Data Preprocessing in Machine Learning: A Simple Guide

Introduction to Data processing

Data preprocessing refers to the procedures we must follow to alter or encode data so a machine can quickly and readily decode it. The algorithm’s ability to quickly analyze the properties of the data is essential for a model to be accurate and exact in its predictions.

Why Data Processing is Important

Due to their heterogeneous origin, most real-world datasets used for machine learning are likely to contain missing data, inconsistent results, and noise.

Data mining methods would not produce high-quality results when applied to this noisy data because they would be unable to find patterns successfully. Therefore, data processing is crucial to raising the general level of data quality.

  • Missing or duplicate values could present an inaccurate picture of the data’s overall statistics.

  • False predictions are frequently the result of outliers and inconsistent data points disrupting the model’s overall learning process.

Quality data is required for quality judgments. In order to obtain this high-quality data, data preprocessing is crucial; otherwise, it would be a case of garbage-in, garbage-out.

4 Steps in Data Preprocessing in Data science projects

Now let us know the steps involved in the data processing.

Step 1: Cleaning of Data

Data cleaning, which fills in missing values, smoothes noisy data, resolves inconsistencies, and removes outliers, is often done as part of data preprocessing.

  1. Incomplete values

Here are multiple options for resolving this issue:

  • Set the tuples aside.

When a tuple contains a lot of missing values, and the dataset is large, this approach should be taken into account.

  • Add the missing values.

There are numerous ways to accomplish this, including manually entering the data, utilizing regression to forecast the missing values, or using numerical approaches like attribute mean.

  1. Unclean Data

It entails eliminating a random variance or mistake in a measured variable. The following strategies can assist with this:

  • Binning

It is a technique that uses the values of sorted data to smooth out any noise. The data is separated into equal-sized buckets, and each bucket is handled separately. A segment’s mean, median, or border values can be used to replace all the data within it.

  • Regression

The main purpose of this data mining technique is prediction. Fitting all the data points into a regression function aids in reducing noise. If there is just one independent attribute, the linear regression equation is utilized; otherwise, polynomial equations are used.

  • Clustering

Grouping or clustering of data with comparable values. Values outside the cluster can be discarded as noisy data by treating them as such. To learn more about clustering methods, refer to the professional machine learning course in Bangalore

  1. Eliminating anomalies

By using clustering techniques, comparable data points are grouped together. Outliers/inconsistent data are the tuples that are not part of the cluster.

Step 2: Integration of Data

One of the data preprocessing procedures, called data integration, combines data from several sources into a single, larger data storage, such as a data warehouse.

Data integration is necessary, particularly when addressing a real-world problem like identifying nodules from CT scan images. The only alternative is to combine the pictures from several medical nodes to create a bigger database.

While implementing Data Integration as one of the Data Preprocessing processes, we could encounter the following problems:

  • Schema integration and object matching: Data can be provided in various formats and with properties that could make data integration challenging.

  • Removing unused attributes from all sources of data.

  • Finding and resolving contradictions between data values.

Step 3: Data Transformation 

After the data has been cleared, we must combine the high-quality data into new formats by altering the data’s value, structure, or format using the strategies listed below for data transformation.

  • Generalization

We have transformed low-level or granular data into high-level information by employing idea hierarchies. The address’s basic information, such as the city, can be transformed into more sophisticated data like the country.

  • Normalization

It is the most significant and extensively used data transformation method. Depending on the range, the numerical properties are scaled up or down. In this method, we limit our data attribute to a specific container to create a correlation between various data points. There are several approaches to normalizing, which are highlighted here:

  • Normalization using min-max

  • Normalization of Z-Score

  • Normalization of decimal scaling

  • Attribute Picking

In order to aid in the data mining process, new properties of data are formed from already existing qualities. For each tuple, the date of birth data attribute, for instance, can be changed to another property, such as senior citizen, which will directly impact predicting diseases or survival rates, etc.

  • Aggregation

It is a technique for compiling facts into a concise form and displaying it. For instance, sales data can be combined and modified to display in a month-by-month and year-by-year style.

Step 4: Data Reduction

Data analysis and mining algorithms may not be able to process a dataset that is too huge for a data warehouse. One potential option is getting a smaller, reduced representation of the dataset that gives high-quality analytical results.

Here is a guide to some data reduction techniques.

  • Cube accumulation of data

It is a technique for data reduction where the acquired information is expressed in a condensed manner.

  • Dimensionality reduction

Techniques for dimensionality reduction are applied to feature extraction. A dataset’s properties or distinct features are referred to as its dimensions. This method seeks to lessen the number of redundant features that machine learning algorithms consider. Dimensionality reduction can be accomplished using methods like Principal Component Analysis and others.

  • Data Compression

Encoding technologies allow for a large reduction in data size. However, there are two types of compression: lossy and non-lossy. Lossless reduction is when original data can be recovered from compressed data after reconstruction; lossy reduction is when original data cannot be recovered.

Key Takeaway

An overview of all we’ve learned so far about data preparation is provided below:

  • Understanding your data is the first step in the data preprocessing process. You can see what you need to concentrate on just by looking at your dataset.

  • Utilize statistical techniques or ready-made libraries to aid dataset visualization and provide a clear representation of how your data appears in class distribution.

  • Count the number of duplicates, missing values, and outliers in your data to summarize it.

  • Remove any fields that you believe won’t be used in modeling or are closely related to other properties. One of the key components of data preprocessing is dimensionality reduction.


If you want to learn in-depth about these techniques, join the IBM data science course in Bangalore, and practice the skills by working on multiple data science projects. 


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