EDA Project - AIPM course

Welcome to the EDA project of week 5 in the AIPM course.

Short description of the problem

1. Through EDA/statistical analysis I will present at least 3 insights regarding the overall data, one of them geographical.
2. I will present at least 3 recommendations for my client Erin Robinson. She invests in poor neighborhoods, does buying & selling, wants mainly to have her costs back and make a little profit. She is a socially responsible woman.
3. Recommendations will include

• where to best invest for making a profit (geographically) (even if small).
• emerging areas - still quite cheap but with growing prices
• what time of the year is best to buy and sell, directed at her area of interest (poor neighborhoods)
• looking at price per sqft values to identify if the areas are really cheap.

4. Assumptions and information from Erin to better specify her search :

• "poor" areas means that it is within the 10% cheapest houses in the county

5. Hypothesis

• The lowest-priced houses (bottom 10%) in the entire region are concentrated in a few of all the ZIP codes, indicating highly localized investment zones.
• The Price-per-SqFt growth rate year-over-year in the target neighborhoods will exceed the city average Price-per-SqFt growth rate over the next years based on the available history.
• Within poor areas, the houses are generally smaller, thus also contributing to a lower mean price
• Summer time is a good time to buy since most people think about vacation. January seems to be good to sell, since people had time over Christmas to investigate the market.
• The average House SqFt in target neighborhoods is significantly smaller than the city average, suggesting the low price is partially driven by unit size, not just location risk.

Description of the contents of the repository

• EDA.jpynb contains the Jupyter Notebook with the code I've written for this project and comments explaining it.

• 1_Fetching_the_data_eda.ipynb contains the Jupyter Notebook with the code to get the house data from the SQL data base

• README.md this file that describes the contents of the repository. This file is the source of information for navigating through the repository.

• 202510_EDA_Project_insights.pptx (and EDA_Project_insights.ppt) is a short presentation giving a high-level overview of my methodology and recommendations for my client Erin.

• cleanup.py - a Python script for processing and cleaning my data, using functions and docstrings.