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This Python script visualizes the decision boundaries created by a linear Support Vector Classifier (SVC) on the Iris dataset. It utilizes scikit-learn for machine learning functionalities and matplotlib for plotting. The code loads the Iris dataset, trains a linear SVC on the first two features (sepal length and sepal width)
This is a binary classification problem. There are numerous factors that can contribute to the presence of heart disease. What is the most important factor causing heart disease? Can an accurate classifier be built to predict the presence of heart disease in patients? These are the questions we want to answer with this project.
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.
Customer Churn Prediction is a machine learning project aimed at predicting whether a specific user will leave a service or not. The project involves extensive exploratory data analysis (EDA), model training and deployment of a Streamlit web application for user interaction.
Bachelor thesis work: Debugging of a counterfactual-explanation library (CFNOW) and its usage in the context of eXplainable Affective Computing. Final evaluation: 30/30.