Amazon SageMaker Examples

2022-01 데이터마이닝이론및응용 프로젝트 <장애인 이동권 제고를 위한 콜택시 이용편의 증진 방안 : 서울특별시를 중심으로>

Crypto & Stock* price prediction with regression models.

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

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

Yandex Practicum Data Science project

Build predictive models for the game-by-game attendance all MLB teams 2023 season. The 1st place solution at MinneMUDAC 2023 Data Science Challenge.

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.

Este fue el proyecto final del Bootcamp de Data Science y Machine & Deep Learning, fue desarrollado junto con mi compañero Pablo Pita. Este proyecto trata de predecir el consumo y la produccion de clientes con placas solares, en el enlace podréis ver la presentación que realizamos

This code demonstrates the use of machine learning to model the multimodal nature of a single cell. Using machine learning to predict RNA from DNA, that is, using chromatin accessibility data to predict the RNA gene expression and to predict surface protein from RNA, that is, using RNA sequence data to predict surface protein levels in a cell

Santander Customer Transaction Prediction

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

This project aims to predict Wind Turbine output power and searches for any anomalies

Sales Forecasting for a Retail Company. A forecasting model is developed to reduce warehouse costs and stock-outs by using a scalable set of recursive machine learning algorithms. This model predicts demand for the next 8 days at a store-product level, based on the historical company’s data.

In this project I have implemented 15 different types of regression algorithms including Linear Regression, KNN Regressor, Decision Tree Regressor, RandomForest Regressor, XGBoost, CatBoost., LightGBM, etc. Along with it I have also performed Hyper Paramter Optimization & Cross Validation.

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

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.