This module allows users to analyze k-means & hierarchical clustering, and visualize results of Principal Component, Correspondence Analysis, Discriminant analysis, Decision tree, Multidimensional scaling, Multiple Factor Analysis, Machine learning, and Prophet analysis.

Analysis classification rules of decision tree

Classification Analysis with supervised algorithms KNN, RandomForest and SVM. Use of PCA and K-means to recognize the importance of features.

This project was implemented as part of my master's thesis in the program "Specialization in Information Systems" at the Hellenic Open University (HOU)/https://www.eap.gr/en/postgraduate-specialization-in-information-systems/.

Customer Churn Predictions Using Classification Analysis

Credit Card Fraudulent Detection

I utilized data science techniques to predict heart disease, exploring relationships between various health indicators and cardiovascular conditions.

A solution that will predict whether a customer will churn

This course introduces techniques to transform raw data into actionable insights for business analysis, covering customer, operation, and people analytics. Customer analytics examines and predicts customer behavior; operation analytics aligns supply with demand and optimizes decisions; people analytics uses data to manage the workforce effectively.

Python

The repository for standard machine learning model developments and related tasks