The real estate market is a complex ecosystem driven by numerous variables such as location, property features, market trends, and economic indicators. One dataset that offers a deep dive into this complexity is the Ames Housing dataset. Originating from Ames, Iowa, this dataset comprises various properties and their characteristics, ranging from the type of alley
The post Exploring Dictionaries, Classifying Variables, and Imputing Data in...
Geospatial visualization has become an essential tool for understanding and representing data in a geographical context. It plays a pivotal role in various real-world applications, from urban planning and environmental studies to real estate and transportation. For instance, city planners might use geospatial data to optimize public transportation routes, while real estate professionals could leverage
The post From Data to Map: Visualizing...
Machine learning is an amazing tool for many tasks. OpenCV is a great library for manipulating images. It would be great if we can put them together. In this 7-part crash course, you will learn from examples how to make use of machine learning and the image processing API from OpenCV to accomplish some goals.
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The digital age has ushered in an era where data-driven decision-making is pivotal in various domains, real estate being a prime example. Comprehensive datasets, like the one concerning properties in Ames, offer a treasure trove for data enthusiasts. Through meticulous exploration and analysis of such datasets, one can uncover patterns, gain insights, and make informed
The post Revealing the Invisible: Visualizing Missing Values in Ames...
In this enlightening journey through the myriad lanes of Ames properties, we shine our spotlight on Descriptive Statistics, a cornerstone of Data Science. The study of the Ames properties dataset provides a rich landscape for implementing Descriptive Statistics to distill volumes of data into meaningful summaries. Descriptive statistics serve as the initial step in data
The post Decoding Data: An Introduction to Descriptive Statistics with...
In a previous tutorial, we explored logistic regression as a simple but popular machine learning algorithm for binary classification implemented in the OpenCV library. So far, we have seen how logistic regression may be applied to a custom two-class dataset we have generated ourselves. In this tutorial, you will learn how the standard logistic regression
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Logistic regression is a simple but popular machine learning algorithm for binary classification that uses the logistic, or sigmoid, function at its core. It also comes implemented in the OpenCV library. In this tutorial, you will learn how to apply OpenCV’s logistic regression algorithm, starting with a custom two-class dataset that we will generate ourselves.
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Many machine learning models have been developed, each with strengths and weaknesses. This catalog is not complete without neural network models. In OpenCV, you can use a neural network model developed using another framework. In this post, you will learn about the workflow of applying a neural network in OpenCV. Specifically, you will learn: What
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Using a Haar cascade classifier in OpenCV is simple. You just need to provide the trained model in an XML file to create the classifier. Training one from scratch, however, is not so straightforward. In this tutorial, you will see how the training should be like. In particular, you will learn: What are the tools
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Before the deep learning revolution redefined computer vision, Haar features and Haar cascades were the tools you must not ignore for object detection. Even today, they are very useful object detectors because they are lightweight. In this post, you will learn about the Haar cascade and how it can detect objects. After completing this post,
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