Simple machine learning model that predicts whether a payment transaction may fail based on customer KYC and transaction metadata.

A machine learning model that predicts the final T20 cricket score based on the current match situation using historical ball-by-ball data. XGBoost Model, Gradient boosting algorithm used here for accurate predictions

This Machine Learning project predicts credit card application approvals using structured applicant data. It includes data preprocessing, model training, and FastAPI deployment for accurate real-time predictions.

CoreML conversion of all-MiniLM-L6-v2 with a full SwiftUI demo, tokenizer implementation, model resources, and conversion script for easy on-device text embeddings.

CIFAR-10 trained with CreateML and a SwiftUI app for real time model testing. Kaggle Score: 0.69660

The study uses the IRSE/FIRE dataset and explores the impact of combining original C code data with Python-derived silver-standard

A concise Streamlit dashboard for analysing transaction data and predicting fraud.

This is a script designed to deploy a ML Model via Client-Side FastAPI !

This project is an end-to-end machine learning pipeline for predicting housing prices in California.

All theory put into actions and my intuitions in own words.

Minimal example showing how to convert ONNX models to CoreML using Docker and a pinned environment.

PyTorch · Scikit-Learn · REST APIs · Docker Products

This model is very useful to detecting cars, buses, and trucks in a video.

Developed a machine learning system to predict flight delays using historical flight and weather data from Kaggle. The pipeline includes data ingestion, storage, and analysis. Trained an XGBoost model to accurately predict delays based on source city, destination city, and date.

A fully automated machine learning pipeline for supervised learning tasks, designed to model training and evaluation

This study applies Decision Tree (98.54% accuracy) and K-Means clustering to financial data analysis, demonstrating their effectiveness for fraud detection and predictive modeling (Wirawan, 2023).

OtosakuStreamingASR-iOS is a real-time speech recognition engine for iOS, built with Swift and Core ML. It uses a fast and lightweight streaming Conformer model optimized for on-device inference. Designed for developers who need efficient audio transcription on mobile.

Computerized Adaptive Test using IRT , implemented with node.js , JavaScript , Python, MLModel

🚀 Mobile-first plant disease detection using CoreML & CreateML An iOS app that leverages computer vision to classify plant diseases in real-time. Trained on 87k+ images, the model achieves good accuracy and works offline, making it ideal for farmers and agronomists in remote areas.

This is the project that I created for DSN 2 at VIT , As its name suggests it will help you to check for any abnormalities with your heart by giving the "Heart Risk Assessment"