#
gradient-boosting-machine
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In this project, we have analyzed, explored and processed the data, developed and evaluated various classification and regression models to provide strategies for high returns with low risk for investors.
This project compares multiple bagging and boosting methods for anomaly detection for the Gecco challenge.
A self-generalizing, hyperparameter-free Gradient Boosting Machine
Analysed the features for breast cancer data and predicted the diagnosis using Random Forest, Gradient Boosting Machine (GBM)
Updated
Dec 23, 2019
Python
[SIGE-MII-UGR-2016-17] Competición en Kaggle: Titanic
Data Cleaning and modeling Approaches
Updated
Apr 16, 2019
Jupyter Notebook
Attrition Prediction using GBM (Classification)
Updated
May 1, 2022
Jupyter Notebook
Kernels for machine learning problems
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Mar 17, 2019
Jupyter Notebook
Ⓜ️ Machine Learning with Python
Updated
Oct 10, 2023
Python
The objective of this project is to predict whether or not an applicant will be able to repay a loan using historical data of user requesting a loan
Updated
Dec 25, 2018
Jupyter Notebook
House Sales Prediction using GBM (Regression)
Updated
May 4, 2022
Jupyter Notebook
Updated
May 2, 2020
Jupyter Notebook
Linear regression at transformed features by gradient boosting machine
An insight to analyzing Titanic survival using decision trees and ensemble methods
Updated
May 17, 2017
Python
This is a set Machine Learning codes written for a kaggle competition titled: "Flavours-of-Physics".
Updated
Aug 20, 2018
Jupyter Notebook
2019.12.12 개인 프로젝트. 직원의 퇴사를 예측하고 퇴사 이유 및 해결방안 제시
Updated
Apr 29, 2021
Jupyter Notebook
A challenge to create a model that uses data from the first 24 hours of intensive care to predict patient survival
Updated
Oct 22, 2023
Jupyter Notebook
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