Welcome to the Feature Selection Techniques, Jupyter Notebook Intro, and Model Training/Evaluation repository! This repository aims to provide a comprehensive guide to understanding and implementing various feature selection techniques, getting started with Jupyter Notebook, and mastering the process of model training and evaluation.
In this section, you will find resources, code examples, and tutorials that delve into feature selection techniques. Discover different approaches, such as filter methods, wrapper methods, and embedded methods, to identify and extract the most relevant features from your dataset. Gain insights into their strengths, limitations, and practical applications.
Explore the power and versatility of Jupyter Notebook, a popular tool for interactive data analysis and scientific computing. This section provides a beginner-friendly introduction to Jupyter Notebook, guiding you through its features, functionality, and best practices. Learn how to create and execute code cells, visualize data, document your work, and collaborate with others using Jupyter Notebook.
Enhance your skills in building and training machine learning models, as well as evaluating their performance. This section covers the fundamental concepts and techniques involved in model training, including data preprocessing, model selection, hyperparameter tuning, and cross-validation. Gain a deep understanding of how to effectively assess and compare the performance of different models.
I hope this repository empowers you to explore feature selection techniques, leverage the capabilities of Jupyter Notebook, and master the art of model training and evaluation. Happy learning and experimenting!