The data preparation stage of the data mining process is crucial. It describes the processes of preparing data for analysis by cleansing, converting, and integrating it. The purpose of data preprocessing is to enhance the data’s quality and suitability for the particular data mining process operation.

The following are typical steps include

Data preprocessing is a crucial phase in the data mining process that entails preparing raw data for analysis by cleaning and converting it. The following are typical steps in data  mining process:

Data cleaning

 is locating and fixing mistakes or discrepancies in the data, such as missing numbers, outliers, and duplicates. Data cleaning can be done using a variety of methods, including imputation, removal, and transformation.

Data integration

 is the process of merging information from various sources to produce a single dataset. Data integration can be difficult because it calls for handling data with various forms, semantics, and formats. Data integration can be accomplished using methods like record linkage and data fusion.

Data transformation

 entails putting the data in a format that will allow for analysis. Normalization, standardization, and discretization are common data transformation procedures. While standardization transforms data to have a zero mean and unit variance, normalization scales data to a common range. Continuous data is discretized into discrete categories using this technique.

Data reduction

 entails shrinking the dataset while keeping the crucial details intact. Techniques like feature extraction and feature selection can be used to reduce the amount of data. While feature extraction entails translating the data into a lower-dimensional space while keeping the crucial information, feature selection requires choosing a subset of pertinent characteristics from the dataset.

Discretizing data

entails breaking up continuous data into discrete periods or groups. Data mining process and machine learning methods that need categorical data frequently use discretization. Techniques like equal width binning, equal frequency binning, and clustering can be used to accomplish discrepancy.

Data normalization

 is scaling the data to a standard range, as between 0 and 1 or -1 and 1. When dealing with data that has diverse scales and units, normalization is frequently utilized. Min-Max normalization, z-score normalization, and decimal scaling are examples of common normalization methods.

 

The accuracy of the analytic results and the quality of the data are both critically dependent on data preprocessing. Depending on the type of data being processed and the objectives of the study, different procedures may be required. These procedures increase the effectiveness of data mining process and improve the precision of the findings

 Data Mining process

Data preprocessing is a data mining process approach that is used to turn the raw data into a format that is both practical and effective. 

Process Steps in Data Preprocessing: 

Data cleaning:

The data may contain a lot of useless and missing information. Data cleansing is completed to handle this portion. It entails dealing with erroneous data, noisy data, etc.

Missing Data:

This circumstance occurs when there are gaps in the data. It can be dealt with in a number of ways. 

Among them are:

Ignore the tuples:

This strategy only works when our dataset is sizable and a tuple has numerous missing 

Fill the missing values

There are several methods to complete this work, so fill in the missing values. You can opt to manually fill in   the missing values, use the attribute mean, or use the value that is most likely. 

Noisy Data:

 Machines are unable to understand noisy data, which is meaningless information. It may be produced as a result of poor data gathering, incorrect data entry, etc.

Binning Method

The binning method employs sorted data to smooth out the data. Before the task is completed, the entire set of data is divided into equal-sized chunks using a number of ways. The segments are addressed separately. The operation can be completed by using boundary values or by substituting the segment’s mean for all of the data mining process.

Regression:

 In this case, smoothing the data involves fitting it to a regression function. It is possible to apply multiple or linear regression, depending on the number of independent variables.

Clustering:

This method creates groupings of related data. The outliers might not be noticed or they might be outside of the clusters.

Data Transformation:

 This stage is used to change the data into a format that is appropriate for the data  mining process. This entails the following: 

Normalization

is the process of scaling data values to fall inside a given range (for example, -1.0 to 1.0 or 0.0 to 1.0). 

Selection of Attributes: 

To aid the data mining process, new attributes are created from the existing set of attributes in this technique. 

Discretization: 

This process substitutes interval levels or conceptual levels for the raw values of a numerical attribute. 

Concept Hierarchy Generation:

 Here, qualities are raised in the hierarchy from a lower level to a higher level. For instance, it is possible to change the attribute “city” to “country”. 

Data Reduction: 

A vital step in the data mining process, data reduction entails shrinking the dataset while maintaining the valuable information. This is done to increase the effectiveness of data analysis and prevent the model from being overfit. The following are some typical steps in data reduction:

 

The process of selecting relevant features from a dataset entails choosing a subset of those features. It is common practice to perform feature selection in order to eliminate redundant or pointless features from a dataset. Different methods, including principal component analysis (PCA), mutual information, and correlation analysis, can be used to do this

Feature extraction

 entails reducing the dimensions of the data mining process while maintaining the most crucial details. When the original characteristics are complicated and high dimensional, feature extraction is frequently applied. Techniques like PCA, linear discriminant analysis (LDA), and non-negative matrix factorization (NMF) can be used to accomplish this.

Sampling 

entails choosing a portion of the dataset’s data points. Sampling is frequently used to shrink the dataset while keeping the crucial facts. Techniques including stratified sampling, systematic sampling, and random sampling can be used.