##Machine Learning Projects
The Machine Learning Visualizer is an interactive web-based application designed to help users understand various machine learning algorithms through real-time visualizations, animations, and interactive controls. This tool provides an engaging and intuitive way to explore classification, regression, clustering, and dimensionality reduction algorithms, making it easier to grasp complex concepts.
Key Features ✔ Algorithm Exploration: Users can choose from multiple machine learning algorithms and see how they work step by step.
✔ Real-Time Visualizations: Graphical representations of how data points are classified, clustered, or transformed in different ML models.
✔ Interactive Controls: Users can tweak hyperparameters (e.g., learning rate, number of clusters) and observe how changes impact the model's performance.
✔ Data Playground: Users can input custom datasets or use built-in datasets to experiment with different models.
✔ Comparative Analysis: Side-by-side comparison of different algorithms to understand their strengths and weaknesses.
✔ Performance Metrics: Display key evaluation metrics such as accuracy, precision, recall, confusion matrices, and loss curves.
✔ Animation & Step-by-Step Execution: See how the decision boundary evolves, how weights are updated in neural networks, and how clustering algorithms converge over time.
Algorithms Covered 🔹 Classification: Logistic Regression, Decision Trees, Random Forest, Support Vector Machines (SVM), k-Nearest Neighbors (k-NN), Naïve Bayes, Neural Networks 🔹 Regression: Linear Regression, Polynomial Regression, Ridge & Lasso Regression, Support Vector Regression (SVR) 🔹 Clustering: k-Means, Hierarchical Clustering, DBSCAN 🔹 Dimensionality Reduction: PCA, t-SNE, LDA 🔹 Other Techniques: Reinforcement Learning basics, Gradient Descent visualization, Ensemble Methods
Target Audience 📌 Beginners in Machine Learning – Learn ML concepts visually 📌 Students & Researchers – Experiment with different models and visualize results 📌 Educators & Instructors – Use it as a teaching tool to demonstrate ML concepts 📌 Developers & Enthusiasts – Explore algorithms in an interactive way
This project aims to make Machine Learning more accessible, intuitive, and engaging by providing real-time feedback, interactive learning, and in-depth algorithm analysis. 🚀
Understanding source code is crucial for software development and maintenance. Automated code commenting enhances readability and aids in comprehension, especially for multi-lingual developers. This paper explores the use of CodeBERT for generating human-readable code comments and translating them into multiple languages using NLP techniques. The study evaluates model performance, translation accuracy, and potential applications in programming education and industry.
Automated code commenting using CodeBERT and NLP translation enhances programming accessibility and efficiency. This study demonstrates the feasibility of AI-driven multilingual documentation. Future research should focus on improving model adaptability for various coding styles and languages.