In the current era of data-driven decision-making, understanding the intricacies of data extraction, cleansing, validation, and enrichment is a valuable asset for businesses across all sectors.
In the current era of data-driven decision-making, understanding the intricacies of data extraction, cleansing, validation, and enrichment is a valuable asset for businesses across all sectors.
In this YouTube video, I showcase an intriguing project I completed: a deep-dive into data extraction and pre-processing for a real-estate prediction model.
This video underscores the nuances of working with complex and unstructured data, particularly from websites that don’t readily offer API access. It mirrors the real-world scenario where important data can be hidden or nested within unconventional formats, demanding innovative problem-solving skills and a deep understanding of data structures.
In my work, I demonstrate how I accessed an archive of rental apartment
listings, using these data to build an impactful machine learning model for real
estate pricing. This model was developed in the context of hyperparameter
optimization for Lasso Regression and XGBoostRegressor models, with predictive
performance and interpretability forming the critical evaluation criteria.
Beyond simply extracting and modeling the data, I emphasize the importance of data cleansing, validation, and enrichment, which ultimately contribute to the quality and accuracy of the resulting model. Notably, I delve into the problem of accurately obtaining and validating GPS coordinates from the listings, and how this information can be leveraged to provide additional value, such as determining neighborhood noise levels or the distance to the nearest public transportation station.
In an industry that’s ever more reliant on data, this video provides a clear demonstration of how meticulous data handling and innovative problem-solving can yield profound insights. It serves as a testament to the applicability of these methods to real-world industry problems, from improving the accuracy of predictive models to enabling smarter, data-driven decision-making processes.
Whether you are a data enthusiast, a professional working in real estate analytics, or simply curious about how data science is applied in a real-world context, this video has something for you. I invite you to watch, learn, and discover how data, when correctly handled and interpreted, can indeed be transformed into actionable insights.
This is the video version of this article, as found on my YouTube channel:
This article precedes the following articles.
| Category: data-preprocessing | Word Count: 3508 | Full Article |
| Category: data-preprocessing | Word Count: 3265 | Full Article |
| Category: data-preprocessing | Word Count: 2372 | Full Article |
| Category: data-preprocessing | Word Count: 4259 | Full Article |
All the articles mentioned here are parts of my Bachelor’s thesis:
Data Mining: Hyperparameter Optimization For Real Estate Prediction Models.