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.
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.
Follow these steps to get started with the project:
-
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 -
Install Dependencies: Install the required dependencies by running:
pip install -r requirements.txt -
Dataset: Download the Bank Customer Segmentation dataset and place it in the
datadirectory. -
Data Preprocessing: Use the provided Jupyter notebooks to preprocess the dataset, handle missing values, and transform features for analysis.
-
Algorithm Implementation: Implement the machine learning algorithms like K-Means Clustering, Hierarchical Clustering, and Gaussian Mixture Models using the notebooks in the
notebooksdirectory. -
Segment Profiling: Analyze the characteristics of each segment to derive insights. Visualizations and statistics can be generated using the tools provided in the notebooks.
-
Strategy Formulation: Based on the insights gained from segment profiling, develop personalized marketing and customer acquisition strategies for each segment.
- First create the ML model by running
python create_model.py. - Then, run
python app.pyto start the server.
The url to access the app will be provided at the console output.
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.
This project is licensed under the MIT License.
- ©2023 Tushar Aggarwal. All rights reserved
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- Tushar-Aggarwal.com
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If you have any questions, suggestions, or just want to say hello, you can reach out to us at Tushar Aggarwal. We would love to hear from you!