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.
- 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.
- 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.
- User Uploads Data: The user uploads a CSV file containing their dataset.
- Algorithm Selection: The user selects an algorithm to apply to the dataset.
- Backend Processing: The selected algorithm is applied to the data in the backend.
- Visualization: The results are visualized in the web interface, providing a graphical representation of the model's behavior.
- Interactive Chat: Users can ask questions and get real-time explanations via the chatbot integrated into the platform.
- 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.
- Upload a CSV File: Upload your dataset in CSV format.
- Select Algorithm: Choose the algorithm you wish to apply.
- View Visualization: Visualize the model’s results on the screen.
- Ask Questions: Use the chat feature for real-time feedback and explanations.