This is the third article in a series about creating business value with data science! Throughout the series, we’ll be going through the entire data science pipeline to hit on specific business value targets using a Kaggle competition as a case study and producing an executive report at the end. See part 1 and part 2.
Here in part 3, we’re going to take a deeper dive into the Zillow Zestimate dataset. Last time we look at very high-level things like variable correlations and made basic visualisations like histograms and bar charts. This time we’re going to go deeper, doing multi-variate feature analysis and former concrete conclusions about our understanding of the dataset. All of this will be presented in the Jupyter notebook linked below.
Here we go!
If you think more can be included in this EDA, be sure to leave a response down below so we can share the knowledge!
Next up …
That concludes Part 3: Data details of our Creating business value with Data Science series! Stay tuned for Part 4: Machine Learning for making decisions where we will train a few machine learning models to make predictions on our data and find out which one is the best!
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