The massive data sets must be cleaned up and transformed into a format that data scientists can use. For improved results, it’s crucial to deal with the redundant data by deleting illogical outliers, corrupted records, missing values, inconsistent formatting, etc.
For data cleaning and analysis, Python modules like Matplotlib, Pandas, Numpy, Keras, and SciPy are frequently used. These libraries are used to load, prepare, and perform efficient analyses on the data. For instance, the “Student” CSV file contains details about the students of a certain institute, including their names, standards, addresses, phone numbers, grades, and other information.