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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 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.

Key Learnings:

• Time Series Data Handling

• Feature Engineering for Demand Forecasting

• Machine Learning (XGBoost) for Prediction

• Model Evaluation (RMSE, MAE)

• Understanding Energy Consumption Patterns

  
  

We will make use of :

• 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

Master Energy Forecasting: A Python Project for Electricity Demand Prediction.

Thanks all students !

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