies using:

pip install -r requirements.txt

Usage

Command Line Interface

1. Place your transaction data in transactions.csv.
2. Run the main script:

python main.py

3. The forecasts will be saved in forecasts.csv, and visualizations will be exported to the exports/ directory.

Graphical User Interface (GUI)

The project includes a user-friendly GUI that provides:

• Dashboard with financial summary and charts
• Transaction management
• Forecast generation and visualization
• Data analysis tools

To launch the GUI:

python gui.py

Models

The project uses the following models for forecasting:

• ARIMA: Stored in models/arima_model.pkl
• Prophet: Stored in models/prophet_model.pkl
• Other ML Models: Stored in models/ml_models.pkl

Outputs

• Forecasts: Saved in forecasts.csv.
• Visualizations: Exported as images in the exports/ directory.

GUI Features

• Dashboard: View financial summary, quick actions, and charts
• Transactions: Add and manage income and expense transactions
• Forecasts: Generate AI-powered forecasts for different timeframes
• Analysis: Analyze income, expenses, and savings with visualizations

Full Documentation

For complete details about the project, please refer to the Full Documentation.