Long Beach Animal Shelter Intakes and Outcomes

Table of Contents

• Overview
• Project Structure
• Data Source(s)
• Installation
  • Prerequisites
  • Install dependencies
• Usage
  • Run a Jupyter Notebook
• Conclusions
• Technologies Used
• Contributing
• License
• Contact

Overview

This project performs exploratory data analysis on a dataset of animal shelter intakes and outcomes using the Python libraries pandas and seaborn. The goal is to uncover trends, detect anomalies, and visualize key insights to support data-driven decision-making for various city shareholders including shelter managers, animal welfare advocates, government officials, local citizens, and internal analysts.

This is a portfolio project created to demonstrate my proficiency in data analysis, data cleaning, and data visualization using Python. It highlights my ability to work with real-world datasets, derive meaningful insights, and communicate results clearly through code and visualizations.

Project Structure

└── 📁lb-animal-shelter-intakes-outcomes
    └── 📁assets
    └── 📁code
        └── 📁utilities
            ├── __init__.py
            ├── config.py
        ├── notebook.ipynb
    └── 📁data
        └── 📁variable counts
            ├── animal_type.csv
            ├── crossing.csv
            ├── date_of_birth.csv
            ├── geopoint.csv
            ├── intake_condition.csv
            ├── intake_date.csv
            ├── intake_duration.csv
            ├── intake_is_dead.csv
            ├── intake_subtype.csv
            ├── intake_type.csv
            ├── is_current_month.csv
            ├── jurisdiction.csv
            ├── latitude.csv
            ├── longitude.csv
            ├── outcome_date.csv
            ├── outcome_is_dead.csv
            ├── outcome_subtype.csv
            ├── outcome_type.csv
            ├── primary_color.csv
            ├── reason_for_intake.csv
            ├── secondary_color.csv
            ├── sex.csv
            ├── was_outcome_alive.csv
        ├── processed_animal_names.csv
        ├── processed_crossings_unique.csv
        ├── processed_data.csv
        ├── processed_dead_by_name.csv
        ├── raw_animal_names.csv
        ├── raw_crossing_wo_zip.csv
        ├── raw_data.csv
        ├── raw_number_names.csv
    └── 📁products
        └── 📁images
        ├── report.md
    ├── .gitignore
    ├── LICENSE
    ├── README.md
    └── requirements.txt

Data Source(s)

• File: raw_data.csv
• Source: Long Beach Data Portal
• Description: Contains intake and outcome data for Long Beach, CA, starting in 2017.

Installation

Prerequisites

• Python 3.11+
• pip (Python package manager)

Install dependencies

Create a virtual environment (optional but recommended):

python -m venv venv
source venv/bin/activate  # On Windows: venv\\Scripts\\activate
pip install -r requirements.txt

Clone the repository and install required packages:

git clone https://github.com/kozmik-moore/lb-animal-shelter-intakes-outcomes.git
cd lb-animal-shelter-intakes-outcomes
pip install -r requirements.txt

Usage

Run a Jupyter Notebook

Start the Jupyter server:

jupyter notebook

Open and run notebooks from the /code directory to explore data and generate visualizations.

Conclusions

See full visual report in /products/report.md.

Technologies Used

• Python 3.11+
• pandas – for data manipulation
• seaborn – for statistical data visualization
• matplotlib – for low-level plotting
• Jupyter Notebook – for interactive analysis

Contributing

Contributions are welcome. To contribute:

1. Fork the repository
2. Create a new branch (git checkout -b feature-branch)
3. Make your changes
4. Commit your changes (git commit -m "Add feature")
5. Push to your branch (git push origin feature-branch)
6. Open a pull request

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contact

Kozmik Moore
Email: koz.moore@gmail.com
GitHub: @kozmik-moore
LinkedIn: @kozmik-moore