Explore the data using data visualization techniques, such as histograms, scatter plots, and heatmaps. Identify any trends or patterns in the data that you find interesting, and write a brief report (2-3 pages) summarizing your findings.

Exploring the Housing Market in Python

Objective: To gain practical experience in importing and managing data in Python, and to use data visualization techniques to explore trends in the housing market.

Task:

1. Obtain a dataset of housing prices and related variables such as square footage, number of rooms, location, etc.
2. Import the data into Python using the Pandas library.
3. Perform any necessary data cleaning and preparation, such as handling missing values and removing duplicates.
4. Explore the data using data visualization techniques, such as histograms, scatter plots, and heatmaps.
5. Identify any trends or patterns in the data that you find interesting, and write a brief report (2-3 pages) summarizing your findings.

Resources:

• Pandas documentation: https://pandas.pydata.org/docs/
• Links to an external site.
• Matplotlib documentation: https://matplotlib.org/stable/contents.html
• Links to an external site.
• Seaborn documentation: https://seaborn.pydata.org/

• Links to an external site.