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
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.
Hybrid RecSys, CF-based RecSys, Model-based RecSys, Content-based RecSys, Finding similar items using Jaccard similarity
Machine Learning model for price prediction using an ensemble of four different regression methods.
Predicting Snow Conditions in Passo Tonale (Trento, Italy)
Estimate Formula 1 qualifying results using ML
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.
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.
EcoSphereAI is a sustainable, AI-driven platform optimizing connectivity systems with features like energy optimization, predictive maintenance, and sustainability reporting. Build the future of efficient and green networks!
Python package that converts an XGBRegressor or XGBClassifier model to an Excel formula expression.
An attempt to implement an AutoML around xgb, catboost and lightgbm
Forecasting Vehicle Sales Using XGBoost - A Practical Guide to MLops Best Practices
Fully automated (GH Actions) air quality prediction system (XGBoost) using free server-less solutions (Hopsworks)
A Streamlit web app that predicts the likelihood of road accidents based on road and environmental conditions using a trained machine learning model. Enter road parameters manually and get real-time risk predictions powered by an ensemble learning model.
AQI Predictor V2 use multiple Supervised Machine Learning with Hyper tuning. ML algorithms used Linear Regressor, Lasso Regressor, Decision Tree Regressor, Random Forest Regressor, XGboost Regressor. The Model deployed on web and can predict AQI visit https://aqipredictor.up.railway.app/
This repository contains detailed code for the academic project related to concrete compressive strength estimation.
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