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A project on anomaly detection in voice conversations aims to develop algorithms that can automatically detect unusual patterns or behaviors in spoken interactions, helping identify potential threats, anomalies, or aberrations in real-time communication
Detect anomalies in transactional data using advanced statistical methods and machine learning algorithms. Enhance fraud detection and anomaly identification in financial transactions for improved security and risk management.
Teaming up with Generative AI and RAG, we've developed a cutting-edge solution to streamline telecom network performance analysis. Our innovation provides real-time insights, empowering engineers to resolve issues swiftly and boost network efficiency. Stay tuned for more updates on how our project is making waves in the industry!
This project focuses on the detection of credit card fraud using various data science and machine learning techniques. The dataset includes a record of credit card transactions over a specific period, with the goal of accurately identifying fraudulent activities. 🚀✨
The objective of the project is to detect anomalies in credit card transactions. More precisely, given the data on time, amount and 28 transformed features, our goal is to fit a probability distribution based on authentic transactions, and then use it to correctly identify a new transaction as authentic or fraudulent.
This repository contains a Python notebook that demonstrates the use of the Mean Shift clustering algorithm for image segmentation. Mean Shift is a non-parametric clustering algorithm widely used in computer vision tasks.
Explore anomaly detection methods using the Isolation Forest approach in this GitHub project. Learn preprocessing techniques like one-hot encoding and timestamp conversion to enhance data analysis. Apply the algorithm to identify anomalies effectively. Adapt these insights to your own projects.