Set in the Harry Potter universe, this project tasks participants with creating a classifier to substitute the Sorting Hat using logistic regression. It's an exercise in data analysis, visualization, and logistic regression in a multi-classification setting.
- Python with Libraries: NumPy, Matplotlib, and Sklearn.
- Class:
logistic_regressionwith methods for initialization, data splitting, standardization, model training (gradient descent or stochastic gradient descent), loss calculation, plotting loss, and model parameter handling.
- Initialization: The
initcommand must be executed before running tests to set up necessary directories. - Test Scripts: Include data description, histogram, pair plot, scatter plot, logistic regression training, and prediction.
- Clean-up Command: Removes all generated files and directories.
- Learning Focus: Practical experience in machine learning, with an emphasis on logistic regression and data visualization.
- Evaluation: Classifier performance measured by accuracy, targeting at least 98% precision.
This project uniquely combines theoretical and practical learning in an engaging context, fostering deep engagement with machine learning concepts. So no PR will be accepted.
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Create a Virtual Environment: To ensure a clean workspace with specific library versions, it is recommended to set up a Python virtual environment. This can be done using the following commands:
python -m venv venv source venv/bin/activate # On Windows use `venv\Scripts\activate`
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Initialization: Before running any tests or scripts, it's crucial to execute the
make initcommand. This sets up the necessary directories for the project.make init
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Executing Tests: After initialization, use the provided test commands in the Makefile (e.g.,
make test-1,make test-2, etc.) to run different parts of the project and validate its functionality.