9th place solution in "Santa 2020 - The Candy Cane Contest"

Crypto & Stock* price prediction with regression models.

No description provided.

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

This repository contains code and data for analyzing real estate trends, predicting house prices, estimating time on the market, and building an interactive dashboard for visualization. It is structured to cater to data scientists, real estate analysts, and developers looking to understand property market dynamics.

Predicting the Residential Energy Usage across 113.6 million U.S. households using Machine Learning Algorithms (Regression and Ensemble)

Notebooks for Kaggle competition

This repository contains code and resources for an end-to-end regression project on retail sales prediction. The goal of this project is to develop a regression model that can accurately predict retail sales based on various features.

Amazon SageMaker Examples

No description provided.

Automobile dataset for used Car Price Analysis to predict the price of a vehicle with their features and performance factor to provide the exact value of a vehicle for buyer seller satisfaction using exploratory data analysis and machine learning models.

Predict stock returns using ARIMA and LightGBM to analyze historical data and uncover key drivers with feature importance in this financial forecasting project.

A Machine Learning Case Study based on helping the company target customers by predicting the customer loyalty score based on the transactions data.

Food Delivery Time Prediction using Machine Learning Techniques

Silver medal solution for the "M5 Forecasting - Accuracy" Kaggle competition

Analysis of time series data from IoT devices

Projeto de previsões de pontos de chegada em corridas de táxi na cidade do Porto, Portugal.

Notes, tutorials, code snippets and templates focused on LightGBM for Machine Learning

In this section, we will use machine learning algorithms to perform time series analysis.

A final project of Data Science Bootcamp Batch 20 in Rakamin Academy.

VALORA AI is a Multimodal Pricing Prediction Model that uses textual and visual data to make precise predictions on product prices

Repository for the Elo Merchant Category Recommendation Kaggle competition.

This machine learning model was developed for "House Prices - Advanced Regression Techniques" competition in Kaggle by using several machine learning models such as Random Forest, XGBoost and LightGBM.

Test using LightGBM and FastTree models with GPU acceleration in C#/.NET via ML.NET.

A machine learning based university degree recommendation system

Examples using LightGBM for several ML tasks

This repository contains a project I completed for an NTU course titled CB4247 Statistics & Computational Inference to Big Data. In this project, I applied regression and machine learning techniques to predict house prices in India.

End-to-end Bus Demand Forecasting project: performed data preprocessing, feature engineering, and trained models (LightGBM) to predict seat demand. Includes cross-validation, evaluation (RMSE), and generates submission files for competition use.

This repository contains my solution for the Kaggle competition Automated Essay Scoring 2.0. The goal of this project is to develop an automated system capable of scoring essays based on their content and quality using machine learning techniques.

Bike rentals predictions by using live data from Api's with lightgbm regressor.