| Name | |
|---|---|
| Fowzaan | fowzaan.rasheed@gmail.com |
| Mohit S | smohit28.04@gmail.com |
| Pronoy Kundu | pronoykundu513@gmail.com |
This project demonstrates the application of advanced time series analysis and machine learning techniques in forecasting and anomaly detection. The project uses a dataset of hourly electricity production data from American Electric Power (AEP) to predict future electricity production and detect anomalies in the data.
Dataset Link : Kaggle
- This is a time-series dataset that contains hourly energy consumption in MegaWatts. The data spans from 2004 to 2018 and contains 121,273 data points.
- Phase 1: Problem Definition
- Phase 2: Innovation and Design Thinking
- Phase 3: Data Preprocessing and Visualization
- Phase 4: Model Training and Evaluation
- Phase 5: Documentation
To run this project, clone the repository to your local machine and navigate to the project directory. Install the necessary Python libraries by running pip install -r requirements.txt. Then, you can run the project by executing the main Python script with python main.py.
This project is licensed under the MIT License - see the LICENSE.md file for details.