Preprocessing is an important step in EDA, as it can help to reduce noise and improve the quality of the images. Common preprocessing techniques include resizing, normalization, data augmentation, image …
Performing data preprocessing – Exploring Image Data Read MoreThe first step in any EDA process is to load the image data into your Integrated Development Environment (IDE) workspace, such as VS Code, Jupyter Notebook, or any other Python …
Loading the data – Exploring Image Data Read MoreIn this section, we explore the power of visualization tools and techniques to gain meaningful insights into the characteristics and patterns of image data. Using Python libraries such as Matplotlib …
Visualizing image data using Matplotlib in Python – Exploring Image Data Read MoreIn this chapter, we will learn how to explore image data using various packages and libraries in Python. We will also see how to visualize images using Matplotlib and analyze …
Technical requirements – Exploring Image Data Read MoreIn summary, the line cluster_mask = (kmeans_model.labels_ == cluster_idx) creates a mask that helps you filter and select the data points in a specific cluster based on their assigned cluster …
Using k-means clustering to label regression data – Labeling Data for Regression-2 Read MoreIn this section, we are going to use the unsupervised K-means clustering method to label the regression data. We use K-means to cluster data points into groups or clusters based …
Using k-means clustering to label regression data – Labeling Data for Regression-1 Read MoreBy iterating through each value in noise_range and adding noise to each data point’s price feature, the code generates multiple data points with different levels of noise. This process results …
Using data augmentation to label regression data – Labeling Data for Regression-2 Read MoreData augmentation can be used to generate additional labeled data for regression tasks where labeled data is limited. Here is a way to use data augmentation to label regression data: …
Using data augmentation to label regression data – Labeling Data for Regression-1 Read MoreIn this code snippet, we’re building and training a linear regression model using the labeled data. First, we import the LinearRegression class from the sklearn.linear_model library. Then, we create an …
Pseudo-labeling – Labeling Data for Regression-2 Read MoreUsing semi-supervised learning to label regression data In this section, we are going to use semi-supervised learning to label the regression data. Semi-supervised learning is a type of machine learning …
Pseudo-labeling – Labeling Data for Regression-1 Read More