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.
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Updated
Nov 28, 2022
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)
Kernels for machine learning problems
[SIGE-MII-UGR-2016-17] Competición en Kaggle: Titanic
Data Cleaning and modeling Approaches
Attrition Prediction using GBM (Classification)
Linear regression at transformed features by gradient boosting machine
An insight to analyzing Titanic survival using decision trees and ensemble methods
This is a set Machine Learning codes written for a kaggle competition titled: "Flavours-of-Physics".
2019.12.12 개인 프로젝트. 직원의 퇴사를 예측하고 퇴사 이유 및 해결방안 제시
A challenge to create a model that uses data from the first 24 hours of intensive care to predict patient survival
Predictive Machine Learning Project
Exploration of ensemble methods on a imbalanced binary classsification problem
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