Working with data science and machine learning
ML is all about working with data. The quality of the training data is crucial to the success of an ML model. High-quality data leads to a more accurate ML model and the right prediction.
Data often has multiple issues, such as missing values, noise, bias, outliers, and so on. Exploring the data makes us aware of this, providing us with necessary information on data quality and cleanliness, interesting patterns in the data, and likely paths forward once you start modeling. Data science includes data collection, data preparation, analysis, preprocessing, and feature engineering.
Data preparation is the first step in building an ML model. It is time consuming and constitutes up to 80% of the time spent on ML development. Data preparation has always been considered tedious and resource intensive due to the inherent nature of data being “dirty” and not ready for ML in its raw form. “Dirty” data could include...
