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https://github.com/prangonghose/analysis_of_bangladesh_economic_complexity

In this project a brief analysis has been done by our team in the export economy of Bangldesh for the past three decades.
https://github.com/prangonghose/analysis_of_bangladesh_economic_complexity

data-analysis data-science data-visualization inequalipy matplotlib pandas plotly

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In this project a brief analysis has been done by our team in the export economy of Bangldesh for the past three decades.

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Analysis of Export Economy of Bangladesh

# 📗 Table of Contents

- [📖 About the Project](#about-project)
- [🛠 Built With](#built-with)
- [Key Features](#key-features)
- [💻 Getting Started](#getting-started)
- [Setup](#setup)
- [Prerequisites](#prerequisites)
- [Install](#install)
- [👥 Authors](#authors)
- [🤝 Contributing](#contributing)
- [🔭 Future Features](#future-features)
- [⭐️ Show your support](#support)
- [🙏 Acknowledgements](#acknowledgements)

# 📖 Project Name

## Analysis of Export Economy of Bangladesh

### About this project

In this project a brief analysis has been done by our team in the export economy of Bangldesh for the past three decades. The data utilized for this project was collected from the website of [Observatory of Economic Complexity (OEC)](https://oec.world/) and [International Monetary Fund](https://www.imf.org/). The thorough investigation has been done with the help of several python libraries including `Pandas, Matplotlib, NumPy, Plotly,` and `IneualiPy`. The detailed [project report](/report/Analysis%20of%20Export%20Economy%20of%20Bangldesh.pdf) and [presentation](/report/Exports%20of%20Bangladesh.pptx) are added to this repository for the understanding of our analysis and the outcome of the project.

## 🛠 Built With

- `Python3`, `Pandas`, `NumPy`, `Matplotlib`, `Plotly`, and `IneualiPy`

### Key Features

- This project shows the data cleaning process using `Pandas`.
- This project collects data from two open source server and makes the data usable for analysis and visualization.
- This project includes detailed visualization with minimal styling for the understanding and demonstration of the insights of the collected data.
- This project also includes the utilization of `GeoJSON` to plot a choropleth map for better visualization.

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## 💻 Getting Started
To clone the repository in local environment try following steps.

### Prerequisites

- [x] A web browser like Google Chrome.
- [x] A code editor like Visual Studio Code with Git.
- [x] `Python3`, `NumPy`, `Pandas`, `matplotlib`, `iequalipy` and `plotly`.

You can check if Git is installed by running the following command in the terminal.
```
$ git --version
```

Likewise for `python` and `pip` for package installation.
```
$ python --version

$ pip install pandas

$ pip install numpy

$ pip install matplotlib

$ pip install inequalipy

$ pip install plotly
```
### Setup

Clone the repository using [this link](https://github.com/PrangonGhose/Analysis_of_Bangladesh_economic_complexity.git).

### Install

In the terminal, go to your file directory and run this command.

```
$ git clone https://github.com/PrangonGhose/Analysis_of_Bangladesh_economic_complexity.git
```
### Get into development

In the terminal, run these commands to get into development.
```
$ cd Analysis_of_Bangladesh_economic_complexity

```

## 👥 Authors

👤 **Prangon Ghose**

- GitHub: [@PrangonGhose](https://github.com/PrangonGhose)
- LinkedIn: [Prangon Ghose](https://www.linkedin.com/in/prangon-ghose/)

👤 **Maiwand Qayomi**

- GitHub: [@Qayomimaiwand](https://github.com/Qayomimaiwand)

👤 **Salamullah**

- GitHub: [@Salaamullah](https://github.com/Salaamullah)

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## 🤝 Contributing

Contributions, issues, and feature requests are welcome! Add suggestions by opening new issues.

Feel free to check the [issues page](https://github.com/PrangonGhose/Analysis_of_Bangladesh_economic_complexity/issues).

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## 🔭 Future Features

- Build a machine learning model to predict the future trade values and biggest investors for each business sector of Bangladesh for the upcoming fiscal years.

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## ⭐️ Show your support

Give a ⭐️ if you like this project!

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## 🙏 Acknowledgments

Authors would like to thank:
- [Observatory of Economic Complexity (OEC)](https://oec.world/)
- [Internation Monetory Fund](https://www.imf.org/)
- Instructor and Supervisor: [Liubov Tupikina](https://www.linkedin.com/in/liubov-tupikina-233139199/)
- Instructor and Supervisor: [Marc Santolini](https://marcsantolini.com/)

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