GitHub topics: xgboost-model

Repositories

tuni56/churn-prediction-streamlit

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prathamesh693/02_Customer-Churn-Prediction-in-Telecom-Industry

🔍 Predict customer churn in the telecom industry using machine learning models like Decision Tree, XGBoost, and SVM. Includes data preprocessing, model training, evaluation, and a Streamlit app for interactive predictions.

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dpb24/fake-news-detector

Building a machine learning model to classify fake and real news using scikit-learn and XGBoost (Python)

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prathamesh693/03_Predictive-Maintenance-in-Manufacturing

This project is focused on predicting machine failures in a manufacturing setting using classification models trained on sensor data. The dataset includes features such as tool wear, rotational speed, torque, and temperatures, and the goal is to classify the type of failure a machine is likely to experience.

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mkpg/Stock-Price-Prediction-using-XGBoost-and-LSTM-HYBRID-

using the hybrid of LSTM and XGBoost for stock prediction

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NCMBianchi/DrugRepurposing Fork of carmenreep/DrugRepurposing

Automated drug repurposing pipeline for rare diseases – using Jupyter Notebook, Docker-compose, Flask, Monarch API, DGIdb API, RDkit, Node2vec, XGboost and Torch-geometric.

Language: Python - Size: 107 MB - Last synced at: about 2 months ago - Pushed at: about 2 months ago - Stars: 0 - Forks: 1

krithicswaroopan/Lithium-ion_battery_SOH_Prediction

The project analyzes battery cycling data to predict degradation patterns and performance metrics using both deep learning (LSTM) and traditional machine learning (XGBoost) approaches. The implementation enables accurate estimation of battery health, which is crucial for battery management systems in various applications.

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KonovalovaDS/PROJECTS

A sight on my work

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Metavisionpk/DiagnosAI

DiagnosAI is an AI-powered symptom analysis system that helps users identify potential health conditions based on their symptoms and provides intelligent triage recommendations. Combining machine learning with medical knowledge, it offers accurate preliminary assessments to guide users toward appropriate care.

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keisukeirie/quickdraw_prediction_model

this is my repository for the quick draw prediction model project

Language: Python - Size: 435 KB - Last synced at: 3 days ago - Pushed at: over 7 years ago - Stars: 46 - Forks: 20

oonuroo/BitcoinARIMA

Forecasts Bitcoin prices over 6 months using different models integrating historical data, Google Trends, and on-chain metrics (0.28 correlation with active addresses) using R language.

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fractal-solutions/xgboost-js

A pure JavaScript implementation of XGBoost for both Node.js and browser environments.

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akashavcoewala/Churn-Prediction-using-NLP-ML

To identify the customers at the risk of churning, because churn badly affects companies revenue. (Customer churn means the customers who will leave companies service soon) by using NLP & ML. Also to Help telecom Companies for customer retention. Tech used: Python | HTML+CSS | MYSQLBE final year project

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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.

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Mahendra357/Analysis-of-Amazon-Cell-Phone-Reviews-Using-NLP-Technique

The Amazon Cell Phone Review Sentiment Analysis project is a Flask-based web application that classifies Amazon cell phone reviews as positive or negative using a machine learning model powered by NLP techniques. Users can enter a review, analyze its sentiment with a single click, and view the result in real-time.

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clementetienam/Data_Driven_MPC_Controller_Using_CCR

Language: MATLAB - Size: 11.9 MB - Last synced at: 2 months ago - Pushed at: over 4 years ago - Stars: 4 - Forks: 2

Akimuddinshaikh/Master-s-Research-Project

A hybrid approach combining texture-based (GLCM) and deep learning (ResNet50) features with unsupervised clustering and supervised classification for detecting liver diseases. Achieved 99%-100% accuracy using SVM, XGBoost, and Random Forest on pseudo-labeled medical imaging datasets

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SagharShafaati/Air-quality

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MuskanP98/Aviation-Data-Analysis-and-Fatal-Accident-Prediction-Using-Machine-Learning-Algorithms-

This project analyzes aviation accident data using machine learning to predict and prevent fatal accidents. By testing models like Linear Regression, Random Forest, and XGBoost, the study found XGBoost to be the most accurate in predicting high-risk scenarios, aiding efforts to improve aviation safety.

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bilalhameed248/XG-Boost-TS-Prediction

Predict/Forecast monthly and daily charges, as well as payments associated with claims generated during the billing process

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bintangyosua/alzheimer

This repository contains a dataset and analysis focused on the risk factors associated with Alzheimer's disease. The dataset includes comprehensive patient information, demographic details, lifestyle factors, medical history, clinical measurements, cognitive assessments, and symptomatology.

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SartHak-0-Sach/Laptop-Price-Predictor

Just as the name suggests, this project's solo goal is to simplify finding laptops in a world where there are ton of companies offering variety of features and specifications at numerous price differences. This project aims to be the best analysis tool of what should be the price of a standard laptop with desired specifications. Follow for more😇✨

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AmitGairola/Python-Projects

This is a repository created for Python Projects

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prgrmcode/tr-earthquake-predictor

Turkey Earthquake Prediction 🌍📊: Unleashing AI/ML powers in Python for seismic forecasts. #MachineLearning #Python #DataScience 🤖

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Shegzimus/ML-Airline-Prediction-Model

Machine learning model leveraging XGBoost to predict flight status of airlines across various continents

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JovanLiem/VehiclePricePrediction

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muhammadhaerul/Repo-Machine-Learning-Haealthkathon-BPJS-2022

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peeti-sriwongsanguan/cicd-mlops-timeseries-walmart

END-to-END MLOps CICD pipeline using MLflow for model tracking and experimenting and Amazon S3 for storing model artifacts.

Language: Python - Size: 4 MB - Last synced at: 3 months ago - Pushed at: 9 months ago - Stars: 0 - Forks: 0

Ayush0345/ML-Forecasting-Methods

Forecasting House Prices in MSA counties in the United States using Machine Learning

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IanDublew/xGBoost-Sports-betting-Predictor

Sports betting match predictor

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closedloop-ai/cv19index

COVID-19 Vulnerability Index

Language: Python - Size: 7.13 MB - Last synced at: 1 day ago - Pushed at: over 2 years ago - Stars: 88 - Forks: 36

johannaschmidle/House-Price-Predictor

A machine learning model to accurately predict house prices based on various features such as quality, size, and location, utilizing Random Forest and XGBoost algorithms (Python)

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harmanveer-2546/Prediction-Of-Ticket-Cancellation

The objective is to develop a model that accurately predicts whether users will cancel their tickets. Each cancellation incurs a fine for the ticket registration site from the passenger company.

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Abrar2652/Road-Friction-Forecasting

A supervised classification machine learning approach to forecasting the road as safe (label 1) or dangerous (label 0) for driving in the arctic regions. If the friction is 0 <= x < 0.5 then we labeled it as 0, either 1 in the range 0.5 to 1.

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sergray/mlflow-xgboost-proba

MLflow XGBoost flavour with probabilities

Language: Python - Size: 257 KB - Last synced at: 18 days ago - Pushed at: 7 months ago - Stars: 0 - Forks: 0

Akash1070/BigMart-Sales-Prediction-

Building BigMart Sales Prediction

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HakimGhlissi/House-Price-Prediction-Using-a-XGBoost-classifier

Built a Linear Regression and XGBoost model to predict house prices from the Boston house price dataset

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grtvishnu/Air-Pollution-Prediction-and-Forecasting

:octocat: Detection and Prediction of Air quality Index :octocat:

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ShraddhaPChaudhari/Breast_Cancer_Detection

Breast cancer detection app using machine learning XG-BOOST model. Model deployment using python and flask.

Language: Tcl - Size: 4.79 MB - Last synced at: about 1 year ago - Pushed at: about 4 years ago - Stars: 0 - Forks: 0

peijin0405/ML-XGBoostModel-for-Deal-and-User-Churn-Forecast

This project employs XGBoost regression and XGBoost classifier model to predict user order and user churn on online travel agency data. Reach 97% prediction accuracy.

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jakemaz66/EntityResolution

An Entity Resolution project deploying a doctor-to-grant XGBoost classifier using Vector Search with HNSW Graphs

Language: Python - Size: 87.9 KB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 0

aotantawy/Monefy 📦

A stock prediction using machine learning

Language: CSS - Size: 7.27 MB - Last synced at: 4 months ago - Pushed at: over 2 years ago - Stars: 0 - Forks: 1

aarushi-vermaa/airbnb_price_occupancy_revenue_prediction

Building a model to predict airbnb price and future occupancy bucket to estimate revenue for future investors

Language: Jupyter Notebook - Size: 279 MB - Last synced at: about 1 year ago - Pushed at: about 3 years ago - Stars: 2 - Forks: 2

Sarthak-Mohapatra/US-Airlines-Tweets-Sentiment-Analysis

Classifying a tweet as positive, neutral, or negative sentiment using Natural Language Processing (CBOW approaches) and Traditional Machine Learning Algorithms.

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alexdatadesign/lfp_soc_ml

LiFePo4(LFP) Battery State of Charge (SOC) estimation from BMS raw data

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omerfarukeker/The-Complete-Journey

The Complete Journey Dataset: Churn Prediction

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Muhammad-Talha4k/House-Price-Prediction

House Price Prediction using Xgboost model in python

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Mawbhe/Predicting-hospital-admission

ED Triage prediction

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tadedoyinsola/Air-Quality-Project

This repository contains the AirQo Low -cost Quality monitor calibration challenge, the codes and readme files after data pre-processing and predictive modelling.

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YoweioY/Product_demand_forecast

利用機器學習方法預測產品的需求量

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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.

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MsTao-68/IMDB-NLP-Classification

# 自然语言处理 IMDB 情感分析数据集任务

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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。

Language: Jupyter Notebook - Size: 15 MB - Last synced at: over 1 year ago - Pushed at: about 3 years ago - Stars: 5 - Forks: 0

naikaly/credef

Credit Default Approximation for Unsecured Lending Built Machine Learning Classification models (Random Forest, LGBM, XGBoost) in Python to assess the probability of credit defaults.

Language: Python - Size: 18.6 KB - Last synced at: over 1 year ago - Pushed at: over 3 years ago - Stars: 0 - Forks: 0

DHwass/XGBoost-on-Home-Data-KAGGLE

In this project, XGBoost is applied to forecast real estate prices using the Boston Housing Dataset. The primary aim is to create an effective predictive model, assess its accuracy through metrics like Mean Absolute Error (MAE), and refine its performance by tuning hyperparameters with HYPEROPT.

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Ideas2IT/datascience-lab

Data Science Experiments Repository of Ideas2IT

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arminZolfaghari/Diabetes-Classification-XGBoost

Data Mining Course Project - Diabetes Classification with XGBoost - Winter 2022

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srivatsakoustub/BIG-MART-SALES-PREDICTION

conducted in-depth analysis of a large dataset containing historical sales data, product attributes and store information. --> Developed and implemented machine learning models, including regression algorithms, to accurately forecast sales for different products and stores and we finally obtained a better result through random forest regression alg

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Javihaus/Advanced-Time-series-analysis

Advance Time Series Analysis using Probabilistic Programming, Auto Regressive Neural Networks and XGBoost Regression.

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Keivaly/Xgboost-RandomForest

Contains solution of Jigsaw academy's final capstone project, mostly to indicate coding prowess and analytics methodology followed for recruiters

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shivangi-975/Machine_Learning

Machine Learning Concepts and Algorithms.

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SanyaGoyal/Allstate-Insurance-Predicting-Claim-Severity-Xgboost-ML

This is a solution to a Kaggle competition on predicting claim severity for Allstate Insurance using the Extreme Gradient Boosting (XgBoost) algorithm in R

Language: R - Size: 17.1 MB - Last synced at: over 1 year ago - Pushed at: over 7 years ago - Stars: 0 - Forks: 8

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.

Language: Jupyter Notebook - Size: 1.68 MB - Last synced at: over 1 year ago - Pushed at: over 3 years ago - Stars: 3 - Forks: 1

GeorgeOduor/bigmart

Predictive modeling

Size: 236 KB - Last synced at: over 1 year ago - Pushed at: over 6 years ago - Stars: 0 - Forks: 0

NatenaelTBekele/cardiovascular_disease_prediction

Aim of the problem is to detect the presence or absence of cardiovascular disease in person based on the given features

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Mfundo-debug/Bank_Customer_churn

Bank Customer Churn Prediction

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olesyamba/ICvsML

Usual linear regression or XGBoost? Combo! Or how I was investigating the impact of intellectual capital on NASDAQ-100 capitalization during 2 years.

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Ravjot03/Parkinsons-Disease

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Ravjot03/Machine-Learning-Models

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jimschacko/Hotel-reservation-Prediction-using-XGBoost

Hotel Reservation Prediction using XGBoost

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pranaya-mathur/Sentiment-Classification

Implemented Machine Learning Models on Amazon Fine Food Reviews Data Set

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ashcode028/Music-Genre-Classification

Classifying audio files using ML algorithms.

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TrilokiDA/analyticsVidhya

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alexarnold630/Dallas_Crime_Analysis_and_Prediction

Machine Learning Engine predicting Dallas crime incident status with visualization analysis using Tableau for crime time and locations.

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h30306/Kaggle-Airbnb-New-User-Booking

Kaggle competition. Our proposed using Hierarchical XGBoosting model to predict the target country of customer preference

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ankitapiu/Mercedes-Benz-Greener-Manufacturing

Xboost algorithm Model

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HarigovindV10/Credit-Card-Fraud-Detection

A credit card fraud detection algorithm.

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ek0212/TCIA-Classification

Dichotomized classification of glioblastomas and low grade gliomas using publicly available data

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4k11/Savorr

A website utilizing datasets trained using XGBoost algorithm to forecast sales in big mart with best possible accuracy in comparison to other existing models.

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nrgdoublex/TalkingData_challenge

models for TalkingData AdTracking Fraud Detection Challenge

Language: Python - Size: 5.86 KB - Last synced at: almost 2 years ago - Pushed at: about 7 years ago - Stars: 0 - Forks: 0

TanweerulHaque/VindiataBusinessCase-Study

A business case study to solve problems related to an airline startup using ML

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datacrypto-analytics/crypto-analysis-cli

Analise todas as criptomoedas disponíveis na binance spot com algoritmos Machine Learning.

Language: Python - Size: 1.65 MB - Last synced at: 16 days ago - Pushed at: over 4 years ago - Stars: 5 - Forks: 2

keshavbhandari/Gredel

Automated R-Shiny based UI platform to create and deploy a XGBoost model on ANY dataset. Additionally it also includes black box interpretation techniques such as PDP, ALE Plots, permutation importance.

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lperconti/Visitation

Visitation Modeling - Capstone Project for Flatiron DataScience Bootcamp. In this project, I look at three winter resorts and utilize weather and event data to predict visitation for each resort. Uses Machine Learning (Regression) analysis to make predictions.

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KindlyGentleman/DS-DA-Bank-Churn-Prediction

This repo contains a machine learning project for predicting bank customer churn using XGBoost. It includes data preprocessing, model training, and deployment as an API and Streamlit app in a Docker container. The repo also contains a report and images documenting the project.

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