This case study revolves around performing exploratory data analysis on a real estate agency's data to help the agency understand their business's performance and provide them with actionable metrics to enhance their decision-making. To achieve this, the following steps were performed:
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Business Requirements & Conceptual Data Modelling: The first step of this case study was to understand the requirements and map them to a conceptual data model.
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Creating the Relational Data Model: The conceptual data model created using EER and UML class diagram was then mapped to a relational model.
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Data Gathering: For this project, the data was gathered/generated from different sources such as Zillow's website and Excel.
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Loading Data: After collecting the data, it was loaded into MySQL and MongoDB databases.
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Data Pre-pocessing: Columns like the date columns had to be modified after loading into the database to make the data types suitable for MySQL.
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Data Analysis: In this step, the data was analyzed using SQL, MongoDB Query Language, and Python.
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Data Visualization: Finally, the findings of the previous steps were visualized using Python.
- Python
- MySQL (SQL)
- MongoDB (MongoDB Query Language)
This project is complete and therefore, not open for contribution. But any suggestions regarding the implementation or additions are highly appreciated.
- LinkedIn - https://www.linkedin.com/in/stuti-dhebar/
- Email - stutidhebar@gmail.com