What are the most important steps in data preparation for an ML project?

3 Transform the data

In the third step of data preparation, you can apply various techniques or operations to transform the data in order to enhance features, reduce dimensionality, or normalize scale. For example, encoding categorical variables can be done using one-hot encoding, label encoding, or ordinal encoding, depending on the type and number of categories. Similarly, numerical variables can be scaled using standardization, normalization, or min-max scaling, depending on the scale and variance of the data. Additionally, feature engineering can create, select, or extract new features from existing data based on domain knowledge and relevance. Lastly, feature reduction using methods such as PCA, LDA, or feature selection can reduce the number of features based on correlation and importance of the data.

4 Split the data

The fourth step in data preparation is to split the data into training, validation, and test sets. This will help you avoid overfitting, underfitting, or bias in your ML model. The training set is used to train the model, the validation set is used to tune the hyperparameters, and the test set is used to evaluate the final performance. You should also ensure that the data is balanced and representative of the population, and use stratified sampling or cross-validation methods to preserve the distribution and proportion of the data.

5 Augment the data

The fifth step in data preparation is to augment the data by generating or synthesizing new data from the existing data. This can help to increase the diversity, quantity, and quality of the data. Data augmentation is particularly useful for image, text, or audio data; you can apply transformations or modifications to create new variations of the data. Examples of common data augmentation techniques include flipping, rotating, cropping, or resizing images; adding noise, distortion, or filters to images or audio; changing the brightness, contrast, or color of images; translating, paraphrasing, or summarizing text; and adding synonyms, antonyms, or spelling errors to text.

6 Document the data

The final step in data preparation is to document the data, recording and explaining the steps, decisions, and results of the process. Doing so will help ensure reproducibility, transparency, and quality of the data, as well as allow for communication and collaboration with other stakeholders. Common data documentation tools include data dictionaries, which provide metadata about each variable in the data set; data lineage diagrams that show the origin, flow, and transformation of the data; and data quality reports that summarize statistics, metrics, and issues related to the quality of the data.