This repository provides a comprehensive guide and implementation for time series forecasting using XGBoost, a powerful machine learning algorithm. The focus is on predicting energy consumption, making it particularly useful for applications in energy management and resource optimization.
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Python Implementation: The entire project is implemented in Python, leveraging popular libraries such as Pandas, Scikit-Learn, and XGBoost.
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Machine Learning with XGBoost: Learn how to harness the capabilities of XGBoost for time series forecasting. Understand the key parameters and techniques to fine-tune the model.
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Data Preprocessing: Explore effective techniques for preparing time series data, handling missing values, and creating lag features to make it suitable for machine learning.
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Evaluation Metrics: Understand and implement appropriate evaluation metrics for time series forecasting, ensuring the model's performance is effectively assessed.
- data: Contains the dataset used for energy consumption forecasting.
- notebooks: Jupyter Notebooks explaining the entire process from data preprocessing to model evaluation.
Feel free to adapt the code and methodologies for your specific time series forecasting tasks, especially in the domain of energy consumption prediction.
This project is inspired by the need for accurate time series forecasting in the domain of energy management. It aims to provide a practical guide for implementing machine learning solutions using XGBoost.