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

Time Series Forecasting of Walmart Sales Data using Deep Learning and Machine Learning

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.

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.

Algerian Forest Fire Prediction

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

This repository contains Python functions for predicting time series.

Crypto & Stock* price prediction with regression models.

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.

End to End Machine Learning Project along with deployment.

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.

Machine-Learning: eXtreme Gradient-Boosting Algorithm Stress Testing

Predicting the sales of a store

回归预测

Hybrid RecSys, CF-based RecSys, Model-based RecSys, Content-based RecSys, Finding similar items using Jaccard similarity

Time series processing library

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.

This repository will work around solving the problem of food demand forecasting using machine learning.

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.

Machine Learning model for price prediction using an ensemble of four different regression methods.