Understanding the Differences Between Normalization and Standardization

Explore the key differences between data normalization and standardization, and when to use them. Learn through practical examples with the 'Hotel Reservation Cancellations' dataset, used in our revenue management project. Discover how scaling your data can boost analysis and model accuracy.

Introduction

Data preprocessing is a critical step in the world of data analysis and machine learning. Among the numerous techniques available for data preprocessing, normalization and standardization are two fundamental methods. These techniques play a pivotal role in making our data ready for analysis, ensuring that our models perform effectively. However, they serve different purposes and have distinct effects on the data. In this blog, we will explore the differences between normalization and standardization and when to use each one.

Normalization: Scaling Data to a Common Range

Normalization is a technique used to rescale data to fall within a specific range, typically between 0 and 1. It is particularly useful when the features (variables) in your dataset have different units or ranges. The primary goal of normalization is to eliminate the influence of scale, making all features equally important during analysis.

Here's the formula for Min-Max normalization:

In this equation:

• X is the original value of a data point.

• X min is the minimum value of the feature in the dataset.

• X max is the maximum value of the feature in the dataset.

• X scaled is the scaled value between 0 and 1.

Key points about normalization:

1. Range Preservation: Normalization maintains the original distribution of the data within the specified range.

2. Applicability: It's suitable when you have a bounded dataset and want to ensure that all features have similar scales.

3. Outliers: Sensitive to outliers since it uses the minimum and maximum values for scaling.

Standardization: Scaling Data to a Standard Distribution

Standardization, also known as Z-score normalization, transforms data into a standard distribution with a mean (μ) of 0 and a standard deviation (σ) of 1. This technique is particularly helpful when your data features exhibit different units, and you want to bring them to a common scale while preserving the distribution's shape.

Here's the formula for standardization:

In this equation:

• X: The original value of a data point.

• μ (mu): The mean of the feature in the dataset.

• sd or σ (sigma): The standard deviation of the feature in the dataset.

• X_scaled: The standardized value with a mean of 0 and a standard deviation of 1.

Key points about standardization:

1. Mean and Variance Preservation: Standardization maintains the original distribution's mean and standard deviation.

2. Outliers: Less sensitive to outliers compared to normalization since it uses the mean and standard deviation.

3. Applicability: Suitable for algorithms that assume the data follows a normal distribution, such as many machine learning models (e.g., Support Vector Machines, Principal Component Analysis).

Example: Scaling and Visualizing Data (Normalization and Standardization)

For this example, we will use the "Hotel Reservation Cancellations" dataset, which was utilized in the project titled "Forecasting Hotel Reservation Cancellations: Advanced Machine Learning for Revenue Management.".

Let's walk through the code using Python to apply both normalization and standardization to this dataset. We'll also visualize the distributions before and after scaling

In this section, we import the necessary libraries and load the Hotel Reservation dataset from Kaggle. Replace the file path with your dataset's location.

Here, we create a MinMaxScaler object and apply Min-Max scaling (normalization) to the selected columns. The scaled data is stored in the df_hotel_reservation_scaled DataFrame.

In this part, we create a StandardScaler object and apply standardization to the selected columns. The standardized data is stored in the df_standardization DataFrame.

In this section, we set up a Dash app for visualization. We create a layout that includes a dropdown to select the column for visualization and three graphs for displaying histograms before scaling, after normalization, and after standardization.

In this final part, we define a callback to update the histograms based on the selected column. We create histograms for the original data, data after normalization, and data after standardization. The Dash app runs the server, allowing users to interactively explore the effects of scaling on the dataset.

This practical demonstration should help you understand the impact of scaling techniques on your data and how to visualize these effects.

Conclusion

Normalization and standardization are essential data preprocessing techniques that help prepare your data for analysis and modeling. Understanding the differences between these methods is crucial for choosing the right one for your specific problem. Whether you opt for normalization to bind your data or standardization to maintain the distribution, these techniques empower you to extract meaningful insights and build accurate machine-learning models.