Utah Code Camp 2015
IPython notebooks and supporting files for
a presentation on the Google Prediction API.
The primary result in the presentation is a performance
benchmark of the Google Prediction API against
Key files include:
- ml_with_goog_pred.ipynb: The main presentation.
A brief introduction to machine learning,
an example of predicting Titanic survival using
the Google Prediciton API, and then a comparitive
sklearn.RandomForestClassifierand the Google Prediction API. The benchmark was conducted as part of a data science competition to classify very short Taylor Swift audio clips as either huge hit or not hit.
- sklearn_fft_ave.ipynb: A notebook that details
RamdomForestClassifierbenchmarking solution, including the feature engineering to compute frequency spectra.
- google_prediction_fft_ave.ipynb: A notebook that details the Google Prediction API benchmark solution.
- googleprediction.py: A simple wrapper around the
Google Predication API functions to make it look
more like the
scikit-learnstandard interface to models.
- ipython_fft_example.ipynb: A notebook showing a quick example of computing FFTs in scientific Python. The emphasis is on preserving the physical significance of the resulting frequency spectrum.