This repository contains a machine learning project to forecast energy load in Great Britain. The project utilizes a range of weather and time-series features to develop a robust predictive model, providing valuable insights for energy management and grid optimization.

The primary objective of this project is to build a highly accurate model for forecasting energy consumption. The forecast is based on historical energy load data, combined with a comprehensive set of external features, including weather conditions and time-series components (e.g., day of the week, hour of the day).

Data-Driven Forecasting: Uses machine learning models to identify complex patterns and relationships between energy load and various influencing factors.

Time-Series Analysis: Incorporates seasonal and temporal trends to improve prediction accuracy.

Weather Integration: Leverages weather data (temperature, humidity, etc.) as a key feature for enhanced forecasting.

Performance Evaluation: Includes a robust evaluation framework to assess model performance and ensure reliability.

The project is organized into the following directories:

data/: Contains the raw and preprocessed datasets used for training and testing the model.

eda_outputs/: Stores the results and visualizations from the Exploratory Data Analysis (EDA) phase.

models/: Holds the trained machine learning models, including the final predictive model.

results/: Contains the final forecast outputs and performance metrics.

scripts/: All the Python scripts for data preprocessing, modeling, and evaluation are stored here.

visuals/: Stores key visualizations generated throughout the project, such as feature importance plots and forecast vs. actual charts.

README.md: This file, providing an overview of the project.

requirements.txt: Lists all the necessary Python libraries and their versions for replicating the project environment.

Pandas for data manipulation

Scikit-learn for machine learning models

Matplotlib and Seaborn for data visualization

Jupyter Notebooks (if used for EDA)

Aryan Kushwah

LinkedIn: https://www.linkedin.com/in/aryan-kushwah/

GitHub: https://github.com/Aryankushwah1915