Power BI using DAX and Advanced Analytics for the given Airbnb case.

$***$Requirements: Create a Power BI report $($NO DASHBOARD$)$ using PB Desktop step$-$by$-$step including:

$-$ Do any necessary data cleaning and transformation to enhance$/$enrich the dataset using DAX. Ensure that there are no $$unnecessary$$ columns in your main table $$Listing$$ and the end result of the data cleaning and transformation is three tables: listing, reviews, and neighborhood contains the data for the three quarters.

$-$ Perform data anonymization techniques

$-$ Create the necessary Model and relations.

$-$ Using DAX as a data transformation tool, add at least one from each type of calculation $($calculated columns, Measures, and "date table"$).$

$-$ Using the added calculations to your project, present the results of your analysis.

$-$Investor$$ point of view, answering questions like $($or any other question you find valuable$)$:

o Which location$/$ time is best to invest in Airbnb?

o What is the expected revenue from the investment? Change over time?

o What are the characteristics of a $$good$$ unit to invest $($number of beds, baths, unit type $..$etc$)$

$-$ Customer's point of view, answering questions like $($or any other question you find valuable$)$::

o Which location$/$ time is best to rent on Airbnb?

o What is the expected cost of short$-$term$/$long$-$term stay? Which is better, multiple short stays in different units or one long stay in the same unit?

o What are the characteristics of a $$good$$ unit to rent $($number of beds, baths, unit type $..$etc$)$

$***$Deliverables:

$-$ Power BI report construction step by step including:

Page $1$: Overview, Page $2$: $$Investor$$ point of view analysis with $4$ diferent kind of visuals, Page $3$: Customer's point of view analysis with $4$ diferent kind of visuals, Page $4$: Conclusions.

Please avoid to use any "review" variable for Investor$/$Customer analysis. Thx :$)$

$-$ Data transformation efforts using DAX, a list of created calculations with a short description of the calculation and its added value to the analysis.

$-$ Analysis findings and main recommendations.

$-$ Appendix: All DAX code used in the project $($as text, not screenshots$).$

$***$ Data source: $($Inside Airbnb: Get the Data$)$

$//$insideairbnb$.$com$/$get$-$the$-$data$/$

Files needed: Toronto, Ontario, Canada

$>12$ December, $2023$

$-$ listings.csv

$-$ reviews.csv

$-$ neighbourhoods.csv

$>03$ September, $2023$

$-$ listings.csv

$-$ reviews.csv

$-$neighbourhoods.csv

$>05$ June, $2023$

$-$ listings.csv

$-$ reviews.csv

$-$ neighbourhoods.csv

Note: You need to "merge" them in a single "listing", "reviews" and "neighbourhoods" data tables $($as the attached pic$).$

Note $2$:

$+$ listings.csv: Summary information and metrics for listings in Toronto $($Good for visualisations$).$

$+$ reviews.csv: Summary Review data and Listing ID $($to facilitate time based analytics and visualisations linked to a listing$).$

$+$ neighbourhoods.csv Neighbourhood list for geo filter. Sourced from city or open source GIS files.

Note $3$: Model View attached as a picture

First $5$ columns for each file as follows:

$>>>$ listings $(18$ columns$)$

id name host$\_$id host$\_$name neighbourhood$\_$group neighbourhood latitude longitude room$\_$type price minimum$\_$nights number$\_$of$\_$reviews last$\_$review reviews$\_$per$\_$month calculated$\_$host$\_$listings$\_$count availability$\_365$ number$\_$of$\_$reviews$\_$ltm license

$1419$ Beautiful home in amazing area! $1565$ Alexandra Little Portugal $43\mathrm{.}6459-79\mathrm{.}42423$ Entire home$/$apt $2862017-08-070\mathrm{.}06100$

$8077$ Downtown Harbourfront Private Room $22795$ Kathie & Larry Waterfront Communities$-$The Island $43\mathrm{.}6408-79\mathrm{.}37673$ Private room $1801692013-08-270\mathrm{.}96200$

$26654$ World Class @ CN Tower, convention centre, Theatre $113345$ Adela Waterfront Communities$-$The Island $43\mathrm{.}64608-79\mathrm{.}39032$ Entire home$/$apt $16428422023-09-010\mathrm{.}2651152$

$307726$ Boutique Chic at Maple Leaf Square $1108156$ Karen Waterfront Communities$-$The Island $43\mathrm{.}64221-79\mathrm{.}38061$ Entire home$/$apt $28662022-05-280\mathrm{.}45200$

$27423$ Executive Studio Unit$-$ Ideal for One Person $118124$ Brent South Riverdale $43\mathrm{.}66884-79\mathrm{.}32725$ Entire home$/$apt $7590282023-08-310\mathrm{.}1711461$

###Caution### info about number of beds $/$ baths in column B $($name$).$ Please split as a single colum for beds and baths.

$>>>$ neighbourhoods $(2$ columns$)$

neighbourhood$\_$group neighbourhood

Agincourt North

Agincourt South$-$Malvern West

Alderwood

Annex

Banbury$-$Don Mills

$>>>$ reviews $($Columns$)$

listing$\_$id date

$14192015-07-19$

$14192015-08-29$

$14192015-09-07$

$14192016-03-28$

$14192017-08-03$