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Python BI project applying MongoDB & Folium

For this project, I chose a certain Japanese bakery with 107 years of history that originally started in Seattle, USA. As expansion project, they are interested in going back to Seattle, the company hometown and have a location where they can showcase their long history and be part of one of the most vibrant cities in the USA.

Seattle is home to some of the greatest companies in the world like Amazon, Boeing, Costco, Starbucks and others...is time to take our place too.

Client Requirements

As mentioned before our client wants to be located in Seattle but is open to explore the Great Puget Sound Area best know as Seattle Great Area that includes the cities of Seattle, Bellevue, Redmond, and Kirkland.

• Budget: 3 million dollars as initial investment and setup

• Restrictions:

• • Must be located in a high foot traffic area.

  • Must be located in a safe environment where Japanese employees can work peacefully without dealing with the negative aspects of Seattle.

  • Must be located in a highly commercial area with preference in Seattle downtown or the International District.

  • Must be located at the ground level (First floor) with:

    • Street access
    • Employees access

  • Parking area for clients is optional as longer the possible location is in a commercial area with easy street parking access.

  • Easy access for public and private transportation for customers and employees.

  • Multi-purpose commercial areas are preferable.

  • This store is a step-stone for future stores.

• Localization: Seattle, Bellevue, Kirkland, Redmond.

Project Steps

Jupyter notebook:

1. I defined a testing area where I can try my mongo queries, data frames and results manipulation.
2. Defined a specific query that helped me to find a possible cluster of companies as the product of the client is targetting middle and middle-high class workers. As the Great Seattle Area is home of large companies I wanted to find how these clusters are distributed in the area.
3. Once I got the query result I worked my wat to unwrap all info in a clean and clear data frame.
4. This data frame was added to Mongo for future reference.
5. With this data frame, I produced 2 maps: Heatmap and Markers map that allowed me to identify possible locations.
6. After, some extra research a location was chosen where all the conditions of the client are covered.

Files

1. Mongo query's - Funnel.ipynb
2. dataset_clean.csv
3. dataset_clean.json
4. README.md
5. Maps:
   1. heatmap.html
   2. detail_clustering.html
   3. proposed_site.html

Time

2 days

Author

EsdrasGrau

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