Gradient boosted classification and regression trees in python

Predicting Used Car Prices in San Francisco Bay Area

In this project, I used supervised learning to build an algorithm to best identify potential donors for CharityML while reducing overhead cost of sending letters.

General boosting framework for any regression estimator

Finding donors using supervised learning

Data Analysis in Spark to Identify Customer Churn for a fictional music service.

This repository is a solution to Analytics Vidhya Practice problem called 'Predicting Loan Status Approval'

Uses ML models to predict the type of crime that is likely to happen at a given time of the day at a specific location on a college campus.

Comparisons of various ML algorithm that detects Diabetic Retinopathy from the data that contains area and perimeter of blood vessel, exudates and micro-aneurysm using Machine Learning.

Ensemble Learning | Flask

ML models for HR classification problem. For more information please visit the link: https://datahack.analyticsvidhya.com/contest/wns-analytics-hackathon-2018-1/

With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, the challenge is predict the final price of each home.

Toolkit for Doing Research with ECMAScript-based Statistics (DRESS Kit)

Using the Sklearn classifiers: Naive Bayes, Random Forest, Adaboost, Gradient Boost, Logistic Regression and Decision Tree good success rates are observed in a very simple manner. In this work sensitivity is also considered. Treating each record individually, differences are found in the results for each record depending on the model used, which…

This project is to predict food calories based on their nutrients using Linear Regression as a part of machine learning.

Taking into account that the accuracy of statistical results depend on the accuracy of the input data, not only on the algorithm, a Hastie file has been created in which all the records have the correct class assigned and tests of hit rates and sensitivity have been carried out

Identify employees that are probable to change their jobs using CART modelx.

A multi-class classification problem where the task is to classify a file to one of 9 types of Malware usually found in a Windows system, using information from the raw data and metadata of the file.

AI model to predict computationally expensive vertex-wise descriptors like the local gyrification index from the mesh structure.

Learning to rank is an algorithmic technique employing machine learning models to solve ranking problems