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https://github.com/venkat-a/data-analysis-and-visualization-dashboard

Development and deployment of a Plotly Dash dashboard on Google Cloud Platform (GCP) and Heroku
https://github.com/venkat-a/data-analysis-and-visualization-dashboard

dashboard data-mining deployment descriptive-statistics gcp heroku-deployment modularization pipenv plotly-dash statical-analysis visualization

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Development and deployment of a Plotly Dash dashboard on Google Cloud Platform (GCP) and Heroku

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# Data Analysis and Visualization Dashboard

Development and deployment of a Plotly Dash dashboard on Google Cloud Platform (GCP) and Heroku



## Project Overview



This project showcases a comprehensive approach to data cleaning, analysis, and visualization, deployed as an interactive dashboard using Plotly Dash. The core objective is to reveal insights through at least 20 different types of correlations, frequencies, and relationships between various variables in the dataset. This analysis is presented through meaningful plots and narrations, making extensive use of data cleaning techniques and visualization strategies.



## Modules



- **clean_data.py**: A module dedicated to preparing the dataset for analysis. This involves at least four steps of data cleaning to ensure the data is accurate and suitable for visualization.

- **visualization.py**: Handles the creation of visual representations of the dataset's underlying patterns and relationships. This module generates plots with clear titles, labels, and no overlapping, adhering to the project's visualization standards.

- **app.py**: Integrates the data cleaning and visualization modules into a Plotly Dash application. This serves as the entry point for the dashboard, orchestrating the data pipeline and user interface.



## Features



- **Interactive Data Visualizations**: Utilizes Plotly Dash to create interactive plots that allow users to explore various aspects of the dataset.

- **Comprehensive Data Cleaning**: Implements thorough preprocessing steps to ensure the data is of high quality for analysis.

- **Insightful Analysis**: Offers deep insights into the data through various types of correlations, frequencies, and relationships between variables.

- **Deployment**: The dashboard is deployed on both Google Cloud Platform (GCP) and Heroku, showcasing the flexibility and accessibility of the application.



## Deployment



The dashboard is accessible via URLs for both GCP and Heroku deployments. This ensures that users can interact with the dashboard from anywhere, without needing to run the code locally.



- GCP URL: [Expired]

- Heroku URL: [Expired]



## Getting Started



To run the dashboard locally or contribute to its development, follow these steps:



1. Clone the repository to your local machine.

2. Ensure you have Python installed, along with the necessary libraries listed in `requirements.txt`.

3. Navigate to the project directory and install dependencies using `pip install -r requirements.txt`.

4. Run `app.py` to start the local server. Access the dashboard through your web browser at `http://localhost:8050`.



## Contributing



We welcome contributions to enhance the dashboard's functionality or improve the data analysis. Please feel free to fork the repository, make your changes, and submit a pull request.



## License



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