Climage Change Analyzer

This project analyzes climate data, implementing predictive algorithms such as SARIMA and linear regression, and then visualizes the results.

Setup

1. Clone the repository.
2. Create a virtual environment: python -m venv venv
3. Activate the virtual environment:
   • Windows: venv\scripts\activate
   • macOS/Linux: source venv/bin/activate
4. Install dependencies: pip install -r requirements.txt

Usage

Run the main script:

python src/main.py

Or use the command-line interface

1. Show available commands: python src/cli.py --help
2. Train Linear and SARIMA models: python src/cli.py data/WeatherData.csv --train
3. Visualize the data: python src/cli.py data/WeatherData.csv --visualize
4. Analyze and detect anomalies in data: python src/cli.py data/WeatherData.csv --analyze
5. Run all three commands: python src/cli.py data/WeatherData.csv --train --visualize --analyze

Running Tests

First run this to find all the tests: python -m unittest discover tests

Afterwards, tests can be run individually:

1. For data processing: python -m unittest tests.test_data_processor
2. For the algorithms: python -m unittest tests.test_algorithms
3. For the visualizer: python -m unittest tests.test_visualizer

Project Structure

• src/: Source code
  • algorithms.py: Implementation of SARIMA and linear machine learning algorithms
  • cli.py: Command-line interface
  • data_processor.py: Handles data processing and cleanup
  • main.py: Main script
  • visualizer.py: Visualizes generated data into charts
• tests/: Unit tests
  • __init__.py: Init file
  • test_algorithms.py: Unit test for algorithms.py
  • test_data_processor.py: Unit test for data_processor.py
  • test_visualizer.py: Unit test for visualizer.py
• data/: Climate data
  • WeatherData.csv: Weather dataset in CSV format
• requirements.txt: Project dependencies
• project_report_MLA.pdf: Project report in MLA formatting
• project_report_IEEE.pdf: Project report in IEEE formatting

Project Features

Includes two machine learning models (SARIMA and linear-based) for predicting trends. Further explanation and results of the project can be found in the project report.

Contributions

• Alexander Kajda: Implemented the SARIMA and linear model algorithms and their related unit tests for the project.

• Kaitlyn Franklin: Implemented the visualizers and related unit test for the project.

• Maddy Burns: Implemented the data processor, its related unit test, and the command-line interface for the project.

• Ömer Tüzün: Wrote the project report and the README.md for the project. Suggested the SARIMA model be used.