https://github.com/nourayman10/servicecancellationpredictor
Service Cancellation Predictor Project
https://github.com/nourayman10/servicecancellationpredictor
cart-algorithm desisiontree id3-algorithm jupyter-notebook knn-algorithm logistic-regression pycharm-ide python3 svm-model tkinter-python
Last synced: 5 months ago
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Service Cancellation Predictor Project
- Host: GitHub
- URL: https://github.com/nourayman10/servicecancellationpredictor
- Owner: NourAyman10
- Created: 2022-05-18T11:01:15.000Z (about 4 years ago)
- Default Branch: master
- Last Pushed: 2022-06-03T08:02:31.000Z (about 4 years ago)
- Last Synced: 2025-05-16T05:37:13.478Z (about 1 year ago)
- Topics: cart-algorithm, desisiontree, id3-algorithm, jupyter-notebook, knn-algorithm, logistic-regression, pycharm-ide, python3, svm-model, tkinter-python
- Language: Jupyter Notebook
- Homepage: https://github.com/NourAyman10/ServiceCancellationPredictor
- Size: 10 MB
- Stars: 2
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# ServiceCancellationPredictor
## About Project
Service cancellation is simply when customers leave doing business with an entity.
It involves determining the possibility of customers stopping doing business with an entity.
In other words, if a consumer has purchased a subscription to a particular service, we must determine the likelihood that the customer will leave or cancel the membership.
For many businesses, the ability to predict that a particular customer is at a high risk of cancelling service while there is still time to do something about it is crucial.
whereas the company will attempt to provide some additional functionalities in order to keep the service.
In Machine Learning, foreseeing business-related actions is our core work, and for that we managed to predict user churn with an average accuracy of 78%.
and attractive UI for a better user experience.
Dataset Link: [Service Cancellation Dataset](https://drive.google.com/file/d/1PrE7kY9h8yTawg0Ul0Ij5RFjiSO0hdYk/view)
## Problem and Algorithms
Problem stipulated that based on 20 attributes and 7043 record we should make several models that predict whether a user will cancel his service or not
we applied 4 models :
* Decision Tree (ID3 / CART)
* Logistic Regression
* SVM
* KNN
Models consumed clean data which led to high accuracy:
| Model | Accuracy |
| --- | --- |
| Decision Tree(ID3) | 77.69% |
| Decision Tree(CART) | 74.2784% |
| Logistic Regression | 80.7109% |
| SVM | 79.18% |
| KNN | 74.48% |
## For GUI
### Splash Screen

### Main Screen
#### Empty with no data



#### All data filled


#### When customer cancel service

#### When customer keep service

#### When train button pressed

#### When test button pressed



## Contributors
- [Mohamed-Samy26](https://github.com/Mohamed-Samy26)
- [RedBiscuits](https://github.com/RedBiscuits)
- [KareemHussen](https://github.com/KareemHussen)
- [NourAyman10](https://github.com/NourAyman10)
- [HaneenIbrahim2](https://github.com/HaneenIbrahim2)
- [OmarSherif2](https://github.com/OmarSherif2)
- [Abdelrhman-Sayed70](https://github.com/Abdelrhman-Sayed70)