Autoencoder Anomaly Detection in Python | Iris Dataset Tutorial

Learn how to build a powerful unsupervised anomaly detection system using autoencoders in Python, applied to the famous Iris dataset. This hands-on tutorial walks you through the full process—data preprocessing, training, thresholding, and evaluation—with clear, professional explanations suitable for students, engineers, and developers alike.

💡 Ideal for industrial use cases like fault detection, predictive maintenance, and sensor-based anomaly tracking.

🔧 What you'll learn:
– Installing the required ML libraries
– Loading and preparing the Iris dataset
– Scaling and labeling normal vs anomaly
– Building a lightweight autoencoder with TensorFlow/Keras
– Training the model only on “normal” data
– Setting a 3σ statistical threshold
– Visualizing reconstruction error to detect anomalies
– Evaluating with a confusion matrix and classification report

🛠️ Tools Used:
– Python
– TensorFlow/Keras
– scikit-learn
– Matplotlib & Pandas

🔗 Watch the full video to learn how to adapt this model for real-world anomaly detection systems in manufacturing, IoT, cybersecurity, and more.

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