This project contains American real estate analytical insights based on datasets made available by the House Rocket Company in King County (USA)
This is a fictional project. The company, context and business questions are not real. This portfolio is following the blog recommendations Seja um Data Scientist
Data for this project can be found at: Kaggle
- Business problem House Rocket is currently in the real estate market buying and reselling properties through a digital platform. The data scientist is responsible for developing an online dashboard that can be accessed by the CEO from a cell phone or computer with information on properties sold in King County (USA).
The panel must contain:
- Data Overview - Database Overview;
- Properties by zip code - information about properties grouped by zip code;
- Portfolio Density Map - A map view with database distribution;
- Property price by timeline - property price by year of construction or by sale date and property price distribution;
- Distribution of properties by main attributes - distribution of properties by number of bedrooms, bathrooms, floors and whether or not the property has a sea view;
- Custom Data and Map View - A section to choose attributes and see the dataframe and map distribution of these properties.
- Business Assumptions Available data are only from May 2014 to May 2015. The variables are as follows:
| Variable | Definition |
|---|---|
| id | Unique ID for each property sold |
| date | Date of the property sale |
| price | Price of each property sold |
| bedrooms | Number of bedrooms |
| bathrooms | Number of bathrooms, where .5 accounts for a room with a toilet but no shower, and .75 or ¾ bath is a bathroom that contains one sink, one toilet and either a shower or a bath. |
| sqft_living | Square footage of the apartments interior living space |
| sqft_lot | Square footage of the land space |
| floors | Number of floors |
| waterfront | A dummy variable for whether the apartment was overlooking the waterfront or not |
| view | An index from 0 to 4 of how good the view of the property was |
| condition | An index from 1 to 5 on the condition of the apartment |
| grade | An index from 1 to 13, where 1-3 falls short of building construction and design, 7 has an average level of construction and design, and 11-13 have a high quality level of construction and design. |
| sqft_above | The square footage of the interior housing space that is above ground level |
| sqft_basement | The square footage of the interior housing space that is below ground level |
| yr_built | The year the property was initially built |
| yr_renovated | The year of the property’s last renovation |
| zipcode | What zipcode area the property is in |
| lat | Lattitude |
| long | Longitude |
| sqft_living15 | The square footage of interior housing living space for the nearest 15 neighbors |
| sqft_lot15 | The square footage of the land lots of the nearest 15 neighbors |
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Solution Strategy
- Understanding the business model;
- Understanding the business problem;
- Collecting the data;
- Data Preparation;
- Exploratory Data Analysis;
- Dashboard deploy on Heroku.
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Conclusion The objective of this study case was to create a online dashboard to House Rocket's CEO. Deploying the dashboard on Heroku platforms provides the CEO acess from anywhere facilitating both pre-arrange or costumized data visualization.
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Next Steps
- Create and analyze some business hypotheses;
- Flag the recommendation to buy or not the properties in the dataset;
- Flag the sales recommendation with an increase of 10% or 30%;
- Update the online dashboard with these improvements. See more at: https://houserocket-analytics-data.herokuapp.com/
References:
- Python from Zero to DS lessons on "Comunidade DS - course"
- Dataset House Sales in King County (USA) from Kaggle
- Icons made by from https://www.cfrinc.net/cfrblog/data-analysis-techniques-for-market-research