Build Anomaly Detection with Isolation Forest in Python | Machine Learning Fraud Detection Project

Learn how to build a real-world anomaly detection system using Isolation Forest in Python.

In this tutorial, I walk through a complete end-to-end machine learning pipeline for detecting fraudulent and abnormal transactions using realistic financial data. You’ll learn how Isolation Forest works, why it is effective for unsupervised anomaly detection, and how to handle real-world challenges like missing values, skewed distributions, imbalanced data, and noisy labels.

🚀 In this video, you’ll learn:

Isolation Forest explained visually
How anomaly detection works in practice
Data preprocessing pipelines with Scikit-learn
Handling missing values and outliers
Feature engineering for fraud detection
Building an end-to-end ML pipeline
Anomaly scoring and interpretation
Error analysis and model evaluation
Precision, recall, and imbalanced datasets
Production ML considerations and monitoring

📌 Technologies Used:

Python
Pandas
NumPy
Scikit-learn
Matplotlib

This tutorial is designed for machine learning engineers, data scientists, AI developers, and anyone interested in production-ready anomaly detection systems.
00:00 Introduction & Project Overview
00:24 Dataset Loading & Initial Exploration
02:10 Understanding Feature Distributions
04:35 Detecting Skewness in Transaction Data
06:12 Analyzing Missing Values
08:30 Handling Data Quality Issues
10:54 Building the Preprocessing Pipeline
11:20 Median Imputation with SimpleImputer
12:45 Scaling Outliers using RobustScaler
14:05 Encoding Categorical Features with OneHotEncoder
16:45 Training the Isolation Forest Model
18:30 How Isolation Forest Detects Fraud Anomalies
20:05 Configuring Contamination & Model Parameters
21:30 Generating Anomaly Predictions
22:19 Evaluating Model Performance
23:40 Understanding Precision, Recall & F1-Score
25:15 Why Accuracy Fails on Imbalanced Fraud Data
26:32 Inspecting Top Fraud Anomaly Scores
27:40 Extreme Outliers vs Behavioral Anomalies
28:49 Final Thoughts & Production Deployment Ideas
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Видео Build Anomaly Detection with Isolation Forest in Python | Machine Learning Fraud Detection Project канала Epython Lab