What Is Data Preparation?
Data preparation refers to collecting, cleaning, labelling, exploring, and visualizing raw data to make it suitable for processing and analysis. The key steps include collecting raw data, cleaning it, labelling it, analyzing it, and visualizing it in a suitable form for machine learning algorithms (ML). Data preparation can take as much as 80% of an ML project. Specialized data preparation tools are recommended to optimize this process.
Data Preparation ArchitectureData Preparation process is an essential part of Data Science. It includes two concepts: 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 The Need For Data Preparation?
For achieving better results from the applied model in Machine Learning and Deep Learning projects, the data format has to be in a proper manner; this is where the term Data Preparation is used. Some specified Machine Learning and Deep Learning models need information in a prescribed format; for example, the Random Forest algorithm does not support null values; therefore, to execute a random forest algorithm, null values must be managed from the original raw data set. Another aspect of Data Preparation and analysis is that the data set should be formatted so that more than one Machine Learning and Deep Learning algorithm is executed in one data set, and the best out of them is chosen.
What Is Data Preprocessing?
Data preprocessing is a crucial technique that transforms raw data into a clean and organized dataset. When data is collected from various sources, it is often in a basic format that is not suitable for analysis. To overcome this, specific steps are taken to convert the data into a concise and refined dataset. The steps involved include:1. Data Cleaning
2. Data Integration
3. Data Transformation
4. Data Reduction
Data transformation is the process of converting data from one format, such as a database file, XML document or Excel spreadsheet, into another.
What Are The Challenges Of Data Preprocessing?
Data preprocessing is essential due to unstructured data from the real world. Real-world data is primarily comprised of the following:
1. Inaccurate data
There are many reasons for missing data, such as data not being continuously collected, a mistake in data entry, technical problems with biometrics, and much more, which requires proper Data Preparation.
2. The presence of noisy data
The reasons for noisy data could be a technological problem of a gadget that gathers data, a human mistake during data entry, and much more.
3. Inconsistent data
The presence of inconsistencies is due to reasons such as duplication within data, human data entry, mistakes in codes or names, i.e., violation of data constraints, and much more necessary data preparation and analysis.
A data center migration is the process of moving select assets from one data center environment to another. It is also referred to as a data center relocation.
How Data Preprocessing Performed?
Data Preprocessing is carried out to remove the cause of unformatted real-world data, which we discussed above. First, explain how missing data can be handled during Data Preparation. Three different steps can be executed, which are given below -
1. Ignoring the missing record
While ignoring the disappeared record is a simple and effective method for handling missing data, it may not be suitable when there is a large number of missing values or when the data pattern is closely linked to the underlying cause of the problem.
2. Filling the missing values manually
However, this method has one drawback: when dealing with large datasets or when the missing values are substantial, it can become a time-consuming and inefficient approach.
3. Filling using computed values
The missing values can also be occupied by calculating the observed given values' mean, mode, or median. Another method could be the predictive values in the preprocessing of data computed using machine learning or Deep Learning tools and algorithms. However, one drawback of this approach is that it can generate bias within the data as the calculated values are inaccurate concerning the observed values.
Let's move further and discuss how we can deal with noisy data. These are the famous methods that can be followed for data preprocessing and analysis:
Preprocessing in Clustering
1. Data Binning
Data binning, or data bucketing, is a data pre-processing procedure that reduces the effects of little observation errors. The actual data values that fall into a given small interval, a bin, are replaced by a value representative of that interval, often the mean or median. This method is also known as local smoothing. There are two types of binning:
Equal width binning, Equal frequency binning.
2. Preprocessing in ClusteringIn the approach, the outliers may be detected by grouping similar data in the same group, i.e., in the same cluster.
3. Machine LearningA machine learning algorithm can be executed to smooth data during preprocessing. For example, a Regression Algorithm can file data using a specified linear function.
4. Removing manuallyThe noisy data can be deleted manually by human beings, but it is a time-consuming data preparation process, so this method is not given priority. To deal with the inconsistent data manually and perform Data Preparation and analysis properly, the data is managed using external references and knowledge engineering tools like the knowledge engineering process.
What Are The Best Data Preprocessing Tools?
1. R: R is a framework comprising various packages that can be used for Data Preprocessing, like dplyr, etc.
2. Weka: Weka is a software that contains a collection of Machine Learning algorithms for the Data Mining process. It consists of Preprocessing tools that are used before applying Machine Learning algorithms.
3. RapidMiner: RapidMiner is an open-source Predictive Analytics Platform for the Data Mining process. It provides efficient tools for performing the exact Data Preprocessing process.
4. Python: Python is a programming language that provides various libraries for Preprocessing.
How Can XenonStack Help To Know More About Data Preprocessing?Here is a quick video to help you get a summary of what we have discussed.Developing a Data Preparation analytic model using Machine Learning and Deep Learning is complex. Data must be prepared, which takes 70 percent of the pipeline. Data Preprocessing and Data Wrangling are necessary methods for Data Preparation. They are used mainly by Data scientists to improve the performance of the Data Preparation and analysis model.
Data Cleansing SolutionsXenonStack offers powerful Data Cleaning with Enterprise Data Quality. Powerful, Reliable, and easy-to-use Data Quality Management Solutions with Data Profiling, Data Discovery, Data Migration, Data Enrichment, and Data Synchronization.
Data Preparation SolutionsTransform into a Data-Driven Enterprise with self-service Data Preparation. Use Machine Learning guides to identify errors in your data set. Data Preparation is a service on Public, Private, or Hybrid Cloud. Run Big Data Preparation for Real-Time Insights with Apache Spark.
XenonStack Knowledge Discovery Services helps you understand data and gather maximum information with Pattern Detection using Data Mining, Data Mapping, and Clustering.