Overview This project demonstrates the application of machine learning algorithms using a sample dataset. It is part of the FreeCodeCamp Magic Telescope educational project, designed to help you understand key concepts of machine learning, including data preprocessing, model training, evaluation, and prediction.

In this example, you’ll walk through the process of training a model to make predictions. By the end of the tutorial, you'll have a hands-on understanding of how machine learning can be used to analyze data and make predictions in a real-world scenario.

Features Data Preprocessing: Clean and prepare the dataset for model training. Model Training: Train machine learning models to predict outcomes based on the data. Evaluation: Assess the performance of the models using various evaluation metrics. Prediction: Use the trained model to make predictions on new data. Requirements Python 3.x Jupyter Notebook or any compatible IDE Required libraries (e.g., pandas, scikit-learn)