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https://github.com/imsanjoykb/banking-churn-prediction-ann-based
This repository will have all the necessary files for machine learning and deep learning based Banking Churn Prediction ANN model which will analyze tha probablity for a customer to leave the bank services in near future.
https://github.com/imsanjoykb/banking-churn-prediction-ann-based
ann deep-learning deployment flask machine-learning neural-networ neural-network python
Last synced: 26 days ago
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This repository will have all the necessary files for machine learning and deep learning based Banking Churn Prediction ANN model which will analyze tha probablity for a customer to leave the bank services in near future.
- Host: GitHub
- URL: https://github.com/imsanjoykb/banking-churn-prediction-ann-based
- Owner: imsanjoykb
- License: mit
- Created: 2020-10-25T04:38:29.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2020-11-09T16:13:01.000Z (almost 4 years ago)
- Last Synced: 2023-03-04T22:23:47.132Z (over 1 year ago)
- Topics: ann, deep-learning, deployment, flask, machine-learning, neural-networ, neural-network, python
- Language: Python
- Homepage:
- Size: 517 KB
- Stars: 3
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# ANN-based-Banking-Churn-Prediction
- This repository will have complete machine learning and deep learning based banking churn prediction ANN model which will analyze tha probablity for a customer to leave.
- The project was deployed on Google Cloud Platform as well as completely tested on Localhost.
## Project Description
### Welcome Screen
- The main welcome screen is made in **HTML5** and **CSS3** with a basic and simple design.
- Here is the main screen for the **Bank Churn Prediction** Interface for the bank admin.
### Bank Admin Input
- The bank employee has to enter the details of the customer whose churn they want to analyze.
- Below is the screenshot of the input being filled by bank employee.
## Analysis and Accuracy
- The Prediction engine is built over a deep **Artificial Neural Network** backed with **[Keras](https://www.tensorflow.org/guide/keras)**.
- I have achieved an accuracy of around **~85%** on both training and testing data.
- The ANN is trained over K-fold cross validation testing over 10 rounds to find if it was underfit or overfit over the data based on the variance betweent the accuracies of the 10 rotations.
- The model is Tuned over the Hyerparametes to find the best **batch_size**, **epoch** and **optimizer** for generating the best possible combination for best fit model.
## Deployment and Production
- The **API interfacing** for the deplyment on [Localhost](http://localhost:8080/index) is done using [Flask](https://flask.palletsprojects.com/en/1.1.x/).
- The server is run on Local system during the staging of the project.
- Older deployment was done on [Google Cloud Platform](https://cloud.google.com/)
- Recently, the final deployment was done on **Heloku** platform and can be accessed from the link below.
- LINK: [https://banking-churn-pediction](https://banking-churn-pediction.herokuapp.com)
## Predictions
- The final prediction of the model is the percentage of churn for that customer.
- The prediction signifies the chances of the customer to leave the services of the bank which makes the bank to _focus more on such such customers_ and try to retain them using **[Sales and Marketing strategies](https://github.com/paras009/Sales-and-Marketing-Analytics)** about which I have worked in this [GitHub](https://github.com/paras009/Sales-and-Marketing-Analytics) module.
## Contribution
- The project is built completely by Sanjoy Biswas.
Connect on [LinkedIn](https://www.linkedin.com/in/imsanjoykb)
Follow on [Github](https://github.com/imsanjoykb)
#### Thank You!