Predictive Analysis of Energy Consumption Patterns

Project Overview

This project is developed for a hackathon, focusing on the predictive analysis of household energy consumption. Utilizing machine learning and data analysis, our solution forecasts energy usage based on historical data, economic indicators, weather patterns, and other relevant factors. This tool aims to help households and energy providers optimize energy consumption and plan for future needs.

Features

• Data Collection & Preparation: Automated scripts to collect and preprocess data from various sources.
• Exploratory Data Analysis: Jupyter notebooks detailing the exploratory analysis of the dataset.
• Predictive Modelling: Implementation of machine learning models for energy consumption forecasting.
• API for Model Serving: Backend setup for serving the predictive model through a RESTful API.
• Interactive Dashboard: A user-friendly frontend dashboard for displaying predictions and insights.

Technologies Used

• Programming Languages: Python, R
• Libraries & Frameworks: Pandas, Scikit-learn, TensorFlow, PyTorch, Dash
• API Development: Flask/Django for creating RESTful APIs
• Data Visualization: Matplotlib, Seaborn
• Version Control: Git, GitHub

Getting Started

Prerequisites

• Python 3.8+
• R (for certain data analysis tasks)
• Required Python packages: requirements.txt provides a list.

Installation

1. Clone the repository:

   git clone [repository URL]

2. Install the required Python packages:

   pip install -r requirements.txt

Running the Application

• To start the backend server:

  python app.py

• To view the dashboard, navigate to localhost:[PORT] in your web browser.

Usage

• Describe how to use the application, including any scripts for data collection/preparation, model training, etc.
• Provide examples or screenshots for clarity.

Contributing

• Contributions are welcome! Please read CONTRIBUTING.md for details on our code of conduct and the process for submitting pull requests.

License

• This project is licensed under the [LICENSE] - see the LICENSE.md file for details.

Acknowledgements

• Mention any collaborators, data sources, or third-party tools or frameworks used.

Contact

• Your Name - [Your Email]
• Project Link: [repository URL]