Dental caries analysis. For analysis i used: k-means clustering, k-neighbors classifier, decision tree classifier. Libraries: scikit-learn.

Coronary heart disease analysis, dataset - https://www.kaggle.com/datasets/billbasener/coronary-heart-disease. For analysis i used: k-means clustering, k-neighbors classifier, decision tree classifier. Libraries: scikit-learn.

Using supervised Machine Learning algorithms to build models for classification of Heart Diseases among patients and predicting the risk of getting a Heart Disease in the future.

This project focuses on building a fraud detection model for credit card transactions using a dataset containing transactions made by European cardholders in September 2013. We are working with a highly unbalanced dataset and the challenge lies in effectively detecting fraudulent transactions while minimizing false positives.

This project analyzes the chemical properties of wines to identify key factors influencing quality. By leveraging machine learning techniques, i aim to develop predictive models that accurately classify wine quality, providing valuable insights for producers and enthusiasts alike.

Stroke analysis, dataset - https://www.kaggle.com/datasets/fedesoriano/stroke-prediction-dataset. For analysis i used: mlp classifier, k-means clustering, k-neighbors classifier. Libraries: tensorflow, scikit-learn.

Iris Classification : Developed a ML Model for classifying iris flowers based on their features using Python, scikit-learn, and TensorFlow.

Credit scoring is a crucial task in financial institutions to assess the creditworthiness of individuals or businesses. This project focuses on building classification models to predict credit scores based on various features such as income, debt, and credit history.

Experimenting ML Algorithms, feature selection, cross validation and feature transformation on self-annotated custom Eskişehir real estate dataset. - 2021 - Yildiz Technical University

Developed a text classification model to classify SMS as either spam or non-spam using Python.