Machine Learning Algorithm Visualizer

Project Overview

The Machine Learning Algorithm Visualizer is a web-based tool designed to help users visualize machine learning models and interact with datasets in real-time. This application simplifies the understanding of complex ML algorithms by providing graphical representations and integrating Large Language Models (LLMs) to offer real-time explanations. The intuitive interface allows users to explore machine learning models and datasets, enhancing their learning experience.

Objective

  • Develop a sophisticated web application to visualize user-specific data and promote interactive learning of ML algorithms.
  • Incorporate a chat-based explainer for better understanding and query resolution regarding algorithms.
  • Integrate traditional ML algorithms and LLMs into a unified platform for holistic exploration and comprehension.

Key Components

  • Data Visualization: Automatically generates visual insights from user-uploaded datasets to assist in understanding patterns and data distribution.
  • Model Architecture Insight: Displays the layers and connections within ML models for better understanding of decision-making processes.
  • LLM Integration: Includes tools like Gemini for real-time ML concept explanations during visualization.
  • Interactive Web Interface: Users can explore algorithms and datasets through an intuitive interface.
  • Chat-based Explainer: Integrated chatbot for real-time query resolution and algorithm clarification.

Workflow

flow diagram
  1. User Uploads Data: The user uploads a CSV file containing their dataset.
  2. Algorithm Selection: The user selects an algorithm to apply to the dataset.
  3. Backend Processing: The selected algorithm is applied to the data in the backend.
  4. Visualization: The results are visualized in the web interface, providing a graphical representation of the model's behavior.
  5. Interactive Chat: Users can ask questions and get real-time explanations via the chatbot integrated into the platform.

Supported Algorithms

  • Linear Regression: A regression model to analyze linear relationships between variables.
  • Decision Tree: A non-linear classification and regression algorithm.
  • Model-Based Visualization: Graphical representation of model performance.
  • K-Nearest Neighbors (KNN): An algorithm used for classification and regression tasks.
  • Interactive Linear Regression: A JavaScript-based interactive version of linear regression, allowing parameter adjustments in real time.

How to Use

  1. Upload a CSV File: Upload your dataset in CSV format.
  2. Select Algorithm: Choose the algorithm you wish to apply.
  3. View Visualization: Visualize the model’s results on the screen.
  4. Ask Questions: Use the chat feature for real-time feedback and explanations.

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