autoencoder-imputation-xyonix-blog

Exploring the Power of Autoencoders for Data Imputation.

installation

# 1. Create a conda environment with Python 3.10
conda create -n xyonix_autoencoder python=3.10

# 2. Activate the environment
conda activate xyonix_autoencoder

# 3. Install essential Python packages via conda
conda install -c conda-forge jupyter ipykernel numpy pandas joblib scikit-learn matplotlib seaborn

#    Note: The `-c conda-forge` ensures we get up-to-date packages from the conda-forge channel.

# 4. Install TensorFlow
#    - On most Linux/Windows with Python 3.10:
pip install tensorflow

#    - On M1/M2 Macs (Apple Silicon), install these instead:
# pip install tensorflow-macos tensorflow-metal

# 5. Register the environment’s kernel with Jupyter
python -m ipykernel install --user --name xyonix_autoencoder

# 6. Launch Jupyter Notebook
jupyter notebook

Once the Jupyter notebook navigator loads:

1. Select autoencoder_imputation.ipynb to open notebook
2. Select Kernel >> Change kernel >> xyonix_autoencoder from pull-down menu

notebook

The Jupyter Notebook corresponds to the XYONIX article, Filling the Gaps: AI-Powered Data Imputation with Autoencoders on Housing Data. Run the cells in the notebook in order to reproduce the plots shown in the article.