[This is a template directory for a basic data analysis project, free for you to use.
If you do use it, be sure to thoroughly inspect and update this README, the LICENSE, and the content in the notebook to make it your own.]

Project: [the project's name]

A short description for this project.

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...

This is a portfolio project created to demonstrate 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

└── 📁basic-data-analysis-template
    └── 📁assets
    └── 📁code
        └── 📁utilities
            ├── __init__.py
            ├── config.py
        ├── notebook.ipynb
    └── 📁data
        ├── raw_data.csv
    └── 📁products
        └── 📁images
        ├── report.md
    ├── .gitignore
    ├── LICENSE
    ├── README.md
    └── requirements.txt

Data Source(s)

• File: raw_data.csv
• Source: Some data
• Description: Contains some data.

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/basic-data-analysis-template.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: [email protected]
GitHub: @kozmik-moore
LinkedIn: @kozmik-moore