How do you clean multi-modal data for ML?

1 Identify the data sources

The first step is to identify the data sources and their characteristics, such as the type, size, format, and metadata of each modality. You also need to understand the purpose and context of the data collection, such as the target domain, task, and audience. This will help you to define the scope and requirements of the data cleaning process, such as the level of detail, accuracy, and consistency needed.

2 Preprocess the data

The second step of any data preprocessing is to tailor it to the specific needs and challenges of each modality. For example, text may need to be tokenized, normalized, and have stop words removed. Images may require resizing, cropping, rotating, flipping, or enhancing. Audio may require sampling, trimming, normalizing, filtering, and transcribing. Video can be extracted into frames and trimmed or stabilized. Sensor data can be calibrated, filtered, smoothed, and resampled. To perform these tasks you can utilize tools and libraries such as pandas , numpy , scikit-learn , nltk , opencv , ffmpeg , or librosa .

3 Align the data

The third step of data integration is to align data across different modalities. This may involve matching, synchronizing, and fusing the data. Matching requires finding corresponding pairs or groups of data from different modalities based on criteria such as timestamps, filenames, labels, etc. Synchronizing adjusts the data to have same or compatible temporal or spatial resolution, such as sampling rate, frame rate, aspect ratio, etc. Fusing combines the data from different modalities into a single representation, such as a feature vector, a matrix, or a tensor. Various methods and techniques can be used to perform these substeps, such as join , merge , concatenate , interpolate , resample , encode , and embed . Aligning the data across modalities is essential for analysis and integration.

4 Clean the data

The fourth step in the ML process is to clean the data from any errors, outliers, missing values, duplicates, or noise that may affect the quality and performance of the ML models. This may involve detecting the data that are problematic or suspicious based on some criteria, such as statistics, rules, thresholds, models, etc. Then you need to decide how to handle the data - whether to remove, replace, impute, or correct it. Finally, you should validate the data for any remaining or new issues such as consistency or accuracy. Various methods and techniques can be used for these substeps including describe , isnull , dropna , fillna , replace , outlier_detection , and data_validation . Cleaning your data is an important part of creating successful ML models and ensuring they perform optimally.

6 Transform the data

The sixth step in ML model preparation is transforming the data to make them suitable and ready for use. This may include splitting the data into training, validation, and test sets; adjusting the data to have the same or similar range or scale; converting the data to have the appropriate format or type; and selecting the data that are relevant or important for the ML models. You can use various tools and libraries such as train_test_split , StandardScaler , OneHotEncoder , and SelectKBest to perform these substeps.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?