Includes top ten must know machine learning methods with R.
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Updated
Mar 6, 2024
Includes top ten must know machine learning methods with R.
Solution for the Ultimate Student Hunt Challenge (1st place).
In this work an application of the Triple-Barrier Method and Meta-Labeling techniques is explored with XGBoost for the creation of a sentiment-based trading signal on the S&P 500 stock market index. The results confirm that sentiment data have predictive power, but a lot of work is to be carried out prior to implementing a strategy.
In this project we will be using the publicly available and Kaggle-popular LendingClub data set to train Linear Regression and Extreme Gradient Descent Boosted Decision Tree models to predict interest rates assigned to loans. First, we will clean and prepare the data. This includes feature removal, feature engineering, and string processing.The…
Detecting Fraudulent Blockchain Accounts on Ethereum with Supervised Machine Learning
mlim: single and multiple imputation with automated machine learning
This repository contains several machine learning projects done in Jupyter Notebooks
Sports Analytics in R (Gradient Boost approaches for Decision Tree in Regression problems)
Comparison of ensemble learning methods on diabetes disease classification with various datasets
Identifying the most influential food groups on COVID-19 recovery rate: exploratory data analysis and statistical modeling
This repository is associated with interpretable/explainable ML model for liquefaction potential assessment of soils. This model is developed using XGBoost and SHAP.
Kaggle challenge to predict if a customer is satisfied or dissatisfied with their banking experience.
This repo contains the result of my computer science course: An automated tool to classify credit card transactions. Could be used with any dataset
Kaggle challenge asking to predict how a supplier will quote a price on a given tube assembly.
Algorithms used to confirm whether a celestial body is a planet or not.
Big Mart Sales Prediction is a data-driven project aiming to forecast product sales accurately across Big Mart outlets. Leveraging machine learning and comprehensive datasets, our project empowers retailers to optimize inventory, enhance profitability, and make informed decisions in the dynamic world of retail.
Kaggle challenge asking to predict the final price of each home based on their description/properties.
Kaggle challenge asking to predict the outcome for each animal of the shelter.
This project compares the different machine learning models on Walmart Weekly Sales Data and predicts the weekly sales for the test data.
Using data to help us choice high quality wine
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