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A Complete Guide for Processing of Data

Introduction

Data Processing happens when data is gathered and converted into usable data. Typically performed by data scientists or groups of data scientists, it is significant for information preparing to be done accurately as not to contrarily influence the finished result or information yield.  

Six Data Processing Steps:

Six steps of data processing:

  1. Data collection: Information from accessible sources, including information lakes and data warehouses, are collected from different processes.
  2. Data preparation: The main reason for this step is to reduce the redundant data (incomplete data or incorrect data) so that we can create a good quality of data for different business purposes.
  3. Data input: After preparation of data this data is converted into a language that can be easily understandable and data can be made usable.
  4. Processing: Processing of data is done by using machine learning algorithms for the manipulation of data so that information or pattern is identified.
  5. Interpretation of data: At this stage, data is being interpreted for final use by the non-data scientist. This stage provides the output of data processing. 
  6. Data storage: All the processed data is then stored for future use.

Preprocessing of data

As we know, approximately 80% of real-world data is unstructured or unorganized. These data are mostly inconsistent, lacks similar behaviour or pattern, incomplete and contain many errors.

Preprocessing of data is a well-known data mining technique that converts raw or unstructured data into a meaningful or understandable format. Data preprocessing is a basic unit of meaningful data analysis. It is one of the most important stages of machine learning projects. Data preprocessing is mostly used in database-driven applications.

In data preprocessing, data passes through a series of steps:

Guide for Processing of Data

Read: Job Description & All Key Responsibilities of a Data Scientist
  • Data cleaning: Real-world data contains irrelevant, duplicate and missing parts. For this phase, data cleaning is performed. Data cleaning involves handling of missing data by ignoring the missing tuples and filling the missing values. For cleaning noisy data different machine learning methods are used like clustering or regression.
  • Data Transformation: Data transformation is used to convert real-world data into an understandable format. It is the most important process of data preprocessing.
  • Data Reduction: It is used to handle large amounts of data. Working with large amounts of data, analysis becomes difficult. For this, we use different data reduction techniques like dimensionality reduction or data cube aggregation.

Data Standardization

Data Standardization is information preparing the work process those changes over the structure of dissimilar datasets into a Common Data Format. As a component of the Data Preparation field, Data Standardization manages the change of datasets after the information is pulled from source frameworks and before it's stacked into target frameworks. Hence, Data Standardization can likewise be thought of as the change rules motor in Data Exchange tasks. 

Data Standardization empowers the information customer to investigate and utilize information in a reliable way. Ordinarily, when information is made and put away in the source framework, it's organized with a certain goal in mind that is regularly obscure to the information customer.

Data Normalization

The need for data normalization is required when we are dealing with attributes on different scales. 

Data normalization is used for mapping data attributes so that it falls under the lower range. At the point when various qualities are there yet characteristics have values on various scales, this may prompt poor information models while performing information mining tasks. So they are standardized to welcome all the traits on a similar scale.

Data Cleaning:

Data Cleaning is a process by which it guarantees that your information is right, reliable and useable. Cleaned data is more important than using sophisticated algorithms because even simple algorithms can show amazing results on clean data.

It involves two steps:

  • We exclude unwanted data such as duplicate and irrelevant data.
  • Errors such as measurement errors, data transfer error and many more of this type are fixed during the data cleaning process.

Missing Value in Data:

The idea of missing qualities is critical to understand to effectively oversee information. On the off chance that the missing qualities are not taken care of appropriately by the analyst, at that point he/she may wind up drawing an off-base derivation about the information. Because of ill-advised taking care of, the outcome got by the scientist will contrast from ones where the missing qualities are available.

Read: An Easy to Understand the Definition of the Confidence Interval

Randomly missing values is of two types:

  • MCAR: Missing completely at random: This structure exists when the missing qualities are arbitrary across all observations. This structure can be affirmed by dividing the information into two sections: one set containing the missing qualities, and the other containing the non-missing qualities.
  • MAR: Missing at random: In MAR, the missing values are not distributed randomly across observations but are distributed with one or more samples.

Imputation:

In statistics, imputation plays a major role. Imputation involves the replacement of missing data with arbitrary values. The leads to three major problems in statistics:

  1. Lots of biasing in data occur in missing data because of imputation.
  2. Due to the addition of arbitrary values analysis and handling of data are more difficult.
  3. Imputation creates reductions in inefficiency. 

Outliers in data mining. 

An outlier is an object digresses essentially from the remainder of the object. The occurrence of an outlier is caused by measurement or execution error and the process of analyzing outlier data is referred to as outlier analysis or outlier mining. 

What is the need for outlier analysis?

Many of the data mining methods do not usually focus on outliers but some applications such as fraud detections can be more interesting by using outliers.

Read: An Introduction to the Recurrent Neural Networks

Detecting Outlier:

Threshold value must be initialized before the detection of outliers such distance of any data point is greater than the distance from its nearest cluster identifies it as an outlier.

Steps:

  • The mean of each cluster is calculated,
  • A threshold value is initialized.
  • Distance between test data and each cluster mean is calculated.
  • Nearest cluster to the test data is identified
  • If (Distance > Threshold) then, it is said to be an outlier. And this value is further processed for outlier analysis.

Conclusion

In today's world of so much of economic exchanges and so much of change in science and technology, now a day’s companies change their working style to remain in the competition. As a large amount of data is present in the world, we need equipped ways to handle this data, this large amount of data is known as 'Big Data'. For this Big Data, we need proper processing of data so that the data can be used for various business purposes. So, data must be explored properly and must be used properly to understand the meaning and to identify the relationship between the data and data models to explain their behaviour. We need to have the right processing method to analyze data for the best results. 

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