Build an Electricity Demand Prediction XGBoost ML Model in Python (Start-to-End Project)
Project Video available on YouTube - https://youtu.be/iop8TUxmgO0
Download Dataset - https://www.kaggle.com/datasets/rohitgrewal/electricity-demand-data-dsl
In this project, you will learn how to build a Machine Learning model with Python. We will build a XGBoost Model that will help us in forecasting of electricity demand in a city. You will learn how to handle time-series data, create powerful features, train a machine learning model and and evaluate its performance. Here, we have used a synthetic historical data of last 5 years. Based on this data we will predict the future demand using our model. This is a time series dataset with Per Hour information. In this dataset, we have multiple useful columns like - Temperature, Humidity, Demand etc. From the datetime column, we created other useful columns like day_of_year, week_of_year, is_weekend, is_holiday etc. We have used the line chart, box plot for visualization.
Time Series Data Handling
Feature Engineering for Demand Forecasting
Machine Learning (XGBoost) for Prediction
Model Evaluation (RMSE, MAE)
Understanding Energy Consumption Patterns
Python: The core programming language
Pandas: Data manipulation and analysis
NumPy: Numerical operations
Matplotlib & Seaborn: Data visualization
Scikit-learn: Machine learning utilities
XGBoost: Gradient Boosting for robust predictions
Holidays: For national holiday data