Anomaly Detection

Practical Anomaly Detection using Python and scikit-learn Introduction Anomaly detection is a critical task in various domains, including finance, healthcare, and cybersecurity. It involves identifying data points, events, or patterns that deviate from the norm within a given dataset. In this article, we will explore how to build an anomaly detection system using Python and scikit-learn. Prerequisites To follow this article, you should have: Familiarity with Python and basic data structures (e.g., lists, dictionaries) Understanding of basic machine learning concepts (e.g., supervised vs. unsupervised learning) Installations: Python, scikit-learn, and relevant libraries (e.g., NumPy, Pandas) Main Sections 1. Data Preparation and Preprocessing Data preparation is a crucial step in anomaly detection. It involves cleaning, transforming, and normalizing the data to make it suitable for analysis. ...

Detecting Anomalies with Machine Learning and Python Introduction Anomaly detection is a critical task in data analysis, enabling the identification of suspicious transactions, credit card inconsistencies, and irregularities in medical records. In this post, we will delve into the practical implementation of anomaly detection using machine learning in Python, focusing on real-world security applications and challenges. Prerequisites To follow along with this tutorial, you will need: A basic understanding of Python and machine learning concepts (e.g., supervised and unsupervised learning) Familiarity with popular Python libraries for machine learning (e.g., scikit-learn, TensorFlow) Access to a Python environment for code execution Preparing the Data Before training a machine learning model, we need to prepare our dataset. This includes selecting relevant data, handling missing values, and scaling numerical features. ...