https://github.com/shubhamahobia/ann-classification
The project involves developing a machine learning model to predict customer churn using Artificial Neural Networks (ANN). Customer churn refers to the loss of clients or subscribers. Accurately predicting churn helps businesses implement strategies to retain customers, thereby increasing profitability.
https://github.com/shubhamahobia/ann-classification
aritificial-intelligence artificial-neural-networks churn-prediction churn-rates classification deep-learning deep-neural-networks machine-learning
Last synced: 5 months ago
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The project involves developing a machine learning model to predict customer churn using Artificial Neural Networks (ANN). Customer churn refers to the loss of clients or subscribers. Accurately predicting churn helps businesses implement strategies to retain customers, thereby increasing profitability.
- Host: GitHub
- URL: https://github.com/shubhamahobia/ann-classification
- Owner: ShubhaMahobia
- Created: 2024-08-19T10:45:52.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-08-19T11:20:37.000Z (over 1 year ago)
- Last Synced: 2024-11-07T05:12:47.580Z (over 1 year ago)
- Topics: aritificial-intelligence, artificial-neural-networks, churn-prediction, churn-rates, classification, deep-learning, deep-neural-networks, machine-learning
- Language: Jupyter Notebook
- Homepage: https://ann-classification-churnrate-qjzu6hqbh4epstlaylkypc.streamlit.app/
- Size: 415 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# CHURN RATE PREDICTION - ANN
The project involves developing a machine learning model to predict customer churn using Artificial Neural Networks (ANN). Customer churn refers to the loss of clients or subscribers. Accurately predicting churn helps businesses implement strategies to retain customers, thereby increasing profitability.
## Live Deployement
Live Demo - https://ann-classification-churnrate-qjzu6hqbh4epstlaylkypc.streamlit.app/
## Run Locally
1. Clone this repo into your system.
2. Create virtual environment using the command -
```bash
python -m venv env
```
3. Now install all the packages which are listed in requirements.txt
```bash
pip install -r requirements.txt
```
4. Now run all the cell in the Experiments.ipynb And Prediction.ipynb as per your need.
5. To run on streamlit -
```bash
streamlit run ChurnRate.py
```
## Tech Stack
**Frontend Client:** Streamlit Services
**Model Used:** Artificial Neural Network - ANN
**Dataset Used:** Custom
## Feedback
If you have any feedback or just to say Hi!, please reach out to me at mahobiashubham4@gmail.com
## Authors
- [@ShubhaMahobia](https://github.com/ShubhaMahobia)