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https://github.com/tushar2704/bank-german-customer-segmentation
This project aims to revolutionize the way banks market to their customers by leveraging machine learning techniques to segment customers based on their account history, credit scores, and demographics.
https://github.com/tushar2704/bank-german-customer-segmentation
artificial-intelligence banking clustering customer-segmentation data-science machine-learning python streamlit-tushar2704 tushar2704
Last synced: 19 days ago
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This project aims to revolutionize the way banks market to their customers by leveraging machine learning techniques to segment customers based on their account history, credit scores, and demographics.
- Host: GitHub
- URL: https://github.com/tushar2704/bank-german-customer-segmentation
- Owner: tushar2704
- License: apache-2.0
- Created: 2023-08-11T13:58:40.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-08-23T17:03:28.000Z (over 1 year ago)
- Last Synced: 2024-05-11T05:53:45.084Z (8 months ago)
- Topics: artificial-intelligence, banking, clustering, customer-segmentation, data-science, machine-learning, python, streamlit-tushar2704, tushar2704
- Language: Jupyter Notebook
- Homepage: https://tushar-aggarwal.com
- Size: 3.29 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Bank German Customer Segmentation & Classification Machine Learning Model as Flask API
Welcome to the Bank Customer Segmentation project! This project aims to revolutionize the way banks market to their customers by leveraging machine learning techniques to segment customers based on their account history, credit scores, and demographics. By gaining insights into distinct customer groups, banks can tailor their marketing and customer acquisition strategies for optimal results.
## Project Overview
In this project, we build a machine learning model using algorithms like K-Means Clustering, Hierarchical Clustering, and Gaussian Mixture Models to segment bank customers using the Bank Customer Segmentation dataset. This allows us to uncover meaningful patterns in customer behavior and preferences, leading to data-driven decisions for marketing strategies.
## Getting Started
Follow these steps to get started with the project:
1. **Clone the Repository:** Clone this repository to your local machine using the following command:
```
git clone https://github.com/tushar2704/Bank-German-Customer-Segmentation.git
```
2. **Install Dependencies:** Install the required dependencies by running:
```
pip install -r requirements.txt
```
3. **Dataset:** Download the Bank Customer Segmentation dataset and place it in the `data` directory.
4. **Data Preprocessing:** Use the provided Jupyter notebooks to preprocess the dataset, handle missing values, and transform features for analysis.
5. **Algorithm Implementation:** Implement the machine learning algorithms like K-Means Clustering, Hierarchical Clustering, and Gaussian Mixture Models using the notebooks in the `notebooks` directory.
6. **Segment Profiling:** Analyze the characteristics of each segment to derive insights. Visualizations and statistics can be generated using the tools provided in the notebooks.
7. **Strategy Formulation:** Based on the insights gained from segment profiling, develop personalized marketing and customer acquisition strategies for each segment.
# Classification ML Model as Flask API on German Credit Risk Data
1. First create the ML model by running `python create_model.py`.
2. Then, run `python app.py` to start the server.
The url to access the app will be provided at the console output.
## Project Structure
The project repository is organized as follows:
```
├── LICENSE
├── README.md <- README .
├── notebooks <- Folder containing the final reports/results of this project.
│ │
│ └── bank_german_customer_segmentation.py <- Final notebook for the project.
├── reports <- Folder containing the final reports/results of this project.
│ │
│ └── Pizza_Sales_Report.pdf <- Final analysis report in PDF.
│
├── src <- Source for this project.
│ │
│ └── data <- Datasets used and collected for this project.
| └── model <- Model.
```
## License
This project is licensed under the [MIT License](LICENSE).
## Author
- ©2023 Tushar Aggarwal. All rights reserved
- [LinkedIn](https://www.linkedin.com/in/tusharaggarwalinseec/)
- [Medium](https://medium.com/@tushar_aggarwal)
- [Tushar-Aggarwal.com](https://www.tushar-aggarwal.com/)
- [New Kaggle](https://www.kaggle.com/tagg27)
## Contact me!
If you have any questions, suggestions, or just want to say hello, you can reach out to us at [Tushar Aggarwal](mailto:[email protected]). We would love to hear from you!