Debugging, monitoring and visualization for Python Machine Learning and Data Science

Entity Framework Core Power Tools - reverse engineering, migrations and model visualization in Visual Studio & CLI

moDel Agnostic Language for Exploration and eXplanation

📍 Interactive Studio for Explanatory Model Analysis

A multi-functional library for full-stack Deep Learning. Simplifies Model Building, API development, and Model Deployment.

Explanatory Model Analysis. Explore, Explain and Examine Predictive Models

Local Interpretable (Model-agnostic) Visual Explanations - model visualization for regression problems and tabular data based on LIME method. Available on CRAN

Visualize correlations between variables

Automated Shorthand Recognition using Optimized DNNs

Triplot: Instance- and data-level explanations for the groups of correlated features.

This repo helps to track model Weights, Biases and Gradients during training with loss tracking and gives detailed insight for Classification-Model Evaluation

Graphical User Interface to debug ROS systems

This repository provides a collection of code and implementations for various chaos theory models. It aims to facilitate the understanding and exploration of chaos theory concepts and inspire further research and experimentation in this field.

Display outputs of each layer in CNN models

This repository contains credit card prediction project that I made using Streamlit and Python programming language.

ML Model Visualization

ReactJS dashboard to visualize the model results of ShipCohortStudy

This project analyzes traffic accident data to identify patterns and predict crash severity using machine learning models. Various classification algorithms, including Random Forest, Logistic Regression, Decision Tree, and K-Nearest Neighbors (KNN), were trained to classify accident types.

Powerful Python tool for visualizing and interacting with pre-trained Masked Language Models (MLMs) like BERT. Features include self-attention visualization, masked token prediction, model fine-tuning, embedding analysis with PCA/t-SNE, and SHAP-based model interpretability.

"A machine learning project to detect fake product reviews using Opinion Mining. It analyzes review text, extracts features, and trains models to classify reviews as genuine or deceptive. The focus is on accuracy and precision to ensure online content authenticity."