Hands-on Anomaly Detection using Isolation Forest | Ethereum Fraud Detection | Kaggle Case Study

In this video, we cover hands-on anomaly detection using the Ethereum fraud dataset from Kaggle! 📊 We'll walk you through the entire process, from initial data exploration to visualizing anomalies using t-SNE. 🚀 First, we'll start by loading the dataset and performing essential data preparation steps. This includes checking for duplicate rows, handling missing values, and exploring feature correlations to ensure our data is clean and ready for analysis. We'll also cover proper encoding techniques for categorical features, including one feature that requires target encoding for optimal anomaly detection performance. Once our data is prepped, we'll apply the powerful Isolation Forest algorithm 🌲 and demonstrate how it can effectively detect anomalies in our Ethereum fraud dataset. You'll learn about the intuition behind Isolation Forest and how it works under the hood to identify outliers in high-dimensional data. We'll take our analysis to the next level by visualizing the anomalies using t-SNE (t-distributed Stochastic Neighbor Embedding) for both the training and test datasets. This will provide us with insightful visualizations that can help us understand the distribution of anomalies in our data and gain deeper insights into potential fraudulent activities in the Ethereum network. Whether you're new to anomaly detection or looking to expand your Python data analysis skills, this tutorial has something for you! Happy Learning! 📈✨