🎒 An AI/ML-powered, full-stack job-posting fraud copilot delivering calibrated risk scores, natural-language rationales, interactive chatbot, and real-time insights - powered by ONNX/MLflow exports, classical + transformer ensembles, quantization, Optuna hyperparameter tuning, Google AI LLM, and a sleek Next.js dashboard.

Use auto encoder feature extraction to facilitate classification model prediction accuracy using gradient boosting models

Evaluation and Implementation of various Machine Learning models for creating a "Banking/Financial Transaction Fraud Prevention System"

An NHL expected goals (xG) model built with light gradient boosting.

Model that uses 10 different algorithms to predict the revenue of a movie before it's release

multi-variate deep time series forecasting ensemble models

Comparison of ensemble learning methods on diabetes disease classification with various datasets

Project work related to various hackathons

How to do a simple end-to-end machine learning classification project using the telco churn dataset

A model build on RAVDESS dataset, for speech emotion recognition. 85.59% validation accuracy

Spaceship Titanic project.

This repository contains the project where the goal is to develop a machine learning model that can accurately predict car prices based on various features. We explored multiple models including K-Nearest Neighbor, Decision Tree, Catboost Classifier, and Light Gradient Boosting Classifier.

A binary classification task performed with machine learning in Python. The dataset's target distribution is heavily imbalanced. The model performance was evaluated with F1 scores.

Forest Cover Type Classifier predicts forest cover types using UCI Covertype data. Built with Python, pandas, scikit-learn, XGBoost, and LightGBM. Evaluates KNN, Decision Tree, Gradient Boosting, Random Forest, and XGBoost, with Random Forest achieving ~96% accuracy.