There are 0 repository under ml-model topic.
Key word/wake word detection with espressif esp32s3
🪨 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.
Seatbelt detection using YOLOv5 ML model
Fine-tuning OpenAI's Whisper model on Persian speech datasets for enhanced automatic speech recognition (ASR) performance.
Predict the winning team of IPL matches using XGBoost and Flask, powered by historical data from 2008–2024.
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.
EY Hackathon Project
to predict the survival of the fittest.
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%
SugarSense : The Diabetes Prediction Application
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.
For seamlessly training, evaluating, and deploying machine learning models.
Aplikasi ini adalah sebuah web sederhana untuk prediksi harga mobil menggunakan model Machine Learning yang telah dilatih sebelumnya.
This project focuses on detecting fraudulent credit card transactions using the K-Nearest Neighbors (KNN) algorithm. The dataset, sourced from Kaggle, contains anonymized features extracted through PCA to maintain confidentiality. Only numerical features like V1 to V28, Time, and Amount were used for analysis and model training.
This repository contains a LSTM model, Google Stock Price Predictor
Forecasting the crime in a city from OSN data
With the help of this chatbot, users can communicate doubts regarding vaccine registration.
Developed as part of the Huawei Internship Program in collaboration with Kuwait University. It replicates a simplified version of Huawei SmartCare’s churn analysis.
The Smart Crop Disease Detection System is a Django web app that uses machine learning to identify crop diseases from leaf images. It helps farmers detect diseases quickly and take action to protect their crops. The system features AWS S3 image storage, TensorFlow Lite integration, and a responsive front-end for easy use.
General ML Dashboard is a ready-to-use Streamlit app for visualizing data and making predictions with any scikit-learn-compatible model. It includes modular components for configuration, model loading, and CSV input. Easily customizable, this project is perfect for quick ML demos and prototyping.
Video-based surgical skill assessment using 3D convolutional neural networks
An image classification machine learning model that recognizes hot dogs.
A price predictor model that predicts the price of your old car, based on some required input fields.
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.
A Machine Learning web app that predicts the likelihood of diabetes based on user health data.
the ai product recommender that is a next-generation e-commerce recommendation engine
🚗 ML-powered car resale price prediction using LightGBM (81% R² CV accuracy). Compared Random Forest, XGBoost, and LightGBM models. Full-stack Flask web app deployed on Render with modern UI.
Python ML project predicting student mood based on sleep, study, and activity data with a Streamlit interface.
Developed and compared models to forecast hourly electricity load and prices using over nine years of real-world German market data, spanning linear methods (AR, OLS) and machine learning algorithms (Random Forests, Regression Trees).
E-commerce Customer Segmentation and LTV Prediction Platform