Real-Time Fatigue Monitoring with Dual CNN Models for Face & Eye Status. This is a real-time safety system designed to monitor driver alertness using Tensorflow. Built from scratch using Convolutional Neural Networks (CNNs), the system tracks eye and face status independently and triggers alerts when signs of drowsiness exceed a defined threshold.

SDK for Inquire model test database API, by Sevan

fescarefine ⛷️🗻🦮 : Refine & Test ML Models # Features Scaling # Normalization # Datasets

built a regression model to predict molecular solubility using the Delaney Solubility Dataset, diving deep into the world of cheminformatics and data science.

Real Estate Price Prediction via Model Training-Testing

Build predictive models to identify students at risk of dropping out, using their demographic, academic, and social indicators.

📚 A curated list of papers & technical articles on AI Quality & Safety

A python package, command-line tool, and Shiny application to compare short tandem repeat (STR) profiles.

Video labeler for Training and Testing Computer Vision Models Developed in Detectron2 & Pytorch

Using Advance Machine Learning, we will help a bank analyze the data of departing customers and identify and PREDICT the customers who will likely leave their credit card services and their reasons for same – so that bank can improve those areas (reasons for leaving) before departures.

This repository contains a collection of various Machine Learning and Deep Learning projects for free download. These projects are hosted on Engineers Planet.

An image detection program that uses machine learning algorithms to analyze and identify objects(Apples in this case), patterns, or features within images.

Data Science Project (Decision Trees & Random Forests M11)

Simple DB Fixtures for Sails.js v1 (fake data for testing).

Package for testing fields in django models and regular classes.

Goal is to identify an appropriate pipeline to obtain good performance on the dataset, using LSTM neural network approaches.

Q1) Delivery_time -> Predict delivery time using sorting time. Build a simple linear regression model by performing EDA and do necessary transformations and select the best model using R or Python. EDA and Data Visualization, Feature Engineering, Correlation Analysis, Model Building, Model Testing and Model Predictions using simple linear regressi

This repository lists one of my projects and findings as part of my Machine Learning DevOps Engineer Nanodegree.

This project investigated the behavior of a nonlinear harmonic oscillator solver and explained the observed loss of accuracy under certain conditions. It extended a linear harmonic oscillator solver to a nonlinear counterpart using the model 'Method of Manufactured Solutions'.

Successfully established a machine learning model that can accurately predict the sales of a superstore based on various features such as quantity, profit, discount, postal code, etc. The features are mainly associated with order details and customer demographics.