Belangrijkste beïnvloeders voor gemiddelde prijs van een listing

Door de hele cursus heen heb je Exploratory Data Analysis gebruikt om mogelijke variabelen te vinden die de prijs van een listing kunnen beïnvloeden. De Power BI-functie "Key Influencers" versnelt dit proces of stuurt op z'n minst je verdere EDA-inspanningen.

In deze oefening ga je deze functionaliteit verkennen door de belangrijkste beïnvloeders van de gemiddelde prijs van een listing te analyseren.

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In this chapter, you’ll get more familiar with time-based variables and the multiple ways to extract further variables using EDA for analysis—like day of week and time difference. You’ll get hands-on with Power BI as you build line charts to calculate new metrics and uncover trends hiding in your data—including period-over-period change and rolling averages.

Exercise 1: Starting to explore time series data Exercise 2: Statements about time series analysis Exercise 3: Exploring AirBnB time series Exercise 4: Generating a new date variable Exercise 5: Analyzing trends by day of week Exercise 6: Calculating year-over-year change

Exercise 1: Time series run chart Exercise 2: Cyclical vs. Seasonal Exercise 3: Analyzing Glassdoor reviews over time Exercise 4: Building your first time series chart Exercise 5: Calculating rolling averages Exercise 6: Finding anomalies in time series data

One of the most powerful functions of EDA in Power BI is being able to identify which variables have the most influence on your target outcome. A native Power BI visualization tool enabling that is Decomposition Trees. You'll learn about Decomposition Trees, how to construct, then interpret in order to explain a target outcome by other variables.

Exercise 1: Introduction to decomposition trees Exercise 2: Statements about decomposition trees Exercise 3: Using decomposition trees in Power BI Exercise 4: Getting started with decomposition trees Exercise 5: Diving deeper into decomposition trees Exercise 6: Decomposing attrition rates

In this chapter you'll build another native Power BI tool, Key Influencers visual. It helps you to understand how much a target outcome changes based on specific variables and segments of observations.

Exercise 1: Introductie tot key influencers Exercise 2: Decomposition trees vs. key influencers Exercise 3: Key influencers gebruiken in Power BI Exercise 4: Belangrijkste beïnvloeders voor gemiddelde prijs van een listing

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Exercise 5: Segmenten uit key influencers Exercise 6: Key influencers voor superhosts Exercise 7: Gefeliciteerd!