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

Predicting breast cancer in women in the next five years

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.

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.

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.

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

Titanic: Machine Learning from Disaster

Analysis of Contraceptive Discontinuation using machine learning

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

Predicting house prices in California using machine learning techniques.

Diabetes Prediction

Football Prediction Model

In this project, five different machine learning (ML) models are trained and compared in term of predicting the early-stage diabetes. A data collected in hospital Frankfurt, Germany containing 2000 patients’ information have been used in this study. RF, NB, SVM, KNN, and LR are the five models used for predicting the diabetes.

Practical Approach to AI (example testing)

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 !!!

Prediction of optimum number of clusters using K-Means Clustering on Iris dataset

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.