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https://github.com/ashraf-khabar/bank-marketing-data-analysis
This project is focused on analyzing bank marketing data using PyTorch, pandas, numpy, and scikit-learn. The goal is to build a predictive model that can help identify potential customers who are more likely to subscribe to a bank's term deposit.
https://github.com/ashraf-khabar/bank-marketing-data-analysis
data-cleaning data-science data-visualization dataset deep-learning deep-neural-networks feedforward-neural-network learning neural-networks numpy pandas python pytorch sklearn
Last synced: 13 days ago
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This project is focused on analyzing bank marketing data using PyTorch, pandas, numpy, and scikit-learn. The goal is to build a predictive model that can help identify potential customers who are more likely to subscribe to a bank's term deposit.
- Host: GitHub
- URL: https://github.com/ashraf-khabar/bank-marketing-data-analysis
- Owner: Ashraf-Khabar
- Created: 2023-07-08T22:33:55.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-07-12T00:38:51.000Z (over 1 year ago)
- Last Synced: 2024-11-20T12:04:23.372Z (2 months ago)
- Topics: data-cleaning, data-science, data-visualization, dataset, deep-learning, deep-neural-networks, feedforward-neural-network, learning, neural-networks, numpy, pandas, python, pytorch, sklearn
- Language: Jupyter Notebook
- Homepage:
- Size: 2.35 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Support: support paper/Mathematical_Aspect_of_Deep_Learning.pdf
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README
# Bank Marketing Data Analysis
This project is focused on analyzing bank marketing data using PyTorch, pandas, numpy, and scikit-learn. The goal is to build a predictive model that can help identify potential customers who are more likely to subscribe to a bank's term deposit.
## Table of Contents
- [Overview](#overview)
- [Installation](#installation)
- [Usage](#usage)
- [Dataset](#dataset)
- [Project Structure](#project-structure)
- [Results](#results)
- [Contributing](#contributing)
- [License](#license)
## Overview
The main objective of this project is to perform data analysis on a bank marketing dataset and develop a predictive model using PyTorch and scikit-learn. The project involves various stages, including data preprocessing, exploratory data analysis, feature engineering, model training, and evaluation.
## Installation
To run this project locally, follow these steps:
1. Clone the repository:
`git clone `
2. Install the required dependencies. Ensure you have Python 3 installed. Run the following command in your terminal:
`pip install -r requirements.txt`
## Usage
1. Navigate to the project directory:
`cd bank-marketing-data-analysis`
2. Run the main analysis script:
`python main.py`
This will execute the data analysis pipeline, including data preprocessing, feature engineering, model training, and evaluation.
## Dataset
The bank marketing dataset used in this project is available in the `data` directory. It contains information about various features such as age, job, marital status, education, etc., of individuals who were contacted for a marketing campaign. The target variable is whether the individual subscribed to a term deposit or not.
Dataset link : [Kaggle link](https://www.kaggle.com/datasets/janiobachmann/bank-marketing-dataset?resource=download)
## Project Structure
The project structure is organized as follows:
- `data/`: Contains the bank marketing dataset (`bank.csv`).
- `models/`: Contains the PyTorch model implementation (`model.ipynb`, `TF.py`, `NN.py`).
## Results
After running the analysis script, the project will generate various outputs, including:
- Model performance metrics (accuracy, precision, recall, F1-score, etc.)
- Visualizations of data insights and model evaluation results
These results will be displayed in the console and saved in the `results/` directory.
## Contributing
Contributions are welcome! If you would like to contribute to this project, please follow these steps:
1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Make your changes and commit them.
4. Push your changes to your forked repository.
5. Submit a pull request detailing your changes.
## License
This project is licensed under the [MIT License](LICENSE).`