End to End Data-Science Project- Microsoft Malware Detection Kaggle - 0.007 LogLoss with XGBoost

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Hi, I am a Machine Learning Engineer | Kaggle Master. Connect with me on 🐦 TWITTER:   / rohanpaul_ai   - for daily in-depth coverage of Machine Learning / LLM / OpenAI / LangChain / Python Intricacies Topics.

Microsoft Malware Detection-BIG 2015 Kaggle Challenge is one of the largest publicly available datasets on Malware Classification (half a Terabyte).

Here I apply a large number of Feature Engineering to extract features from the 500GB dataset of Microsoft Malware Classification Kaggle Competiton and then apply XGBoost to achieve a LogLoss of 0.007.

This competition is a beautiful example of learning how to handle very large data in your Data Science project and extract innovative features from raw files given as input.

👉 Github Repo - https://github.com/rohan-paul/Machine...

👉 Kaggle Notebook - https://www.kaggle.com/paulrohan2020/...

👉 My Blog explaining the project - https://rohan-paul-ai.netlify.app/blo...

👉 Microsoft Malware Classification Kaggle Competiton Dataset - https://www.kaggle.com/c/malware-clas...

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