A project to deploy an online app that predicts the win probability for each NBA game every day. Demonstrates end-to-end Machine Learning deployment.
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Updated
May 11, 2024 - Jupyter Notebook
A project to deploy an online app that predicts the win probability for each NBA game every day. Demonstrates end-to-end Machine Learning deployment.
Perform a survival analysis based on the time-to-event (death event) for the subjects. Compare machine learning models to assess the likelihood of a death by heart failure condition. This can be used to help hospitals in assessing the severity of patients with cardiovascular diseases and heart failure condition.
This repo has been developed for the Istanbul Data Science Bootcamp, organized in cooperation with İBB and Kodluyoruz. Prediction for house prices was developed using the Kaggle House Prices - Advanced Regression Techniques competition dataset.
A Machine Learning project for Machine Learning Internship offered by InternshipStudio.
No-Caffeine-No-Gain's Deep Knowledge Tracing (DKT)
Easy Custom Losses for Tree Boosters using Pytorch
Using machine learning models to predict the probability of a windows system getting infected by various families of malware, based on different properties of that system.
Kaggle Competition PUBG Player Placement Prediction (ALDA Project Group P09)
Classifying Audio to Emotion
Machine learning solutions for the American Express credit default prediction Kaggle competition
Submission for Grab Challenge - AI For Sea 2019
This is a Django web application that uses machine learning to predict whether a product will go on backorder or not. It uses a pre-trained Random Forest Classifier, Decision Tree and LGBM models to make predictions based on various features such as product availability, lead time, and more.
My own realization of Bayesian Optimization for LightGBM
This project aims to predict the win rate, hitting average and ERA of 10 KBO teams in the 2020 season. The hitting average and ERA was predicted through a LSTM model and the win rate was predicted through a LGBM model.
Kaggle Competition - Analysis and prediction of PUBG players' finishing placement based on their final stats
Concrete strength prediction based on its composition and curing process using CatBoost, XGBoost and LGBM.
In this project, we have to develop accurate models of metered building energy usage in the following areas: chilled water, electric, hot water, and steam meters. The data comes from over 1,000 buildings over a three-year timeframe. With better estimates of these energy-saving investments, large scale investors and financial institutions will be…
In this project, Jane Street which is a quantitative trading company ,challenged us to build our own quantitative trading model to maximize returns using market data from a major global stock exchange. Next, they’ll test the predictiveness of our models against future market returns.
code for the Ubiquant market prediction Kaggle competition. Top 1% rank: 20th out of 2893 teams.
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