KNN, Naive Bayes and Trees - Wine UCI Dataset

This project is aimed at predicting loan terms issued by World Bank to developing countries by using Regression, Decision Trees, K-Nearest Neighbors & PLSR in R

Data Science in R (multiple algo`s)

Prediction whether the economic crisis will occur in Africa countries

Problem Sets and Final Exam for Texas A&M ECMT 670: Machine Learning in Econometrics

This project is about credit risk measurement for a bank. The project entailed a comprehensive analysis of client default tendencies relative to their backgrounds using advanced classification models, providing actionable insights by model comparison and refinement.

Data Science Projects in R

EDA and classification predictions of airline satisfaction using R

Vorhersage, ob ein Spender aufgrund seiner Vorgeschichte erneut Blut spenden wird. | Predicting whether a donor will return to donate blood given their donation history.

BEGINNER - This is a classification project for the subject "Data Mining" in the 3rd year of Statistics (SSE) at the University of Milano-Bicocca.

Supervised learning and unsupervised in R, with a focus on regression and classification methods.

It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase. Here, the aim is to analyze the dataset and detect the fradulent transactions.

Projects developed under the Data Mining I college chair during the 2019/2020 school year

Fixed-volume neighborhood classifier with binary feedback

This R notebook applies machine learning classification methods in the context of organizational network analysis. The goal was to test the predictive accuracy of various supervised learning models on company employee network data.

ML models testing on Chest X-Rays. Pneumonia identification

Statistical Learning with R

The primary objective was to investigate the parameters contributing for customer churn (attrition) in the Telecom Industry. A Logistic Regression Model was developed and validated with test data to predict customer churn.

In this project I collected images of blue eyes, brown eyes, eyes with prominent limbal rings, and eyes with Kayser–Fleischer rings. I then took a random 50px, 50px sample of the iris of the blue and brown eyes. I ran 5 experiments outlined in my paper and discuss interesting ideas for future research.

The project involves deciding on the mode of transport that the employees prefer while commuting to the office.