CoP: Data Science: City of Los Angeles Evictions

### Prerequisite(s)
If you would like to work on this issue, please add a comment below and include the following information:
- Your name
- How many hours you can commit to working on this in the next week (minimum of 2)
- Commit to providing an update with a comment before the next community of practice meeting

For example:
- John Doe
- I can commit to working on this issue 3 hours in the following week.
- Yes, I will provide an update on my progress with a comment below.

Once you have done this, please add yourself to the “Assignees” section on the right and update the issue weekly to document your progress.

### Overview
We want to analyze eviction data for the city of Los Angeles, and incorporate data from other sources, to determine whether there are actions local leaders can take to address the problem. The following background information is from the [LA Controller's website](https://controller.lacity.gov/landings/evictions):
- August 1, 2023 – rent owed from March 1, 2020 to August 31, 2020 is due. If the Declaration of COVID-19-Related Financial Distress form was returned to the landlord within 15 days of rent being due, they cannot be evicted for nonpayment of rent.
- February 1, 2024 – rent owed from October 1, 2021 to January 31, 2023 is due. If a tenant returned the Declaration of COVID-19-Related Financial Distress form to the landlord within 15 days of rent being due AND paid 25% of rent owed from this period, they cannot be evicted for nonpayment of rent.
- However, since March 27, 2023, landlords may not evict a tenant who falls behind in rent unless the tenant owes an amount higher than the Fair Market Rent (FMR). The FMR depends on the bedroom size of the rental unit.

### Action Items
Phase 1
- [x] Find available data sources and add to Resources section
- [x] Perform Exploratory Data Analysis (read more [here](https://www.analyticsvidhya.com/blog/2021/08/how-to-perform-exploratory-data-analysis-a-guide-for-beginners/)
- [x] Create data dictionary (EDA task)
- [x] Perform data cleaning (EDA task)
- [x] Understand and outline data context
- [x] Determine is this is one-time or ongoing project (and assign appropriate label)
- [x] Write one-sheet (see Resources below)
- [x] Define stakeholder
- [x] Summarize project, including value add
- [x] Define project 6 month roadmap
- [x] Detail history (if any)
- [x] Define tools to be used for analysis and visualization (if applicable)
- [x] Create issues required to fulfill project requirements, including exploratory data analysis, required tasks, and deliverables

### Resources/Instructions
[Feb 2023 - July 2023 eviction data csv file](https://drive.google.com/drive/folders/1uyPtg1MNX5LIDwQkFtErmIQJNe9N7X25)
Check #178 for updates on whether a real time source for this data have been found

@pranjaliseth provided the following links
- Aug 12, 2024, Metadata/data sources (external public links):
- https://www.laalmanac.com/employment/em12c.php
- https://www.laalmanac.com/population/po24la_zip.php
- https://gist.github.com/erichurst/7882666
- Mar 25, 2024, (dataset reference) Fair Market Rent data mentioned — came from:
- https://www.laalmanac.com/economy/ec40b.php

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Contributors to this issue - see https://github.com/hackforla/data-science/issues/179#issuecomment-3173421553