COVID-19 Vulnerability Index
-
Updated
Dec 27, 2022 - Python
COVID-19 Vulnerability Index
this is my repository for the quick draw prediction model project
Codes and templates for ML algorithms created, modified and optimized in Python and R.
Machine learning tutorial with examples
Analise todas as criptomoedas disponíveis na binance spot com algoritmos Machine Learning.
Multi-Objective Recommender System
Customer churn, or the loss of customers from a service, significantly impacts a company's revenue. This project aims to identify customers at risk of churning using Natural Language Processing (NLP) and Machine Learning (ML). By leveraging predictive analytics, telecom companies can take proactive steps to retain customers and minimize losses.
Machine Learning to Determine Auto-Insurance Premiums using Telematics
A host of data science + machine learning projects with Python, pandas, scikit-learn and more!
A python project which uses laptop webcam to detect hands by using mediapipe library and then perform some further processing to figgure out hand posture e.g. one, two or Left, Right, The postures are used to control different computer functions e.g desktop switch in windows or mouse control
Automated drug repurposing pipeline for rare diseases – using Jupyter Notebook, Docker-compose, Flask, Monarch API, DGIdb API, RDkit, Node2vec, XGboost and Torch-geometric.
TWITTER DATA ANALYSIS FOR PERSONALITY PREDICTION
models for TalkingData AdTracking Fraud Detection Challenge
Credit Default Approximation for Unsecured Lending Built Machine Learning Classification models (Random Forest, LGBM, XGBoost) in Python to assess the probability of credit defaults.
Backend Service for Aegis AI - Fraud Detection System. Using XGBoost ML model with a 99.93% accuracy to swiftly detect fraudulent transactions.
This repository consists of 6 sections, detailing hands on Machine Learning Models: Regression, Classification, Clustering, AssocaitionRuleLearning, Deep Learning and Natural Language Processing Techniques
END-to-END MLOps CICD pipeline using MLflow for model tracking and experimenting and Amazon S3 for storing model artifacts.
An advanced machine learning application that predicts heart disease risk using XGBoost. Built with Streamlit and trained on over 300,000 US health records, achieving 91.5% prediction accuracy.
Add a description, image, and links to the xgboost-model topic page so that developers can more easily learn about it.
To associate your repository with the xgboost-model topic, visit your repo's landing page and select "manage topics."