Question: This exercise will be using the [Airbnb dataset] (http://insideairbnb.com/get-the-data.html) for NYC called 'listings.csv'. You can download it directly [here] (http://data.insideairbnb. com/united-statesyew-york-city/2022-12-04/visualisations/listings.csv) g) For all listings
![This exercise will be using the [Airbnb dataset] (http://insideairbnb.com/get-the-data.html) for NYC](https://dsd5zvtm8ll6.cloudfront.net/si.experts.images/questions/2024/09/66f3d9ec76a09_06866f3d9ec0c8dd.jpg)
![called 'listings.csv'. You can download it directly [here] (http://data.insideairbnb. com/united-statesyew-york-city/2022-12-04/visualisations/listings.csv) g) For](https://dsd5zvtm8ll6.cloudfront.net/si.experts.images/questions/2024/09/66f3d9ed3eb7d_06866f3d9ecaaee6.jpg)
This exercise will be using the [Airbnb dataset] (http://insideairbnb.com/get-the-data.html) for NYC called 'listings.csv'. You can download it directly [here] (http://data.insideairbnb. com/united-statesyew-york-city/2022-12-04/visualisations/listings.csv) g) For all listings of type Shared room, plot the dendrogram of the hierarchical clustering generated from longitude, latitude, and price. You can use any distance function. - (10 points from scipy. cluster impart hierarchy hierarchy. Linkage(...) hierarchy, dendrogram(...) 0.0s Python h) Normalize longitude, latitude, and price by subtracting by the mean (of the column) and dividing by the standard deviation (of the column). Repeat g) using the normalized data. Comment on what you observe. - (5 points)
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