Rently - London Housing Market

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.

Contents

• 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.

Example Prerequisites

• 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

Tools and Editors

• Google Colab

Libraries

NumPy, Pandas, Matplotlib, and Seaborn

Case Study Involves

• 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