When creating an interactive model, a process known as data wrangling is used. To put it another way, it is used to convert raw data into a format that is suitable for data consumption. Data munging is another name for this method.
The process of cleansing and integrating chaotic and complicated data sets for easy access and analysis is known as data wrangling. With the amount of data and data sources continually rising and expanding, it is becoming increasingly important to organise vast amounts of data for analysis.
Data wrangling entails converting data into multiple formats and analysing it so that it can be combined with other data to produce significant insights. Data gathering, data visualisation, and statistical model training for prediction are also included.
The goal of data cleaning is to eliminate erroneous data from your data set. Data-wrangling, on the other hand, focuses on modifying the data format by converting "raw" data into a more useable format.
Prior to data wrangling, data preprocessing is done. Data Preprocessing data is prepared directly after the data is received from the data source in this situation. Data cleansing and aggregation are conducted during the early transformations.