utkarsh-21st/dimensionality-reduction-using-auto-encoder

Perform Dimensionality Reduction using AutoEncoder.

Jupyter Notebook

Dimensionality Reduction Using AutoEncoder

See: notebook.ipynb

Used XGBosst for classification and keras for building an AutoEncoder
Link to the dataset: https://drive.google.com/file/d/1Nl-RU5HggCSWWqINN4gbxHAVzER6T2Po/view?usp=sharing

Result on test data:

with original features (dimensions=28), ROC AUC Score = 0.8430741138771947
with low dimensional features (dimensions=14), ROC AUC Score = 0.736416256396276
Pipeline: original features -> Robust scaler -> low dimensional features -> DMatrix -> XGBoost

Info

notebook.ipynb - contains full code
scaler - pickle file, SkLearn Robust Scaler
saved_encoder - Tensorflow saved model, encoder used to transform features to low dimensional space
clf_red - pickle file, XGBoost classification model, accept low dimensional features as input
clf - pickle file, XGBoost classification model, accept original features as input
saved_model - Tensorflow saved model, Autoencoder, used to train encoder

Load pickle file using python pickle module

Load TensorFlow model using, tensorflow.keras.models.load_model function