Summary
In this chapter, we delved into handling signal data, focusing particularly on audio signals. We explored various storage formats for such data and examined libraries for loading, transforming, and visualizing this data type. To develop potent features, we applied a range of signal-processing techniques. Our feature engineering efforts transformed time-series data from each training segment and aggregated features for each test set.
We consolidated all feature engineering processes into a single function, applicable to all training segments and test sets. The transformed features underwent scaling. We then used this prepared data to train a baseline model utilizing the LGBMRegressor algorithm. This model employed cross-validation, and we generated predictions for the test set using the model trained in each fold. Subsequently, we aggregated these predictions to create the submission file. Additionally, we captured and visualized the feature importance for each fold.
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