Articles About Machine Learning

Mastering Pair Plots for Visualization and Hypothesis Creation in the Ames Housing Market

Navigating the complex landscape of real estate analytics involves unraveling distinct narratives shaped by various property features within the housing market data. Our exploration today takes us into the realm of a potent yet frequently overlooked data visualization tool: the pair plot. This versatile graphic not only sheds light on the robustness and orientation of connections between features and sale prices but also provides a holistic perspective on the dynamics among different features within the dataset. Let’s get started. Mastering […]

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Feature Relationships 101: Lessons from the Ames Housing Data

In the realm of real estate, understanding the intricacies of property features and their impact on sale prices is paramount. In this exploration, we’ll dive deep into the Ames Housing dataset, shedding light on the relationships between various features and their correlation with the sale price. Harnessing the power of data visualization, we’ll unveil patterns, trends, and insights that can guide stakeholders from homeowners to real estate developers. Let’s get started. Feature Relationships 101: Lessons from the Ames Housing DataPhoto […]

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Exploring Dictionaries, Classifying Variables, and Imputing Data in the Ames Dataset

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 access to the property’s overall condition. In this post, your aim is to take a closer look at this dataset using data science techniques. Specifically, […]

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From Data to Map: Visualizing Ames House Prices with Python

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 it to analyze property value trends in specific regions. Using Python, we can harness the power of libraries like geopandas, matplotlib, and contextily to create […]

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Decoding Data: An Introduction to Descriptive Statistics with the Ames Housing Dataset

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 analysis, offering a concise summary of the main aspects of a dataset. Their significance lies in simplifying complexity, aiding data exploration, facilitating comparative analysis, and […]

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Machine Learning in OpenCV (7-Day Mini-Course)

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. This mini-course is intended for practitioners who are already comfortable with programming in Python, know the basic concept of machine learning, and have some background […]

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Revealing the Invisible: Visualizing Missing Values in Ames Housing

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 decisions. Starting from this post, you will embark on a captivating journey through the intricate lanes of Ames properties, focusing primarily on Data Science techniques. […]

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Techniques to Write Better Python Code

We write a program to solve a problem or make a tool that we can repeatedly solve a similar problem. For the latter, it is inevitable that we come back to revisit the program we wrote, or someone else is reusing the program we write. There is also a chance that we will encounter data that we didn’t foresee at the time we wrote our program. After all, we still want our program to work. There are some techniques and […]

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Using Kaggle in Machine Learning Projects

You’ve probably heard of Kaggle data science competitions, but did you know that Kaggle has many other features that can help you with your next machine learning project? For people looking for datasets for their next machine learning project, Kaggle allows you to access public datasets by others and share your own datasets. For those looking to build and train their own machine learning models, Kaggle also offers an in-browser notebook environment and some free GPU hours. You can also […]

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Setting Breakpoints and Exception Hooks in Python

There are different ways of debugging code in Python, one of which is to introduce breakpoints into the code at points where one would like to invoke a Python debugger. The statements used to enter a debugging session at different call sites depend on the version of the Python interpreter that one is working with, as we shall see in this tutorial.  In this tutorial, you will discover various ways of setting breakpoints in different versions of Python.  After completing […]

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