AsadiAhmad/LDA
Dimension Reduction with LDA
This project helps data scientists and machine learning engineers simplify complex datasets by reducing the number of features or variables. It takes a dataset with many columns (features) as input and outputs a new, smaller dataset that retains the most important information for distinguishing between different classes. This is particularly useful for preparing data for classification tasks.
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Use this if you are a data scientist working with high-dimensional data and need to reduce its complexity while preserving class separability for improved model performance.
Not ideal if your primary goal is to find underlying topics in text data or if you need a dimensionality reduction technique that doesn't rely on class labels.
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Jupyter Notebook
License
MIT
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Last pushed
Jan 06, 2025
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