xgboost-regression
There are 1 repository under xgboost-regression topic.
abhinav-bhardwaj / Walmart-Sales-Time-Series-Forecasting-Using-Machine-Learning
Time Series Forecasting of Walmart Sales Data using Deep Learning and Machine Learning
Language:Jupyter Notebook 26tstran155 / Optimization-of-building-energy-consumption
This repo demonstrates how to build a surrogate (proxy) model by multivariate regressing building energy consumption data (univariate and multivariate) and use (1) Bayesian framework, (2) Pyomo package, (3) Genetic algorithm with local search, and (4) Pymoo package to find optimum design parameters and minimum energy consumption.
bayesian-optimizationbuilding-energyhvacmachine-learning-algorithmssobol-indicesxgboost-regressioncross-validationlatin-hypercube-samplingipoptpyomononlinear-optimizationgurobi-solvergenetic-optimization-algorithmprobabilistic-inferencestochastic-optimizationmultivariate-regressionmulti-objective-optimizationnsga-iipymoodeterministicLanguage:Jupyter Notebook 19ritikdhame / Electricity_Demand_and_Price_forecasting
Building Time series forecasting models, including the XGboost Regressor, GRU (Gated Recurrent Unit), LSTM (Long Short-Term Memory), CNN (Convolutional Neural Network), CNN-LSTM, and LSTM-Attention. Additionally, hybrid models like GRU-XGBoost and LSTM-Attention-XGBoost for Electricity Demand and price prediction
Language:Jupyter Notebook 15ashishrana1501 / Forest-Fire-Prediction
Algerian Forest Fire Prediction
Language:Jupyter Notebook 11RezaSaadatyar / Time-Series-Analysis-in-Python
This repository contains Python functions for predicting time series.
Language:Python 11esvs2202 / Concrete-Compressive-Strength-Prediction
The aim of this project is to develop a solution using Data science and machine learning to predict the compressive strength of a concrete with respect to the its age and the quantity of ingredients used.
Language:Jupyter Notebook 9aayush1036 / housing-rent-prediction
End to End Machine Learning Project along with deployment.
Language:Jupyter Notebook 8- Max-Q
MitchellTesla / Max-Q
Machine-Learning: eXtreme Gradient-Boosting Algorithm Stress Testing
Language:C++ 8 artisan1218 / Recommendation-System
Hybrid RecSys, CF-based RecSys, Model-based RecSys, Content-based RecSys, Finding similar items using Jaccard similarity
Language:Python 7Erdos1729 / food-demand-forecasting
This repository will work around solving the problem of food demand forecasting using machine learning.
Language:Jupyter Notebook 7manashpratim / Big-Mart-Sales-Prediction
Predicting the sales of a store
Language:Jupyter Notebook 7shreyaswankhede / Airbnb_Rental_Price_Prediction
The objective of this project is to model the prices of Airbnb appartments in London.The aim is to build a model to estimate what should be the correct price of their rental given different features and their property.
Language:Python 7- Language:Python 7
aws-samples / amazon-sagemaker-xgboost-regression-model-monitor-and-alerting
How to train, deploy and monitor a XGBoost regression model in Amazon SageMaker and alert using AWS Lambda and Amazon SNS. SageMaker's Model Monitor will be used to monitor data quality drift using the Data Quality Monitor and regression metrics like MAE, MSE, RMSE and R2 using the Model Quality Monitor.
Language:Jupyter Notebook 6alexdatadesign / lfp_soc_ml
LiFePo4(LFP) Battery State of Charge (SOC) estimation from BMS raw data
Language:Jupyter Notebook 5dgovor / Housing-Price-Prediction-Python
Machine Learning model for price prediction using an ensemble of four different regression methods.
Language:Python 5Jesus-Vazquez-A / Insurence
We solve a regression problem in which it consists of calculating the health insurance charge in the United States Where we will break down the project into 5 phases: Exploratory Analysis. Feature Engineering. Selection of the ideal model. Development of the final model. Creation of a web application in streamlit.
Language:Jupyter Notebook 5How to train a XGBoost regression model on Amazon SageMaker, host inference on a Docker container running on Amazon ECS on AWS Fargate and optionally expose as an API with Amazon API Gateway.
Language:Jupyter Notebook 4gitzaidi / Prediction-of-volatility-in-stock-movements-on-the-US-market
Answer to CFM challenge US-Stock-Market volatility prediction - Ranked 4th
Language:Jupyter Notebook 4izelcelikkaya / housepriceprediction
This repo is a part of K136. Kodluyoruz & Istanbul Metropolitan Municipality Data Science Bootcamp. The project aims to produce a machine learning model for home price estimation. The model was built on the Kaggle House Prices - Advanced Regression Techniques competition dataset.
Language:Python 4nidragedd / udadsnd-p4-airbnb_inside
Udacity DataScience nanodegree 4th project: pick a dataset, explore it and write a blog post
Language:Jupyter Notebook 4arka57 / Ted-Talk-Views-Prediction
Building a predictive model to predict views of Ted Talks in YouTube from dataset of past events using Machine Learning models
Language:Jupyter Notebook 3How to train a XGBoost regression model on Amazon SageMaker and host inference as an API on a Docker container running on AWS App Runner.
Language:Jupyter Notebook 3How to train a XGBoost regression model on Amazon SageMaker, host inference on a serverless function in AWS Lambda and optionally expose as an API with Amazon API Gateway
Language:Jupyter Notebook 3coderjolly / football-player-prediction
There is an intense transfer speculation that surrounds all major player transfers today. An important part of negotiations is predicting the fair market price for a player. Therefore, we are predicting this Market Value of a player using the data provided in csv format.
Language:Jupyter Notebook 3Collinjia / NLP-CLRP-Contest
Kaggle NLP competition : Rate the complexity of text using BERT
Language:Jupyter Notebook 3GeorgeBatch / moleculenet
MSc Dissertation: Estimating Uncertainty in Machine Learning Models for Drug Discovery
Language:Jupyter Notebook 3AlaaMahmoud95 / Concrete-Compressive-Strength
Regression Machine Learning Project
Language:Jupyter Notebook 2Anamicca23 / Muli-Class-Obesity-Risk-Level-Prediction-Project-using-ML
Advancing Healthcare with 91% Accurate Prediction of Obesity Risk Levels Using XGBoost ,LightGBMand CatBoostClassifier Model
Language:Jupyter Notebook 2Harish-or-Peter / Appliance-Energy-Prediction
Predicted residential energy consumption using Linear Regression, Random Forest, and XGBoost models. Random Forest showed best performance, offering insights into energy usage patterns. Code and results on GitHub for reference and optimization.
Language:Jupyter Notebook 2KalinNonchev / xgbexcel
Python package that converts an XGBRegressor model to an Excel formula expression.
Language:Python 2MYoussef885 / House_Price_Prediction
The "House Price Prediction" project focuses on predicting housing prices using machine learning techniques. By leveraging popular Python libraries such as NumPy, Pandas, Scikit-learn (sklearn), Matplotlib, Seaborn, and XGBoost, this project provides an end-to-end solution for accurate price estimation.
Language:Jupyter Notebook 2samagra44 / Zomato_Delivery_Time_Prediction
The Zomato Delivery Time Prediction Application is a machine learning-driven Flask web application designed to predict the estimated delivery time for food orders placed on the Zomato platform.
Language:Python 2scalable-ml-deep-learning / predicting-snow-conditions
Predicting Snow Conditions in Passo Tonale (Trento, Italy)
Language:Python 2- ipl_2023_score_predictor
ved-et9 / ipl_2023_score_predictor
🏏Predicting Ipl score on the model trained by Various ML algos 🔥,deployed the interface on hugging face for interacting with online users
Language:Jupyter Notebook 2 WalidKW / House-Prices
Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home.
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