https://github.com/simhayn/emergency-calls

https://github.com/simhayn/emergency-calls

Last synced: about 21 hours ago
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• Host: GitHub
• URL: https://github.com/simhayn/emergency-calls
• Owner: simhayn
• License: mit
• Created: 2024-06-16T11:11:04.000Z (5 months ago)
• Default Branch: main
• Last Pushed: 2024-07-16T10:10:34.000Z (4 months ago)
• Last Synced: 2024-07-16T12:43:55.194Z (4 months ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 5.85 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# 911 Emergency Calls 

## Project Overview

Analyze 911 emergency call data to gain insights into call volumes, reasons for calls, and temporal patterns. The project focuses on exploratory data analysis (EDA) using Python and various data visualization libraries.

## Table of Contents

1. [Project Description](#project-description)

2. [Data Source](#data-source)

4. [Usage](#usage)

5. [Features](#features)

6. [Contributing](#contributing)

7. [License](#license)

8. [Contact](#contact)

## Project Description

This project uses a dataset from Kaggle containing emergency call information, including details such as the type of emergency, location coordinates, and timestamps. The goals of this project include:

- Analyzing trends and patterns in emergency call data.

- Visualizing call volumes over time, by location, and by type of emergency.

- Exploring correlations and insights to aid emergency response planning.

## Data Source

The dataset used is sourced from [Kaggle's 911 Calls dataset](https://www.kaggle.com/mchirico/montcoalert) (Provided in the file "911.csv"). It includes information such as:

- Latitude and longitude of the emergency call

- Description of the emergency

- Zip code and township of the emergency location

- Timestamp of the call

- Type of emergency (e.g., EMS, Fire, Traffic)

  

## Usage

Open the notebook and view the project: [notebook](emergency-calls.ipynb)

## Features

- Data Loading and Cleaning: Clean and preprocess the dataset.

- Exploratory Data Analysis (EDA): Analyze call volumes, trends, and geographical distribution.

- Visualization: Use matplotlib, seaborn, and plotly for visualizing emergency call data.

- Insights: Gain insights to aid emergency response planning.

## Contributing

Contributions are welcome! Fork the repository and submit a pull request.

Please follow the [Contributor Covenant Code of Conduct](https://www.contributor-covenant.org/version/2/0/code_of_conduct/).

## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.

## Contact

For any questions or inquiries, please contact Nataly Yakobov at [[email protected]](mailto:[email protected]).

Thank you for your interest in my project!