Open solution to Kaggle's: Google Analytics Customer Revenue Prediction 📊
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Updated
Oct 5, 2018
Open solution to Kaggle's: Google Analytics Customer Revenue Prediction 📊
The repository for standard machine learning model developments and related tasks
🏀 Men and women solutions for the 2019 edition of the Kaggle March Madness competition
This project aims to predict which ratio (alpha and beta ratios) performs better on Taiwan Top50 Tracker Fund (TTT). The regression models including LightGBM, Random Forest, and XGBoost are built based on the ranked information coefficient (Ranked IC) of the fund. The results showed that, by solely using alpha ratios, the total stock return perf…
Simple & basic practice of machine learning problems
Source code for my research: PM2.5 Density Prediction based on a Two-Stage Rolling Forecast Model using LightGBM
data science: da busca de dados ao deploy (regressão)
XGBM-and-LGBM
In this project I used different regression algorithms to do regression to predict financial risk. I used Kaggles free GPUs and Machinehack Datasets in this project.
Building classification models to predict passenger satisfaction
Heart disease is a major global health concern that affects millions of people around the world. Early detection and accurate prediction of heart disease can help to prevent the progression of the disease and save lives. In this project, we aim to develop a predictive model for heart disease using various machine learning algorithms.
NYC Taxi Fare Prediction with XGBoost and LightGBM.
We build a model to predict the value of used cars, while also considering speed and quality of the prediction.
In this project, I implemented genetic algorithm (GA) from scratch using python to select the most impactful features in a dataset.
Regression model package predicting the energy above hull of perovskite oxides.
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