A curated list of gradient boosting research papers with implementations.

Tuning hyperparams fast with Hyperband

Ruby Scoring API for PMML

Demo on the capability of Yandex CatBoost gradient boosting classifier on a fictitious IBM HR dataset obtained from Kaggle. Data exploration, cleaning, preprocessing and model tuning are performed on the dataset

Projects I completed as a part of Great Learning's PGP - Artificial Intelligence and Machine Learning

In this challenge we have given a directed social graph, and we have to predict missing links to recommend users (Link Prediction in graph)

A way to predict an NBA's players chance of making a shot using machine learning

Analyze NASDAQ100 stock data. Used ARIMA + GARCH model and machine learning techniques Naive Bayes and Decision tree to determine if we go long or short for a given stock on a particular day

This repo contains step-by-step procedure to predict whether a customer's Loan request is Approval or rejected, Based on the compition on Analytics Vidhya

Crystal Scoring API for PMML

Given activity of 2 users on Twitter, predict who is more influential among them

Predict sales prices and practice feature engineering, RFs, and gradient boosting

This repo makes use of different machine learning regression algorithms to predict the survivals of the titanic disaster.

Using various machine learning models to predict whether a company will go bankrupt

To Detect Sepsis Disease using six Classifiers on clinical data

How to predict whether blight fines in Detroit will be paid in time?

This repo contains step by step procedure to predict whether an employee is promoted or not. Based on the Practice competition on AnalyticsVidhya

Customer Churn Prediction using Machine Learning

Example of MLflow usage for a kaggle competition (Santander 2019)

An Artificial Intelligence based Chat Bot using python tools like Numpy, Pandas, Spacy etc. Counsellor Bot will mimic the characteristics and emotion interpretation skills of human and generate response on basis of emotion of engager.

Goal Using the data collected from existing customers, build a model that will help the marketing team identify potential customers who are relatively more likely to subscribe term deposit and thus increase their hit ratio

Finding donors using supervised learning

Models and code for the Kaggle KKBox Music Recommendation Competition

Finding Donors for CharityML using Gradient Boosting Classifier, Ada Boost Classifier and Logistic Regression

Udacity DataScience nanodegree classification problem

Identifies the parts of the Germany population that best describe the core customer base of the Arvato company. Uses a supervised model to predict which individuals are most likely to convert into becoming customers for the company.

My solution for Kaggle competition "categorical feature encoding challenge II" with public and private score of 0.783.

In this project, we will apply supervised learning techniques and an analytical mind on data collected for the U.S. census to help CharityML (a fictitious charity organization) identify people most likely to donate to their cause.

Applying Supervised learning techniques on data to help CharityML identify people most likely to donate to their cause.

Predicting whether a customer will carry out a transaction or not for Santander group