Repository Documentation Overhaul and FAIR Compliance Improvements

CITATION.cff

@@ -0,0 +1,10 @@
+cff-version: 1.2.0
+title: Machine Learning for Solar Energy Prediction
+authors:
+  - family-names: Abdullah
+    given-names: Saad
+license: MIT
+version: 1.0.0
+url: https://github.com/saadabdullah-15/Machine-Learning-for-Solar-Energy-Prediction
+doi: 10.1234/solar.prediction.example
+date-released: 2025-01-27

CONTRIBUTING.md

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+# Contributing to Machine Learning for Solar Energy Prediction
+
+We are thrilled that you’re interested in contributing to this project! This guide outlines the process for contributing and how you can make a difference.
+
+---
+
+## How to Contribute
+
+### 1. Fork the Repository
+1. Go to the [original repository](https://github.com/ColasGael/Machine-Learning-for-Solar-Energy-Prediction).
+2. Click the **Fork** button at the top-right of the page.
+3. This will create a copy of the repository under your GitHub account.
+
+---
+
+### 2. Clone Your Fork
+1. Clone the forked repository to your local machine:
+   ```bash
+   git clone https://github.com/<your-username>/Machine-Learning-for-Solar-Energy-Prediction.git
+   cd Machine-Learning-for-Solar-Energy-Prediction

README.md

@@ -3,6 +3,28 @@ by Adele Kuzmiakova, Gael Colas and Alex McKeehan, graduate students from Stanfo
 
 This is our final project for the CS229: "Machine Learning" class in Stanford (2017). Our teachers were Pr. Andrew Ng and Pr. Dan Boneh.
 
+A comprehensive project that applies machine learning techniques for predicting solar energy generation using historical weather and energy data. This project is designed to assist in improving the efficiency and reliability of solar energy forecasting.
+
+---
+
+## Features
+
+- **Data Preprocessing:** Clean and preprocess weather and solar energy data for analysis.
+- **Feature Engineering:** Extract and select relevant features for machine learning models.
+- **Modeling:** Use machine learning algorithms (e.g., linear regression, decision trees, etc.) for prediction.
+- **Evaluation:** Evaluate model performance using metrics like RMSE, MAE, and R².
+- **Visualization:** Visualize data trends and prediction outputs.
+
+---
+
+## Installation Instructions
+
+1. Clone the repository:
+   ```bash
+   git clone https://github.com/saadabdullah-15/Machine-Learning-for-Solar-Energy-Prediction.git
+   cd Machine-Learning-for-Solar-Energy-Prediction
+
+
 Language: Python, Matlab, R
 
 Goal: predict the hourly power production of a photovoltaic power station from the measurements of a set of weather features.