Predicting breast cancer in women in the next five years

App to count and identify crops in a raster with machine learning

This project uses machine learning classifier algorithms to predict whether the patient is suffering from cancer or not.

Simple machine learning projects

Projects from the Post Graduate Program in ML/AI @ UT Austin

Predictive machine learning model with 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa.

Diabetes Prediction

This Projects creates a model that predicts Google Play Store Apps Rating based on parameters like No. of Installs, reviews, size, category , genres etc. It compares several classification model like Xgboost(booster ensembler), Random Forest(bagger ensembler), Logistic regression, Support Vector Machine(SVC) and Bayesian Classifier.

This repository for machine learning projects learned from ineuron.ai uploaded along with the resources.

A curated list of my machine learning projects. Machine learning (ML) is the study of computer algorithms that improve automatically through experience and by the use of data. It is seen as a part of artificial intelligence.

Heart Disease Prediction is a Machine learning Project which is developed using Python . This project aims to predict whether the patient is suffering from a heart disease in a very efficient and less - time consuming way . HOPE YOU ENJOY IT !!!

This is the machine learning I have done in The University of British Columbia

A disease classifier is a machine learning model that uses various algorithms to predict the presence of a specific disease or condition in patients based on their symptoms, medical history, and other relevant factors.

This Python script counts the total number of images in a directory that can be used as training data for machine learning models. It supports three image file extensions: .jpg, .jpeg, and .png.

Titanic: Machine Learning from Disaster

Analysis of Contraceptive Discontinuation using machine learning

Predicting house prices in California using machine learning techniques.

The goal of this project is to obtain a classifier that can automatically classify environmental sounds according to their category. This can be implemented on both transport vehicles and wearable devices to improve road safety.