The real estate industry is a vast network of stakeholders including agents, homeowners, investors, developers, municipal planners, and tech innovators, each bringing unique perspectives and objectives to the table. Within this intricate ecosystem, data emerges as the critical element that binds these diverse interests together, facilitating collaboration and innovation. PropTech, or Property Technology, illustrates this […]
Data transformations enable data scientists to refine, normalize, and standardize raw data into a format ripe for analysis. These transformations are not merely procedural steps; they are essential in mitigating biases, handling skewed distributions, and enhancing the robustness of statistical models. This post will primarily focus on how to address skewed data. By focusing on […]
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In the world of data science, where raw information swirls in a cacophony of numbers and variables, lies the art of harmonizing data. Like a maestro conducting a symphony, the skilled data scientist orchestrates the disparate elements of datasets, weaving them together into a harmonious composition of insights. Welcome to a journey where data transcends […]
In the realm of data analysis, SQL stands as a mighty tool, renowned for its robust capabilities in managing and querying databases. However, Python’s pandas library brings SQL-like functionalities to the fingertips of analysts and data scientists, enabling sophisticated data manipulation and analysis without the need for a traditional SQL database. This exploration delves into […]
Outliers are unique in that they often don’t play by the rules. These data points, which significantly differ from the rest, can skew your analyses and make your predictive models less accurate. Although detecting outliers is critical, there is no universally agreed-upon method for doing so. While some advanced techniques like machine learning offer solutions, […]
In the world of real estate, numerous factors influence property prices. The economy, market demand, location, and even the year a property is sold can play significant roles. The years 2007 to 2009 marked a tumultuous time for the US housing market. This period, often referred to as the Great Recession, saw a drastic decline […]
The Chi-squared test for independence is a statistical procedure employed to assess the relationship between two categorical variables – determining whether they are associated or independent. In the dynamic realm of real estate, where a property’s visual appeal often impacts its valuation, the exploration becomes particularly intriguing. But how often do you associate a house’s […]
In the realm of inferential statistics, you often want to test specific hypotheses about our data. Using the Ames Housing dataset, you’ll delve deep into the concept of hypothesis testing and explore if the presence of an air conditioner affects the sale price of a house. Let’s get started. Overview This post unfolds through the […]
In the vast universe of data, it’s not always about what we can see but rather what we can infer. Confidence intervals, a cornerstone of inferential statistics, empower us to make educated guesses about a larger population based on our sample data. Using the Ames Housing dataset, let’s unravel the concept of confidence intervals and […]
