busradeveci/ml-notebooks
📊 Building ML projects using Python, Scikit-learn & more.
This is a collection of machine learning notebooks that demonstrate how to clean and explore various datasets, then apply different classification, regression, and clustering algorithms to them. It takes raw data from fields like health, marketing, and entertainment, and shows the steps to build and evaluate predictive models. This resource is for data scientists, machine learning engineers, and data analysts who want to learn from practical examples or adapt existing solutions for their own projects.
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Use this if you are a data professional looking for concrete examples of machine learning workflows, from data preparation to model evaluation, across diverse real-world datasets.
Not ideal if you are an end-user seeking a ready-to-use application or a tool that doesn't require coding and data science expertise.
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Jupyter Notebook
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Last pushed
Jun 14, 2025
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