A Quick Guide to Data Preprocessing in Machine Learning

How can you raise the quality of your data to create AI models that are more precise? Learn about the data pretreatment procedures to transform raw data into the processed form.

In the modern world, data has become a valuable asset. But—Can we actually train machine learning algorithms using this vast amount of unprocessed data?

Not quite, I suppose.

Inconsistencies, noise, partial data, missing values, and other tainted characteristics characterize real-world data. By utilizing data warehousing and mining techniques, it is compiled from various sources.

The more data we have, the more accurate models we can train, according to the general rule of thumb in machine learning. The actions that must be taken to transform raw data into processed data are all covered in this article. Here’s a comprehensive data science course in Bangalore, if you are looking for online resources to learn. 

What Does Data Preprocessing Entail?

The actions we must take to alter or encode data so that a machine can quickly and readily parse it are called data preprocessing. The algorithm must be able to quickly analyze the features of the data if a model is to make accurate and exact predictions. 

Importance of Data Preprocessing

Due to their varied origin, most real-world machine-learning datasets are particularly sensitive to missing, inconsistent, and noisy data. 

As a result of their inability to successfully discover patterns, data mining algorithms used for this noisy data will not produce high-quality findings. In order to enhance the overall quality of the data, data processing is crucial. Duplicate or missing values may misrepresent the statistics of the data as a whole. Inconsistent data points and outliers frequently interfere with the model's general learning process, producing inaccurate predictions. 

Good data is required for good decisions. Without data preprocessing, the situation would simply be a "Garbage In, Garbage Out."

Features of Machine Learning

Features in our ML model are single independent variables that act as inputs. They can be viewed as representations of the data or attributes that aid the models' class/label prediction.

Features, for instance, in a structured dataset like in a CSV format refer to each column representing a quantifiable piece of data that may be utilized for analysis, such as Name, Age, Sex, Fare, and so on.

Data Preprocessing in 4 Steps 

Let's go over the four primary stages of preprocessing data in greater detail now.

Clearing Data

Data cleaning specifically includes filling in missing values, removing outliers, smoothing noisy data, and resolving inconsistent data as part of data preparation.

1. Missing values

Below are a few approaches to resolving this problem:

• Leave those tuples alone.

When the dataset is large and a tuple has a lot of missing values, this method should be considered.

• Fill the missing values

This can be done in various ways, including manually entering the numbers, using regression to anticipate the missing values, or using numerical techniques like attribute means.

Noisy Data

A random mistake or variance of a measured variable must be eliminated. The following methods can assist in accomplishing it:

• Binning

The method smoothes out any noise in the sorted data values by applying the methodology. Each bucket or bin of the data is handled separately after being separated into equal-sized buckets. A segment's mean, median, or border values can substitute for all of the segment's data.

• Regression

Usually used for prediction, this data mining approach. Noise can be slowed down by including every data point in a regression function. If there is just one independent attribute, the formula for linear regression is applied; otherwise, polynomial equations are applied.

• Clustering

assembling clusters or groups from data with related values. It is possible to treat the numbers that don't fit into the cluster as noisy data and to discard them. For a detailed explanation, refer to an online data science course in Pune, designed in collaboration with IBM. 

Removing Outliers 

Techniques for clustering combine data elements that are similar. Outliers/inconsistent data are tuples that don't belong in the cluster.

Data Integration 

Data Integration is one of the data preparation procedures used to combine the data from several sources into a single, more substantial data storage, such as a data warehouse.

When trying to solve a real-world issue, like recognizing the presence of nodules from CT Scan images, data integration is absolutely essential. The only solution is to combine the photos from different medical nodes to create a bigger database.

When using Data Integration as a single process in data preprocessing, we could encounter the following problems:

• Schema integration & object matching: The data may be present in various formats and with attributes that make it challenging to integrate.
• From all data sources, removing duplicated attributes. 
• Conflicting data values are discovered and resolved. 

Data Transformation

Following the completion of data clearing, it is necessary to combine high-quality data into new formats by altering the value, structure, or format of the data using the methodologies listed below. 

Generalization

Concept hierarchies have been used to transform low-level or granular data into high-level information. The basic information in an address, such as the city, can be transformed into more advanced data, such as the nation.

Normalization

The most significant and extensively used data transformation method is this one. The numerical properties are scaled up or down to fit within a given range. To create a correlation between various data points, we restrict our data attribute to a specific container in this method. Multiple methods of normalization are highlighted here, including:

the min-max normalization

Norming of Z-Score

normalization of the decimal scale

Attribute Selection

In order to aid in the data mining process, new properties for information are produced from already-existing qualities. For each tuple, the data attribute date of birth, for instance, can be changed to another property, such as is_senior_citizen, which will directly impact the prognosis of illnesses or survival rates, among other things.

Aggregation

It is a way to summarize and present data for storage and display. Data can be changed to appear as per month and year, for instance, after being aggregated and transformed for sales.

Data Reduction 

Data analysis and data mining techniques may not be able to handle a data warehouse's dataset because of its scale.

One potential option is getting a reduced representation of the dataset with a significantly smaller size to yield high-caliber analytical results.

The different data reduction techniques are described here.

Data cube aggregated

This method of data reduction expresses the acquired data in a summarized manner. 

The feature extraction process employs dimensionality reduction techniques. Dataset attributes or distinct aspects are referred to as their dimensionality. By using this method, we hope to decrease the number of redundant features that machine learning algorithms consider.  Methods such as Principal Component Analysis can be used to do this.

Data Compressions

The data size can be considerably decreased by applying encoding technologies. But there are two types of data compression: lossy and non-lossy. Lossless reduction is used when the original data can be retrieved after being decompressed; lossy reduction is used when it cannot. 

Discretization

The continuous qualities of nature are separated into data with intervals via data discretization. Continuous features frequently have a lower likelihood of correlating with the target variable; hence this is done to account for this. As a result, interpreting the findings can be more difficult. Interpreting groups that match the target is possible after discretizing a variable. Age as a property, for instance, may be discretized into bins such as below 18, between 18 and 44, between 44 and 60, and over 60.

Numerosity Reduction

A regression model or other type of equation can represent the data. Using a model instead of a large dataset would reduce the workload of keeping data.

Subset selection for attributes

The choice of qualities must be made with great care. If not, it might produce high-dimensional data that is challenging to train because of underfitting/overfitting issues. The remainder of the traits can all be disregarded, and only those that are more valuable for model training should be considered.

Assessment of the quality of the data

The statistical procedures one must use to ensure the data is error-free are included in the data quality assessment. Data must be of a high standard because it will be used for operations, customer management, marketing analysis, and decision-making.

The following are the primary elements of data quality assessment:

• The absence of any missing attribute values and completeness
• Information that is accurate and reliable Consistency across all features
• Maintain the accuracy of the data
• Redundancy is not present.

There are three basic steps in the process of data quality assurance:

• Data profiling: This process entails examining the data to spot problems with its accuracy. Once the issues have been analyzed, the data must be summarized so that no duplicates, blank values, etc., are found.
• Data cleaning: Fixing data problems is part of data cleansing.
• Data monitoring entails keeping data in order and regularly assessing if it meets business needs. 

Data preprocessing: Best Tips

The lessons we've learned regarding data preprocessing are briefly summarized below:

Knowing your data is the first step in data preprocessing. You can get a sense of what needs to be your main emphasis by simply glancing through your dataset.

Use pre-built libraries or statistical techniques to assist you in visualizing the dataset and provide a clear picture of how your data appears in terms of class distribution. 

Include a summary of your data, including the proportion of duplicates, missing values, and outliers.

Eliminate any fields you believe will not be used in the modeling or closely related to other attributes. Dimensionality reduction is a crucial component of data preprocessing.

Make some feature engineering calculations to determine which characteristics are most helpful for training the model. You can learn these tips online. Learnbay offers the best data science courses in India covering multiple capstone and real-world projects. You can learn and become a skilled data scientist in just 6 months of practical training.