Data Preparation, Preprocessing and Wrangling Tools

Deep learning and Machine learning are becoming more and more important in today’s ERP (Enterprise Resource Planning). During the process of building the analytical model using Deep Learning or Machine Learning the data set is collected from various sources such as a file, database, sensors and much more.

But, the collected data cannot be used directly for performing the analysis process. Therefore, to solve this problem Data Preparation is done. It includes two techniques that are listed below -

Data Preparation Architecture

Data Preparation is an important part of Data Science. It includes two concepts such as Data Cleaning and Feature Engineering. These two are compulsory for achieving better accuracy and performance in the Machine Learning and Deep Learning projects.

What is Data Preprocessing?

Data Preprocessing is a technique that is used to convert the raw data into a clean data set. In other words, whenever the data is gathered from different sources it is collected in raw format which is not feasible for the analysis.

Therefore, certain steps are executed to convert the data into a small clean data set. This technique is performed before the execution of the Iterative Analysis. The set of steps is known as Data Preprocessing. It includes -

Data Cleaning
Data Integration
Data Transformation
Data Reduction

The need for Data Preparation and Preprocessing

For achieving better results from the applied model in Machine Learning and Deep Learning projects the format of the data has to be in a proper manner. Some specified Machine Learning and Deep Learning model need information in a specified format, for example, Random Forest algorithm does not support null values, therefore to execute random forest algorithm null values has to be managed from the original raw data set.

Another aspect is that the data set should be formatted in such a way that more than one Machine Learning and Deep Learning algorithms are executed in one data set, and the best out of them is chosen.

What Is Data Wrangling?

Data Wrangling is a technique that is executed at the time of making an interactive model. In other words, it is used to convert the raw data into a format that is convenient for the consumption of data.

This technique is also known as Data Munging. This method also follows certain steps such as after extracting the data from different data sources, sorting of data using certain algorithms is performed, decompose the data into a different structured format and finally store the data into another database.

Need for Data Wrangling

Data Wrangling is an important aspect of implementing the model. Therefore, data is converted to the proper feasible format before applying any model to it. By performing filtering, grouping and selecting appropriate data accuracy and performance of the model could be increased.

Another concept is that when time-series data has to be handled every algorithm is executed with different aspects. Therefore Data Wrangling is used to convert the time series data into the required format of the applied model. In simple words, the complex data is transformed into a usable format for performing analysis on it.