This repository is dedicated to my journey of learning and mastering Machine Learning concepts through hands-on projects and structured tutorials. Each project has its own folder, containing code, explanations, and insights gained from various learning resources such as GeeksforGeeks and other educational platforms.
- Structured Learning: Following tutorials, courses, and research papers to build a solid foundation.
- Project-Based Exploration: Implementing ML algorithms and real-world use cases to reinforce concepts.
- Documentation & Reflection: Writing detailed notes and explanations to track progress and understanding.
- 📂 Project Folders: Each folder contains a specific ML project, including datasets, notebooks, and reports.
- 📄 Main README: Overview of my learning progress and key takeaways.
Here are the projects I have worked on so far:
- Disease Prediction Using Machine Learning - This project aims to develop a machine-learning model to predict diseases based on symptoms. It uses K-Fold Cross-Validation for model evaluation and implements three classifiers: Support Vector Classifier (SVC), Gaussian Naïve Bayes Classifier, and Random Forest Classifier. The goal is to build an efficient, accurate system for disease prediction using ensemble learning and probabilistic classification techniques
- Develop a strong understanding of fundamental and advanced ML techniques.
- Gain practical experience by working on diverse ML projects.
- Improve coding, model-building, and data analysis skills.