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Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
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Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
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Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
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Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
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8 Ways to Improve Accuracy of Machine Learning Models
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Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
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Different Types of Normalization Techniques

anushkakhatri
anushkakhatri Last Updated : 25 Jul, 2022
6 min read

This article was published as a part of the Data Science Blogathon.

Introduction

In a large dataset, duplicated data leads to resource and disk space waste, and the chance of errors and inconsistencies increases. Therefore, we must decompose redundant data relationships into manageable and more well-structured ones. Through normalization, we can restructure a relational database to reduce redundancy among the data and improve the data integrity, eliminating unwanted characteristics like Insertions, Updates, and Deletions. In this article, we will explain the different types of normalization in detail and explore the advantages and disadvantages of using normalization.

First Normal Form (1NF)

A relation violates the first normal form if it contains composite or multi-valued attributes. Therefore, in order to remove the repetition of values, we convert it into the first normal form. In the first normal form, the attribute values are single-valued and there are no repeating groups. By using the primary key and foreign key, the first normal form creates a functional dependency between the two tables.

Example:

Normalization
Employee Table

The Employee table is not in its first normal form because the column Phone No. contains multiple values.

To convert the Employee table into 1NF we decompose it as follows:

Normalization
Employee Table- 1NF

Second Normal Form (2NF)

In order for a relationship to be in the second normal form, it must first be in the first normal form and should not contain partial dependencies. By using the second normal form the amount of redundant data in the memory is reduced. Those relations not included in the second normal form may suffer anomalies in future updates.

Example:

Normalization

The non-prime attribute AGE in the provided table is reliant on TEACHER ID, a suitable subset of a candidate key. Because of this, it breaks the 2NF rule.

The provided table is divided into two tables to create the second normal form:

Normalization
Normalization

Third Normal Form (3NF)

The relation must first be in the first and second normal form before it can be in the third normal form. The third normal form removes the transitive dependency of the non-primary key attributes on the primary key by moving these attributes along with the copy of the attribute they depend on to a new relation. Removing transitive dependencies of attributes reduces data duplication and improves the integrity of data. In the third normal form, most of the tables are free of anomalies with insertions, updates, and deletions.

Example:

Currently, the following table is not in the third normal form:

Normalization

To decompose the given table into the third normal form we can do the following:

Normalization

Boyce-Codd Normal Form (BCNF)

The Boyce-Codd Normal Form(BCNF) is the fourth form of normalization and is an updated version of the third normal form. The relation first must be in the third normal form to be in BCNF. As per BCNF, if Q is determined by P, then P should be a super key or candidate key for any functional dependency. When we use the third normal form, we can achieve lossless decomposition, but with BCNF, it is very difficult. BCNF is a more restrictive form of normalization, so there are no anomalous results in the database.

Example:

BCNF | Normalization

Think about a relation R that has attributes (student, subject, teacher). Since the teacher is not a candidate key, the subject “teacher” breaches the BCNF.

Divide R into R1 (X, Y) and R2 if X->Y violates the BCNF (R-Y). Consequently, R is split into R1 (Teacher, subject) and R2 (Relation) (student, Teacher).

The dependency preservation property is not always met by BCNF decomposition. If the dependency is not retained following BCNF decomposition, we must choose between staying in BCNF or going back to 3NF.

Fifth Normal Form (5NF)

A relation in a DBMS is in the Fifth Normal Form if it is in the Fourth Normal Form but does not contain any join dependencies. The joining must be lossless. The Fifth Normal Form is satisfied when all the tables present in the DBMS are broken down into as many more tables as possible to reduce redundancy. The Fifth Normal Form is also known as the Project Join Normal Form.

Example:

Mr. Rick takes both C++ and Python classes for Lecture Hall 1, but he does not take Python for Semester 2. For this case, the fields must be combined to identify a valid data set. If we add a new lecture hall as Lecture Hall 3 but do not know what the subject will be, we leave Professor and Subject blank. As all three columns are primary keys we cannot leave the other two columns ( Professor, Subject) blank. As a result, we can decompose the above table into three relations to make 5NF.

Sixth Normal Form (6NF)

In the sixth normal form, the relation variables are decomposed into irreducible components. For a temporal variable or other interval data, this is relatively unimportant for non-temporal relation variables. In many data warehouses, the benefits of the sixth normal form outweigh the downsides.

Example:

For the above, the possible join dependencies are –

(Student ID, First Name)

(Student ID, Last name)

(Student ID, Grade)

So we can decompose it into the sixth normal form as follows:

Advantages of Normalization

Normalization provides the following advantages:

  • Data redundancy can be reduced through normalization.
  • Duplicate data is removed from the database during normalization.
  • In a database, normalization ensures data consistency.
  • The normalization process organizes the database in a better way.
  • The normalization process facilitates faster execution since the database size is reduced.
  • The normalization process involves ensuring that our data is logically stored.
  • A normalized database provides higher security.

Disadvantages of Normalization

The disadvantages of normalization are as follows:

  • Normalization is a long and time-consuming process.
  • You must have the right knowledge of normal forms in order to carry out the normalization process efficiently.
  • An ill-designed database may result from careless decomposition.
  • The lack of duplicate data makes table joins necessary, making queries more complex.
  • Normalization increases the number of tables in the database, so the more there are, the more massive it becomes and the more expensive it becomes to maintain.

Conclusion

In this article, we talked about how normalization helps eliminate anomalies, which can result in data duplication, affecting the integrity of the data and its performance. This article should leave you with the following points:

  • Normalization helps you keep your database organized.
  • It reduces the duplication of data and improves its integrity.
  • In its first normal form, the relation consists of unique values with no-repeat values.
  • The second normal form eliminates all partial dependencies.
  • In the third normal form, no non-prime key attributes should be dependent on anything other than the primary key.
  • BCNF is an updated version of 3NF.
  • The fifth normal form ensures lossless decomposition.
  • The sixth normal form is a breakdown of related variables into irreducible components

As a result of reading this article, hopefully, you now know what normal forms are and how useful they can be when organizing data in a database. Read more articles here!

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anushkakhatri
anushkakhatri

Passionate about solving business problems with data-driven solutions. Skilled in the field of Data Science and Big Data Engineering.
I have worked on various projects, including developing predictive models, analyzing complex data sets, and designing and implementing data architectures and pipelines.
I enjoy exploring new data science and data engineering techniques and keeping up with the latest industry trends.

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