xgboost-regression
There are 476 repositories under xgboost-regression topic.
ritikdhame/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
abhinav-bhardwaj/Walmart-Sales-Time-Series-Forecasting-Using-Machine-Learning
Time Series Forecasting of Walmart Sales Data using Deep Learning and Machine Learning
MYoussef885/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.
tstran155/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.
alexdatadesign/lfp_soc_ml
LiFePo4(LFP) Battery State of Charge (SOC) estimation from BMS raw data
esvs2202/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.
ashishrana1501/Forest-Fire-Prediction
Algerian Forest Fire Prediction
Dalageo/ML-GasSensorDrift
Drift Detection in Gas Sensor Array at Different Concentration Levels ☢️
Mechres/Price-Predict
Crypto & Stock* price prediction with regression models.
holukas/diive
Time series processing library
RezaSaadatyar/Time-Series-Analysis-in-Python
This repository contains Python functions for predicting time series.
banzuzi-carioni/cross-border-electricity-flow-prediction
Serverless ML system to predict the direction and volume of electricity flows to and from the Netherlands and its energy transmission partners.
aayush1036/housing-rent-prediction
End to End Machine Learning Project along with deployment.
Erdos1729/food-demand-forecasting
This repository will work around solving the problem of food demand forecasting using machine learning.
manashpratim/Big-Mart-Sales-Prediction
Predicting the sales of a store
MitchellTesla/Max-Q
Machine-Learning: eXtreme Gradient-Boosting Algorithm Stress Testing
shreyaswankhede/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.
Anamicca23/Muli-Class-Obesity-Risk-Level-Prediction-Project-using-ML
Advancing Healthcare with 91% Accurate Prediction of Obesity Risk Levels Using XGBoost ,LightGBMand CatBoostClassifier Model
artisan1218/Recommendation-System
Hybrid RecSys, CF-based RecSys, Model-based RecSys, Content-based RecSys, Finding similar items using Jaccard similarity
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.
Chandrakant817/Calories-Burned-Prediction
Calories-Burned-Prediction Using Machine Learning. (Regression Use Case)
darjacvetkovic/HSP-predictions
XGBoost and GNN training and models for prediction of Hansen solubility parameters
dgovor/Housing-Price-Prediction-Python
Machine Learning model for price prediction using an ensemble of four different regression methods.
Jesus-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.
aws-samples/amazon-sagemaker-xgboost-regression-model-hosting-on-aws-lambda-and-amazon-api-gateway
How 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
scalable-ml-deep-learning/predicting-snow-conditions
Predicting Snow Conditions in Passo Tonale (Trento, Italy)
shubham5027/Store-Item-Demand-Forcasting
The "Sales Demand Forecasting Regression Model" project aims to develop a predictive model that forecasts future sales demand based on historical data and relevant influencing factors. The project follows a structured approach, encompassing data collection, preprocessing, model selection, training, evaluation, and deployment.
coderjolly/player-market-value-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.
FarhanaTeli/Factors-Influencing-US-Home-Prices
Using publicly available data for the national factors that impact supply and demand of homes in US, build a data science model to study the effect of these variables on home prices.
gitzaidi/Prediction-of-volatility-in-stock-movements-on-the-US-market
Answer to CFM challenge US-Stock-Market volatility prediction - Ranked 4th
izelcelikkaya/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.
kaledhoshme123/A-proposed-model-that-can-predict-the-assessment-of-both-Syntax-Cohesion-Vocabulary-Phraseology-
The proposed model is able to predict the evaluation of both grammatical coherence, vocabulary and grammatical conventions, so that the evaluation can give each of those criteria a value between 1 and 5, I did not treat the system as a classification process, but rather it was treated as a REGRESSION issue. It includes several steps through which a few errors were reached, all ranging between 0.25 for each criterion. The values of the weights that were reached can also be used to deal with the issue as a classification process (but it was not dealt with as well in this proposed methodology).
sparsh0106/Regression-Analysis-of-Ecommerce-Customers
Regression Analysis of Ecommerce Customers Dataset using Linear Regression and XGBRegressor
sushant1827/Traffic-Forecasting-using-IoT-Sensor-Data
Demonstrates how to utilize XGBoost for traffic forecasting using data gathered from IoT sensors, highlighting its efficiency in processing complex datasets and delivering accurate predictions.
tmalik1116/F1_Qualifying_Predictor_ML
Estimate Formula 1 qualifying results using ML