Key word/wake word detection with espressif esp32s3

Machine Learning as a Service for HEP

A Flask web app that predicts the risk of diabetes based on user input using a trained machine learning model. Built with scikit-learn, pandas, and HTML/CSS. Simple UI, real-time predictions, and easy to deploy. Ideal for learning ML model deployment in web applications.

Seatbelt detection using YOLOv5 ML model

Fine-tuning OpenAI's Whisper model on Persian speech datasets for enhanced automatic speech recognition (ASR) performance.

A Machine Learning model created using prebuild model. We need to feed the images to the model and it will predict if the same person is there else it will mark as unknown.

Animal Classification: A CNN-based image recognition model.

Group project for Hackathon

Comment Toxic Analyzer build on machine Learning algorithm (Random Forest) capable to analyze toxicity present in comment or any text with the accuracy of around 83%

EY Hackathon Project

This is an end-to-end ML project, which aims at developing a classification model for the problem of predicting credit card frauds using a given labeled dataset. The classifier used for this project is RandomForestClassifier. Deployed in Heroku.

SugarSense : The Diabetes Prediction Application

🪨 Machine learning project using logistic regression to classify sonar signals as either rocks or mines. Uses scikit-learn to train a binary classifier on sonar dataset with 60 numerical features for accurate underwater object detection.

to predict the survival of the fittest.

Developed as part of the Huawei Internship Program in collaboration with Kuwait University. It replicates a simplified version of Huawei SmartCare’s churn analysis.

Forecasting the crime in a city from OSN data

An image classification machine learning model that recognizes hot dogs.

Video-based surgical skill assessment using 3D convolutional neural networks

Aplikasi ini adalah sebuah web sederhana untuk prediksi harga mobil menggunakan model Machine Learning yang telah dilatih sebelumnya.

For seamlessly training, evaluating, and deploying machine learning models.