In this case study I will be exploring a hypothetical business case for the company Rently (https://use.rently.com/), which developed a patented self-touring technology that automates property tours of a home or apartment. Rently is seeking to expand their customer base to the London market by the end of 2023. In order to do this, Rently has created a proposal for a data-driven approach to this challenge and has provided data from London Datastore: https://data.london.gov.uk/. Rently is planning to begin trial runs of their service in five selected boroughs of London by the second quarter of 2022, so time is of the essence.
- Rently_London_Housing_Market.ipynb: a file containing the Python code for this business case. It includes the following sections: Introduction; Sourcing and Loading; Cleaning, Transforming, and Visualizing; Modeling; and Summary.
- Introduction to Python for Data Science
- Intermediate Python for Data Science
- Data Types for Data Science
- Python Data Science Toolbox
- Pandas Foundations
- Manipulating DataFrames with pandas
- Merging DataFrames with pandas
- Google Colab
NumPy, Pandas, Matplotlib, and Seaborn
- Data Ingestion and Inspection
- Exploratory Data Analysis (EDA)
- Tidying and Cleaning
- Transforming DataFrames
- Subsetting DataFrames with Lists
- Filtering DataFrames
- Grouping Data
- Melting Data
- Advanced Indexing
- Dictionaries
- Handling Dates and Times
- Function Definitions
- Default Arguments, Variable Length, and Scope
- Lambda Functions and Error Handling