An example project that predicts house prices for a Kaggle competition using a Gradient Boosted Machine.

Automated Essay Scoring on The Hewlett Foundation dataset on Kaggle

It's a github repo star predictor that tries to predict the stars of any github repository having greater than 100 stars.

Our goal in this project was to gain insight into the world of Airbnb market dynamics. There are several different ways to accomplish this goal, but more specifically, we attempted to predict the price for any Airbnb given standard measures such as the location of the listing, and the features that any particular Airbnb offers.

Predict the rating of the each ZOMATO restaurants.

Course work of Introduction to Machine Learning - PUCP - Diplomate of Artificial Intelligence - 2020

Bike Sharing Demand Prediction By Supervised Machine Learning Algorithms Implementation On Seoul Bike Sharing Dataset

This repository contains several machine learning projects done in Jupyter Notebooks

Example machine learning applications for the determination of the residual yield force of corroded steel bars tested under monotonic tensile loading. Data is collected from 26 experimental programs avaialbe in the literature.

This repository consist of various machine learning models along with the dataset. The models are trained with widely used ML algorithms like Gradient Boost , Random Forest etc. Pickle is used to serialize ML algorithms for predictions or availing it for the server use.

Computer Intelligence subject final project at UPC.

Goal is to predict the concrete compressive strength using collected data

Kaggle Competition - Analysis and prediction of PUBG players' finishing placement based on their final stats

This project focuses on leveraging machine learning and artificial intelligence techniques to contribute to environmental conservation efforts and predict the growing stock of forests in Indian states.

Using publicly available data for the national factors that impact supply and demand of homes in US, build a data science model to study the effect of these variables on home prices.

Machine learning demonstration of the Gradient Boosting algorithm and it's effectiveness on a regression dataset of house prices.

The objective of the project is to conduct a comprehensive analysis of a dataset of data science job postings, identifying the most important factors that influence salaries. Build predictive models that can be used to predict salaries for data science professionals, taking into account factors such as experience level, education, skills etc.

Machine Learning model for price prediction using an ensemble of four different regression methods.

Simple GradientBoost