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Data Preprocessing – The Foundation of Data Science


Data preprocessing is the process of preparing raw data for analysis. The key steps in this process are cleaning, transforming, and organizing data. You need to prepare the ingredients before cooking a recipe. Right? Just like you cut, peel, and season the ingredients before cooking a delicious dish, a data scientist preprocesses the raw information before analyzing it.

Consider the following practical example:

Imagine you’re developing a machine learning model to forecast a candidate’s performance on an entrance test. You create a dataset by gathering information such as student’s age, gender, academic and semester results, etc. You can also combine some other information such as aptitudes, logical reasoning which might be impacting their result.

The problem in this process is that the information can be fragmented, redundant, or missing. Analyzing this raw information is always a challenge for any data scientist. For example, students who never have appeared in an aptitude test will have missing information for that field. Or some students may provide repetitive, inadequate, or conflicting information. Therefore, to solve this problem, data preprocessing becomes necessary.

  • The first step in data preprocessing is data cleaning. In this phase, any redundant, irrelevant, or incorrect data are eliminated from the data set.
  • Data Integration– In this phase, data is integrated from multiple data sources into a single easy-to-understand format.
  • Data transformation – In this process, any missing information in the data set is filled up.
  • Data normalization– You can generalize the age of a student by diving it with the oldest student’s age in the dataset.
  • Data encoding– This process involves assigning categorical values with numeric data. For example, you can categorize gender as 0 for males and 1 for females.

The cleansed and transformed dataset is then divided into different segments for data analysis and machine learning purposes.

  • Training set
  • Validation set
  • Test sets

This would organize the data into a ready-for-analysis format. Your Machine Learning Model uses the training dataset for training purposes. The performance, competency, and accuracy of the machine learning model can be analyzed by using validation and test sets.

Why Data Preprocessing is important?

Data preprocessing is a crucial stage in the data analysis process. Preprocessing data has the following advantages.

  • It strengthens reliability and accuracy. Preprocessing data can increase the correctness and quality of a dataset. As a result, this makes the dataset more dependable. It removes missing or inconsistent data values generated due to human or computer errors.
  • It ensures data consistency. While gathering large volumes of data, data redundancy can happen. Therefore, removing redundant data during preprocessing guarantees that the data is worthy of analysis. It also makes the data consistent. Finally, this results in the production of accurate results.
  • It improves the algorithmic readability of the data. The quality of the data is improved through preprocessing. This also makes it simpler for machine learning algorithms to read, utilize, and analyze the data.

What are the different phases of Data Preprocessing?

1.      Data Cleaning

Data Preprocessing starts with data cleansing. This segregates the data into simple datasets that machine learning algorithms can easily understand. Replacing missing data and improving accuracy are the primary objectives of the data cleaning phase. This process fills in any gaps left by programming glitches or human errors. The following approaches can be used to remove duplicate or irrelevant data.

  • Regression: The process of fitting data into a single or multiple regression function.
  • Binning: The division of data into distinct parts or bins
  • Clustering: Organizing data into groups of comparable data is called clustering.

2.      Data Integration:

Once your data has been cleaned, the integration phase starts. Here data is consolidated from multiple sources such as CSV, DBMS, spreadsheets, etc.

3.      Data Transformation:

Then the consolidated data is converted into acceptable formats which can be quickly and easily read and interpreted by computer programs and machine learning algorithms.

The following methods can be used to integrate and transform your data:

  • Aggregation: Reducing the overall size of your datasets to make them more manageable.
  • Normalization: Removing duplicate values from the data and storing the data correctly.
  • Discretization: This is the process of splitting the dataset’s range of attribute values. The end result is to replace raw values with interval levels.
  • Generalization: Depending on the objectives of your analysis, this process involves moving lower-level data points to higher-level data points.

4.      Feature extraction:

To perform a smooth analysis, feature extraction is done. This extracts and selects the most relevant and existing features in a dataset. This also reduces the total volume of data to be analyzed.

5.      Feature Engineering:

This process derives new features from the already existing features or in certain cases, combines existing features to create a new feature. Popular algorithms to perform this task are decision trees and categorical mining.

6.      Data encoding:

This is the final phase of data preprocessing. This is where the dataset’s categorical features (columns) are converted into numerical values.


EducationNest’s Data Science module is designed to perfection to kick start your data science career. It includes the fundamentals of data preprocessing, and it’s applications. You’ll master how to standardize your data so that it is in the best possible structure for your model, develop new features to make the most of the data in your dataset, and choose the best features to enhance the competency of your model. EducationNest, which is a subsidiary of Sambodhi Research and Communications Pvt Ltd., has designed its courses by industry experts. Therefore, this isn’t just a certification; this is your career insurance.

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