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☀️ Solar Energy Prediction with Machine Learning

Welcome to the Solar Energy Prediction repository! This project utilizes machine learning techniques to predict solar energy output based on historical data. The analysis is performed using Python, with detailed insights provided through a Jupyter Notebook.

📖 Introduction

Solar energy prediction is crucial for optimizing energy production and managing resources efficiently. This project aims to forecast solar energy output by analyzing historical weather and solar data using advanced machine learning models.

✨ Features

  • Data Preprocessing: Clean and preprocess the solar energy dataset for accurate model predictions.
  • Machine Learning Models: Implement various regression models to predict solar energy output.
  • Performance Evaluation: Assess model accuracy using metrics like MAE, MSE, and R².
  • Visualization: Visualize data trends and prediction results for better understanding.

📊 Dataset Information

The analysis is based on the Solar Prediction Dataset provided in this repository. The dataset includes various features such as temperature, humidity, and solar radiation, which are used to predict solar energy output.

Important:

  • Dataset Location: Ensure that the SolarPrediction.csv file is in the same directory as your scripts and notebooks to allow seamless data loading.

🛠️ Installation Instructions

  1. Clone the repository:

    git clone https://github.com/yajasarora/Solar-Energy-Prediction.git
cd Solar-Energy-Prediction

  2. Install the required dependencies: Make sure you have Python 3.x installed. Then, install the necessary packages:

    pip install -r requirements.txt

  3. Run the Jupyter Notebook: Open the Jupyter Notebook main.ipynb to explore the analysis:

    jupyter notebook main.ipynb

🚀 Usage

  • Data Exploration: Start by exploring the data in the notebook to understand the features and relationships.
  • Model Training: Follow the steps in the notebook to preprocess the data and train the machine learning models.
  • Prediction: Use the trained models to predict solar energy output and evaluate the performance.

📈 Results and Visuals

Here are some key insights and visualizations generated by the analysis:

  • Solar Energy Output Prediction: [Visualization Placeholder]
  • Feature Correlation Analysis: [Visualization Placeholder]

🤝 Contributions

Contributions to this project are welcome! If you have ideas for improving the prediction accuracy or adding new features, feel free to fork the repository and submit a pull request.

📬 Contact

For any questions or feedback, feel free to reach out via GitHub Issues or contact me directly.

Harness the power of the sun with data-driven insights! ☀️