PCA. Clustering Algorithms. Business Analytics.

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Scalable clustering framework for big data using KMeans++, DBSCAN, BIRCH, OPTICS and DENCLUE, applied to NYC Taxi mobility analytics and credit card fraud detection.

Practical Machine Learning : Machine Learning in Nut shell, Supervised Learning, Unsupervised Learning, ML applications in the real world. Introduction to Feature engineering and Data Pre-processing: Data Preparation, Feature creation, Data cleaning & transformation, Data Validation & Modelling, Feature selection Techniques, Dimensionality reduction, Recommendation Systems and anomaly detection, PCA ML Algorithms: Decision Trees, Oblique trees, Random forest, Bayesian analysis and Naïve bayes classifier, Support vector Machines, KNN, Gradient boosting, Ensemble methods, Bagging & Boosting, Association rules learning, Apriori and FP growth algorithms, Linear and Nonlinear classification, Regression Techniques, Clustering, K-means, Overview of Factor Analysis, ARIMA, ML in real time, Algorithm performance metrics, ROC, AOC, Confusion matrix, F1score, MSE, MAE, DBSCAN Clustering in ML, Anomaly Detection, Recommender System Self-Study: • Usage of ML algorithms, Algorithm performance metrics (confusion matrix sensitivity, Specificity, ROC, AOC, F1score, Precision, Recall, MSE, MAE) • Credit Card Fraud Analysis, Intrusion Detection system

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A project for detecting credit card fraud using classification and clustering techniques. This repository includes data preprocessing, model training, and evaluation for effective fraud detection.

Clustering Credit Card Payers

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In this notebook we will explore different approaches for clustering using the credit card dataset available on kaggle.

Customer Segmentation for Marketing Optimization K-Means clustering on credit card data.

In this Notebook I have tried to cover some of important points on Different Clustering Algorithms using Credit Card Data set for Segmenting Customer.

This project deals with the segmentation and grouping of the bank credit card customers using UnSupervised K-Means Clustering Algorithm. The project involves below steps in the life-cycle and implementation. 1. Data Exploration, Analysis and Visualisations 2. Data Cleaning 3. Data Pre-Processing and Scaling 4. Model Fitting 5. Model Validation using Performance Quality Metrics namely WCSS, Elbow Method and Silhouette Coefficient/Score 6. Optimized Model Selection with appropriate number of clusters based on the various Performance Quality Metrics 7. Analysis Insights and Interpretations of 2 different business scenarios with various Visualisations

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Using Gaussian Clustering and PCA Techniques to make clusters of the Credit Car data

Customers segmentation based on their credit card usage behavior.

Our project proposes a unique approach to credit card fraud detection using a hybrid learning technique combining Clustering-based Autoencoder and Random Forest. Our approach outperforms traditional methods in detecting fraudulent transactions in real-time. Supported by University of Hull, UK.

Creating a segment's of some customers from a bank based on their credit card details and divide them into some groups through clustering algorithms and then making a prediction on those groups for new customer's.

This project is related to the clustering of customers based on their credit card history

The main objective of this project is to build a customer segmentation based on credit card payments behavior during the last six months to define marketing strategies.

Credit Card Clustering (PCA + Kmeans + Agglomerative Hierarchical + Gaussian Mixture + Clustering with PyCaret)

Proyecto que integra EDA y clustering para segmentar clientes de una tarjeta de crédito usando Python, identificando patrones financieros y apoyando estrategias personalizadas. Parte de mi portfolio como ingeniero consultor especializado en análisis de datos, ingeniería y gestión de proyectos.

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machine learning

Run K-Means algorithm on a credit card dataset.

The most common way for clustering using Python in Credit Card dataset (with explanation)

clustering analysis of a data set containing credit card data in python

This GitHub project focuses on the analysis, clustering, and classification of credit card data.

"CreditGuard: Detecting Credit Card Fraud with Clustering & Classification - Analyzing European bank data to enhance security."

Customer segmentation analysis on credit card users to identify distinct customer groups based on their behavior and characteristics. The analysis uses unsupervised machine learning techniques, specifically K-means clustering, to group customers into meaningful segments.

Hidden Markov Model (HMM) is used to model the sequence of operations in credit card transaction processing and demonstrate how to use it to identify fraud. An HMM is initially conditioned on a cardholder's normal behaviour using the KMeans algorithm for clustering. HMM calculated the probability of sequence, and when a qualified HMM does not authorize a credit card purchase with a high enough likelihood, it is classified as 'Fraudulent.' This approach comes out to be efficient, scalable and quite accurate in real-time predictions.