Question 1 (46 marks)
Objectives:
● Understand datasets with a data scientist mindset.
● Understand and design computation logic and routines in Python.
● Assess the use of Python only and Python data structures to perform extract, load, and transformation operations.
● Assess the use of the Pandas data frame to perform extract, load, transformation, and calculation operations.
● Structure code in appropriate methods (functions), looping and conditions.
● Conduct visualization in an appropriate way.

The dataset in question provides a rich overview of Housing and Development Board (HDB) flat transactions in Singapore. Derived from the national database managed by Singapore’s open data initiative.

The data captured includes vital information such as the resale price, flat type, address, lease commencement date, and floor area, among other details. These elements allow for robust analysis on a multitude of aspects such as price trends and geographical price disparities. You may refer to more information at `https://data.gov.sg/dataset/resale-flat-prices`.

Additionally, this dataset provides an invaluable resource for understanding the evolution of Singapore’s public housing landscape, the preferences of the populace, and market dynamics over time. As such, it is an essential tool for policy makers, real estate professionals, urban planners, and researchers studying Singapore’s unique public housing model.

By addressing the given tasks, you will gain data analysis competencies, including data reprocessing and manipulation, fundamental for preparing and managing datasets.

Reference source not found. you’ll enhance your ability to comprehend data relationships through the practice of creating data
visualizations and executing correlation analysis.