Articles About Machine Learning

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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Static Analyzers in Python

Static analyzers are tools that help you check your code without really running your code. The most basic form of static analyzers is the syntax highlighters in your favorite editors. If you need to compile your code (say, in C++), your compiler, such as LLVM, may also provide some static analyzer functions to warn you about potential issues (e.g., mistaken assignment “=” for equality “==” in C++). In Python, we have some tools to identify potential errors or point out […]

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Profiling Python Code

Profiling is a technique to figure out how time is spent in a program. With these statistics, we can find the “hot spot” of a program and think about ways of improvement. Sometimes, a hot spot in an unexpected location may hint at a bug in the program as well. In this tutorial, we will see how we can use the profiling facility in Python. Specifically, you will see: How we can compare small code fragments using the timeit module […]

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