Skip to content Skip to sidebar Skip to footer

Help Center

< All Topics

Data Preprocessing – The Foundation of Data Science Solution

Data preprocessing is preparing raw data for analysis by cleaning, transforming, and organizing it. It’s like preparing ingredients before cooking a recipe. Just as a chef would cut, peel, and season the ingredients before cooking a recipe, a data scientist would preprocess the raw information before analyzing it.

Consider the following practical example:

Suppose you want to build a machine learning model to predict whether a candidate will pass or fail an entrance exam. You collect a dataset that includes information about each student’s age, gender, academic performance, and other parameters that could influence their results.

However, the data you collect might be messy and difficult to analyze in its raw format. For example, some students might have missing information for certain fields, while others might have incomplete, redundant, or inconsistent information. That’s when data preprocessing emerges.

In the data preprocessing phase, you would first clean the data by removing any redundancies, irrelevant or erroneous data. You would then transform the data by filling in the gaps (missing values), normalizing the data, and encoding any categorical values. For example, you might replace gender with a binary variable (0 for male, 1 for female), and normalize age by dividing each student’s age by the maximum age in the dataset.

Once the data is cleaned and transformed, you would then organize it into a format that is analysis-ready by splitting the dataset into training, validation, and test sets. The training set is used to train the machine learning model, while the validation and test sets are used to evaluate the model’s performance.

The importance of data preprocessing in data science

A database is a group of data points, which include events, data samples, records, and observations. Several attributes are used to define a data sample. Data preparation is fundamental to data science because it successfully develops data models using attributes. But while information is gathered, multiple issues arise. For example, you need to gather data on customers who use credit/debit cards for online transactions. Now, data can come from various sources (because customers can use both websites and mobile apps during online transactions), which results in inconsistent data formats like integer and float. This could lead to erroneous data analysis. Thus data preprocessing cleanses, formats, and transforms the data. When data inconsistencies or redundancies that could otherwise exist are removed, the value and accuracy of data increase. Preprocessing the data ensures there aren’t any incorrect or missing values generated or gathered by bugs or human error.

The steps involved in data preprocessing

The following are the common steps in data preprocessing:

1. Data Cleaning

This process involves removing irrelevant, incomplete, or inconsistent data, missing values, redundancies, and outliers from the dataset.

2.     Data Integration

This process involves consolidating data from multiple sources, such as DBMS, spreadsheets, and CSVs into a single dataset for consistency.

3.     Data Transformation

This process involves transforming the data into a suitable format for analysis. This may include scaling, normalizing, or standardizing the data.

4.     Feature Extraction

This process involves selecting the most relevant features from the dataset. This improves the performance of the analysis.

5.     Feature Engineering

This phase involves deriving new features from the original features or combining multiple features to create a new feature. Decision trees and categorical mining techniques are used in this case.

6.     Data Encoding

Data encoding is the final phase of the data preprocessing cycle. Your dataset’s categorical features (columns) are converted into numerical values in this step.

Once these steps are completed, the preprocessed data is ready for analysis, and data scientists can then apply various analytical techniques and algorithms to extract insights and knowledge from the data.


According to a study published in datanami, data scientists spend 45% of their time in the data preprocessing phase. After preprocessing the data, a machine learning algorithm can be applied to the cleaned and transformed data in order to train a predictive model that can make accurate predictions.

Education Nest offers a robust course in data science that includes the fundamentals of data preprocessing. You’ll learn to standardize your data so that it fits in your predictive model, develop new features to make the most of the data in your dataset, and choose the best features to enhance your predictive model. The path to becoming a data scientist may seem difficult, but with the correct guidance, you can master it quickly and tackle challenges in the real world. To start on the path to becoming a data scientist, enroll in Education Nest’s Data Science program and jumpstart your career.

Table of Contents