Judging the effectiveness of a machine learning model requires in-depth analysis. This quick liveProject builds on the work you have completed in Machine Learning for Classification. You’ll assess your early models and consider better alternatives. You’ll plot the ROC curves of the model and compare it to multiple dummy models, and tune your hyperparameters to deliver the most accurate results possible.
This project is designed for learning purposes and is not a complete, production-ready application or solution.
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project author
Harshit Tyagi
Harshit Tyagi has helped over a thousand students master the fundamentals of programming and data science. In his roles at OpenClassrooms and Coding Ninjas, he leverages his technical expertise to conduct workshops and help students bring their course projects to the finish line. He also has a YouTube channel, where he covers fundamental concepts in data science and Python, interview tips, and more. In addition to focusing on data science education, Harshit has developed data processing algorithms with research scientists at Yale, MIT and UCLA.
prerequisites
This liveProject is for Python data scientists who want to expand their capabilities in evaluating and tuning machine learning models. To begin this liveProject you will need to be familiar with:
TOOLS
Basic Python
Basic pandas
Basic NumPy
Basic Matplotlib
Basic seaborn
Basic scikit-learn
Basic Jupyter Notebook
TECHNIQUES
Basics of machine learning
Basics of exploratory analysis
features
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