xgboost-python
There are 40 repositories under xgboost-python topic.
msasnur/Healthcare-Analytics
Hospital admission data was analyzed to accurately predict the patient’s Length of Stay at the time of admit so that the hospitals can optimize resources and function better.
www5226448/Master-Machine-Learning
Implement common statistical machine learning algorithms with raw Numpy.
MitchellTesla/Max-Q
Machine-Learning: eXtreme Gradient-Boosting Algorithm Stress Testing
omarmhaimdat/xgboost_student_performance
Introduction to XGBoost with an Implementation in an iOS Application
skinan/Breast-Cancer-Diagnosis-Using-Probabilistic-Ensemble-Based-Machine-Learning-Algorithms
This project is a part of research on Breast Cancer Diagnosis with Machine Learning algorithm using data-driven approaches. The final outcomes of the research were later published at an IEEE Conference and added to IEEE Xplore Digital Library.
eliransr/Health-Online
Angular 8 application for course project using AWS AND ML features
jpg-130/Credit-Card-Fraud-Detection
Credit Card Fraud Detection using Machine Learning
vinbhaskara/XTune-MLUtils
XTune: A custom python wrapper for XGBoost and LightGBM with numerous utility functions to prevent silly gotchas and save time!
alvimahmud-osu/Predicting-Lending-Club-default-using-Machine-Learning
Southern Data Science Conference Attempt 2020
Nishant2018/Academic-Success-Classification-XGBoost-
XGBoost is an open-source machine learning library that provides efficient and scalable implementations of gradient boosting algorithms. It is known for its speed, performance, and accuracy, making it one of the most popular and widely-used machine learning libraries in the data science community.
Rizwan-Hasan/Breast-Cancer-Diagnosis-Using-Probabilistic-Ensemble-Based-Machine-Learning-Algorithms
This project is a part of research on Breast Cancer Diagnosis with a Machine Learning algorithm using data-driven approaches. The final outcomes of the research were later published at an IEEE Conference and added to IEEE Xplore Digital Library.
alvimahmud-osu/Kaggle-Cat-in-the-hat-ii
Kaggle Competition (Encoding categorical variables)
LameesKadhim/Parkinson-detection-python-project
build a model that accurately detect the presence of Parkinson’s disease in an individual.
niketan108/RF-and-GBDT-using-XGBOOST-on-amazon-food-dataset
Built Random Forest and GBDT using XGBOOST model on Amazon fine food review dataset
ShiqinHuo/Practical-XGBoost-in-Python
Self-taught applications of Machine Learning Model XGBoost for COMP4650
stanleyowomero/Walmart-Sales-Forecast
Walmart Sales Forecast Solution
subpic/jupyter-data-science
Jupyter-Lab based setup for data science (Conda, TF2, XGBoost GPU)
ZGrinacoff/customer-churn-predictor
Predictions and Analysis of Customer Churn for Telecoms Company with Plotly Dash Application.
AdiSk325/DataWorkshop_Matrix_Transformation
Three levels of DataWorkshop Matrix Transformation
ChinmayGarud/Purchase-Behaviour
Consumer Spending Analytics
ds-dti/DS02_02_Heart-Failure-Prediction
Predicting heart failure by cardiovascular diseases (CVD).
Jspano95/Retail-TimeSeries-Forecasting-Methods
A comparative breakdown of traditional econometric timeseries models vs. more modern ML methods for predicting a retail firm's sales over a short to medium horizon
JustynaAnd/DataWorkshop-Matrix2
ML projects coded during Matrix 2 by DataWorkshop - car prices prediction
lennartwallentin/churn-hotel-xgboost-alibi
An XGBoost model in Python that classifies if a customer will cancel his/her hotel booking or not. I also use counterfactuals guided by prototypes from the Alibi package to explore the minimum changes needed to flip a prediction from canceled to not canceled and vice versa.
saikasyap/Demographic-Prediction-browser-history
Demographic Prediction using user browser history
sankethkini/autowork
This is React + Django app which helps users to predict the right resell price for the car and It will interpret the output
suhesnabasu/BostonHousing_XgBoost
This repository is about demonstrating XgBoost's Gradient boosting capability with Boston Housing Dataset.
Sumit2514/Customer-Segmentation
Develop a supervised model which can predict customer segment (Low, Medium, high) in Python based on XGBClassifier
tianyiwangnova/2020_project__Customer_Segmentation_and_Campaign_Response_Prediction
☕️Customer segmentation to identify the parts of the population that best describe the core customer base of the company; Predict which individuals are the most likely to respond to the company's mail campaigns.
aslisabanci/xgboost_demo
Demonstrating how to build an XGBoost model and deploy it to Algorithmia, from a Jupyter notebook
jeremywood-ai/Tau_Legion
Sagemaker AI Development
phoenixSP/diabetes-classification
Exploratory Data Analysis and Prediction on Pima Indians Diabetes Dataset