Machine learning is a transformative field in computer science and artificial intelligence that enables systems to learn from data, identify patterns, and make decisions with minimal human intervention. It powers applications in healthcare, finance, marketing, robotics, and more, driving innovation and efficiency. By leveraging algorithms and statistical models, machine learning helps solve complex problems, automate tasks, and uncover insights from large datasets.
This repository contains a comprehensive set of notebooks and resources covering key concepts and practical applications in machine learning:
- Data Preprocessing and EDA: Techniques for cleaning, transforming, and exploring data to prepare it for modeling.
- Data Visualization: Methods for visualizing data distributions and relationships to gain insights.
- Simple and Multiple Linear Regression: Modeling relationships between variables for prediction tasks.
- Logistic Regression: Classification of categorical outcomes, such as disease diagnosis.
- Ensemble Classification: Combining multiple models to improve accuracy and robustness.
- Feature Selection: Identifying the most relevant features using filter, wrapper, and embedded methods.
- LDA and LASSO Feature Selection: Advanced techniques for dimensionality reduction and feature importance.
- PCA and Logistic Regression: Using Principal Component Analysis to reduce dimensionality before classification.
- PCA for Breast Cancer: Applying PCA to medical data for better visualization and analysis.
- K-Means Clustering: Unsupervised learning to group data points based on similarity.
- SVM Classification: Using Support Vector Machines for robust classification tasks.
- California Housing Regression: Predicting housing prices using linear regression models.
- MDS and LDA for Iris Classification: Visualizing and classifying the famous Iris dataset.
Each folder contains a notebook and a README file explaining the concept, methodology, and results. This structure provides a hands-on guide to understanding and applying machine learning techniques to real-world datasets.
For questions or further exploration, refer to the individual folder READMEs or contact the project maintainer.