An ensemble of machine learning models for detecting fraudulent credit card transactions, utilizing advanced techniques for feature selection, data imbalance handling, and hyperparameter tuning.

Feature selection package of the mlr3 ecosystem.

Validation (like Recursive Feature Elimination for SHAP) of (multiclass) classifiers & regressors and data used to develop them.

This study aimed to assess whether machine learning algorithms would outperform traditional modeling in developing a cesarean delivery prediction model among gravidas with morbid obesity (body mass index of ≥40 kg/m2) to determine whether a primary cesarean delivery may be beneficial.

A Jupyter Notebook with the analysis and prediction of Final Grades (Pass/Fail) for students of mechatronics engineering in several mechanic courses.

The research aims to harness machine learning for predicting cardiovascular diseases based on numerous risk factors, addressing the high fatality rates associated with cardiovascular conditions.

This repository contains the notebook used for the Spring 2021 Kaggle Dengue Fever Prediction Competition. Placement was in the top 10% with a MAE of 24.86. Our best approach involved Random Forest Regression on a reduced featureset selected with Recursive Feature Elimination in combination with correlation with the target (number of dengue cases).

Delved into advanced techniques to enhance ML performance during the uOttawa 2023 ML course. This repository offers Python implementations of Naïve Bayes (NB) and K-Nearest Neighbor (KNN) classifiers on the MCS dataset.

The goal of this project is to develop a predictive model that accurately detects failures in the Air Pressure System (APS) of heavy Scania trucks. The APS is responsible for generating pressurized air used in various functions of the truck, such as braking and gear changes. By detecting failures in the APS system.

Predict the attrition (Yes/No) of employees, identify factors significantly impacting it, and finally state recommendations on how to mitigate the attrition.

Answer to how to select variables in data set and build simpler, faster, more reliable and interpretable ML models

Unlocking Insights and Accurate Price Predictions for Uber Rides through Extensive Data Analysis.

Logistic regression model build on lead score data to score leads on the basis of their probability of conversion.

本项目是论文《CAT-RFE：点击欺诈的集成检测框架》的实验代码。

First project implementing Logistic Regression

Used CDC dataset for heart attack detection in patients. Balanced the dataset using SMOTE and Borderline SMOTE and used feature selection and machine learning to create different models and compared them based on metrics such as F-1 score, ROC AUC, MCC, and accuracy.

Acute Myeloid Leukemia Risk Group Prediction from Gene Expression Data with Feed-Forward Neural Networks

Forecast stocks using machine learning algorithms. This is the final project for Data Science 3580.

To model the demand for shared bikes with the available independent variables