Preprocessing data for Keras models (Normalization, Scaling, etc.)

When working with neural networks and in particular with Keras models, preprocessing your data becomes a crucial step in achieving optimal performance. Preprocessing techniques such as normalization, scaling, and other transformations help in improving the convergence speed and overall accuracy of your deep learning models.

In this article, we will explore some of the common preprocessing techniques used for preparing data for Keras models.


Normalization is a widely used technique in deep learning to scale numeric data. It ensures that all input features or variables have the same scale, which prevents some features from dominating the learning process because they have larger values. Normalization generally involves scaling numerical values to a range between 0 and 1.

To perform normalization on your data, you can use the MinMaxScaler class from scikit-learn, which provides an easy and efficient way to apply this transformation. Here's an example of how to use it:

from sklearn.preprocessing import MinMaxScaler

scaler = MinMaxScaler()
normalized_data = scaler.fit_transform(data)

By default, MinMaxScaler scales the data between 0 and 1, but you can specify a different range if needed.


Standardization is another technique that can be useful, especially when dealing with features that have different units or scales. It transforms the data in such a way that it has zero mean and unit variance. Standardization can be achieved using the StandardScaler class from scikit-learn:

from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
standardized_data = scaler.fit_transform(data)

Standardization is particularly useful when using deep learning algorithms that involve optimization methods such as gradient descent, as it can lead to a more stable and faster convergence.

Handling Categorical Data

In many real-world problems, the input data may contain categorical variables, such as different classes or labels. These variables cannot be directly used by deep learning models, as they require numerical inputs. Therefore, we need to perform some preprocessing steps to transform categorical variables into numerical representations.

One common approach is one-hot encoding. This technique creates binary columns for each category and marks the presence of a category with a 1, while the others are marked with zeros. For example, if you have a categorical variable "color" with three possible values: red, blue, and green, one-hot encoding will transform it into three binary columns where only one column will have a 1 and the others will have 0 depending on the value in each data sample.

In Keras, you can use the to_categorical function from the utils module for one-hot encoding:

from keras.utils import to_categorical

encoded_labels = to_categorical(labels)

This function will automatically convert a 1-dimensional array of integers (labels) into a 2-dimensional array of one-hot encoded vectors.

Handling Missing Data

Dealing with missing data is another important preprocessing step, as deep learning models usually require complete datasets. There are various techniques to handle missing data, such as replacing missing values with mean or median, filling with zeros, or using more advanced imputation techniques.

Keras doesn't provide specific functions for dealing with missing data, so you can use libraries like pandas to handle this preprocessing step. For instance, you can use the fillna method in pandas to fill missing values with the mean:

import pandas as pd

filled_data = data.fillna(data.mean())

Replace data with your actual data frame containing the missing values.


Preprocessing data is a vital step to achieve accurate and efficient deep learning models using Keras. Normalization and standardization ensure that features are on the same scale, while handling categorical data and missing values are necessary to make the data suitable for training deep learning models. Understanding and implementing these techniques can greatly improve the performance and convergence of your Keras models.

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