Anomaly Detection with Isolation Forests using Python and Scikit-learn

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Welcome to Code Craft! In this episode, we're diving deep into anomaly detection using Isolation Forests. Whether you're spotting fraud, identifying network intrusions, or detecting manufacturing defects, understanding how to isolate anomalies is crucial.

In this video, you'll learn:

- The theory behind Isolation Forests
- How to generate a synthetic dataset with anomalies
- Exploratory Data Analysis (EDA) techniques
- Building and training the Isolation Forest model in Python
- Visualizing anomalies in your dataset

We'll walk you through each step with detailed explanations and code snippets, ensuring you can apply these techniques to your projects. Check out the written blog linked in the description for more details and resources.

Don't forget to like, share, and subscribe for more AI and Data Science tutorials every Sunday!

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I just subscribed right now . I hope you make video of solving different datasets in kaggle, we all will use that as a reference for our Kaggle practice.

aryanverma

Good job
Thanks
Will be good if you start any playlist
Like Complete EDA or
💯 ML like from beginners to advance level with work on Kaggle too

Btajicrew

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