The proposed website aims to provide a comprehensive visualization platform for four fundamental machine learning algorithms: K-Nearest Neighbors (KNN), Logistic Regression, Decision Tree, and Support Vector Machine (SVM). These algorithms will be demonstrated in the context of analyzing a student performance dataset, offering an interactive and educational experience for users interested in understanding how these methods work in practice. K-Nearest Neighbors (KNN) is a simple yet powerful classification algorithm that assigns a class label to an instance based on the majority class labels of its k nearest neighbors in the feature space. Logistic regression is a versatile algorithm used for binary classification tasks that models the probability of an instance belonging to a certain class using a logistic function. Decision trees offer an intuitive way to make decisions by creating a tree-like structure of if-else conditions, leading to final class predictions. Support Vector Machines (SVM) are effective for both classification and regression, aiming to find a hyperplane that maximizes the margin between classes in the feature space. This project intends to create a platform where students can explore and visualize various algorithms, ranging from fundamental sorting and searching algorithms to more advanced data structures and dynamic programming techniques. The visualizations will be interactive, allowing students to input custom data and witness how algorithms operate in real-time. The website will enable users to upload or select the student performance dataset and interactively apply these algorithms, adjusting parameters, observing how they influence outcomes, and visualizing results through graphs and diagrams. By offering a hands-on experience with these algorithms on a real-world dataset, the website will facitate a better understanding of their mechanisms and aid in building a strong foundation in machine learning concepts.

• Modern and responsive design
• Clean and maintainable code
• Full documentation included
• Ready to deploy