xgboost-model
There are 183 repositories under xgboost-model topic.
closedloop-ai/cv19index
COVID-19 Vulnerability Index
grtvishnu/Air-Pollution-Prediction-and-Forecasting
:octocat: Detection and Prediction of Air quality Index :octocat:
keisukeirie/quickdraw_prediction_model
this is my repository for the quick draw prediction model project
alexdatadesign/lfp_soc_ml
LiFePo4(LFP) Battery State of Charge (SOC) estimation from BMS raw data
aditya-167/Realtime-Earthquake-forecasting
Web application for earthquake prediction in a window of few future days. live data collection from https://earthquake.usgs.gov/
AaronFlore/Forecasting-Bitcoin-Prices
Forecasting Bitcoin Prices via ARIMA, XGBoost, Prophet, and LSTM models in Python
keisukeirie/Amazon_review_helpfulness_prediction
this is my repository for Amazon review helpfulness prediction model
dysdsyd/kaggle-question-pairs-quora
My solution for Quora's Question Pair contest on Kaggle.
chollette/Liver-Disease-Classification-Azure-ML-Capstone-Project
This is a Liver Disease Machine Learning Classification Capstone Project in fulfillment of the Udacity Azure ML Nanodegree. In this project, you will learn to deploy a machine learning model from scratch. The files and documentation with experiment instructions needed for replicating the project, is provided for you.
MitchellTesla/Max-Q
Machine-Learning: eXtreme Gradient-Boosting Algorithm Stress Testing
rudrajit1729/Machine-Learning-Codes-And-Templates
Codes and templates for ML algorithms created, modified and optimized in Python and R.
asaficontact/project_floodlight
Crisis incidents caused by rebel groups create a negative influence on the political and economic situation of a country. However, information about rebel group activities has always been limited. Sometimes these groups do not take responsibility for their actions, sometimes they falsely claim responsibility for other rebel group’s actions. This has made identifying the rebel group responsible for a crisis incident a significant challenge. Project Floodlight aims to utilize different machine learning techniques to understand and analyze activity patterns of 17 major rebel groups in Asia (including Taliban, Islamic State, and Al Qaeda). It uses classification algorithms such as Random Forest and XGBoost to predict the rebel group responsible for organizing a crisis event based on 14 different characteristics including number of fatalities, location, event type, and actor influenced. The dataset used comes from the Armed Conflict Location & Event Data Project (ACLED) which is a disaggregated data collection, analysis and crisis mapping project. The dataset contains information on more than 78000 incidents caused by rebel groups that took place in Asia from 2017 to 2019. Roughly 48000 of these observations were randomly selected and used to develop and train the model. The final model had an accuracy score of 84% and an F1 Score of 82% on testing dataset of about 30000 new observations that the algorithm had never seen. The project was programmed using Object Oriented Programming in Python in order to make it scalable. Project Floodlight can be further expended to understand other crisis events in Asia and Africa such as protests, riots, or violence against women.
Javihaus/Advanced-Time-series-analysis
Advance Time Series Analysis using Probabilistic Programming, Auto Regressive Neural Networks and XGBoost Regression.
olaelshiekh/Heart_Disease_detection
World Health Organization has estimated 12 million deaths occur worldwide, every year due to Heart diseases. Half the deaths in the United States and other developed countries are due to cardio vascular diseases.
omerfarukeker/The-Complete-Journey
The Complete Journey Dataset: Churn Prediction
ankitAMD/NYTaxi_XG_Boost_Challenge-kaggle_challenge
I'm attempting the NYC Taxi Duration prediction Kaggle challenge. I'll by using a combination of Pandas, Matplotlib, and XGBoost as python libraries to help me understand and analyze the taxi dataset that Kaggle provides. The goal will be to build a predictive model for taxi duration time. I'll also be using Google Colab as my jupyter notebook. i will also predict without Google colab on normal system.
ashcode028/Music-Genre-Classification
Classifying audio files using ML algorithms.
g-aditi/customer-personality-analysis
Using a Kaggle dataset, customer personality was analysed on the basis of their spending habits, income, education, and family size. K-Means, XGBoost, and SHAP Analysis were performed.
MsTao-68/Debt-Churn-Data-Analysis
使用比赛方提供的脱敏数据，进行客户信贷流失预测。
Pzugatti/House-Price-Prediction
By using feature engineering technique and XGBoost algorithm to predict house price
alanchn31/Data-Science-Portfolio
Personal Data Science Projects
arminZolfaghari/Diabetes-Classification-XGBoost
Data Mining Course Project - Diabetes Classification with XGBoost - Winter 2022
datacrypto-analytics/crypto-analysis-cli
Analise todas as criptomoedas disponíveis na binance spot com algoritmos Machine Learning.
Majd0507/Machine-Learning
Machine learning tutorial with examples
nickdcox/ml-linear-airfare-prediction
Project: What factors impact the accuracy of airfare prediction?
shivamgupta7/Amazon-Fine-Food-Reviews
Amazon Fine Food Reviews is classification Sentiment Analysis problem. Classify the positive and negative reviews given by Amazon users. Given some product-based features and related reviews in text data. Featuring data and apply various Machine Learning techniques to classify reviews.
skyhuang1208/kaggle-porto-safe-driver-prediction
won silver medal, 164th of 5169
stonecoldnicole/flip-or-skip
Machine learning models are used to determine whether a house is a good potential "flip" or not, using standard 70% rule.
vishalv91/Customer-Analytics
The project concerns an international e-commerce company* based in the USA who want to discover key insights from their customer database. They want to use some of the most advanced machine learning techniques to study their customers.
Akash1070/BigMart-Sales-Prediction-
Building BigMart Sales Prediction
anjalysam/ATM_Transaction-data_analysis-
Find the best algorithm to analyze and predict the demand for cash withdrawals
Madhav-Somanath/WindEnergyPredictor
Machine learning model built for IBM Hack 2020 challenge. ⚙️
rochitasundar/Twitter-Sentiment-Analysis
Data consists of tweets scrapped using Twitter API. Objective is sentiment labelling using a lexicon approach, performing text pre-processing (such as language detection, tokenisation, normalisation, vectorisation), building pipelines for text classification models for sentiment analysis, followed by explainability of the final classifier
jimschacko/Churn-Modelling-using-XGBoost
Churn Modelling using XGBoost
mddunlap924/Recommender-System
Multi-Objective Recommender System