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
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.
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.
LiFePo4(LFP) Battery State of Charge (SOC) estimation from BMS raw data
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.
Algerian Forest Fire Prediction
Drift Detection in Gas Sensor Array at Different Concentration Levels ☢️
This repository contains Python functions for predicting time series.
Crypto & Stock* price prediction with regression models.
Time series processing library
Serverless ML system to predict the direction and volume of electricity flows to and from the Netherlands and its energy transmission partners.
End to End Machine Learning Project along with deployment.
Predicting the sales of a store
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.
This repository will work around solving the problem of food demand forecasting using machine learning.
Machine-Learning: eXtreme Gradient-Boosting Algorithm Stress Testing
Hybrid RecSys, CF-based RecSys, Model-based RecSys, Content-based RecSys, Finding similar items using Jaccard similarity
XGBoost and GNN training and models for prediction of Hansen solubility parameters
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