Bank Customer Churn Predictor

Description + stats

A full-stack bank customer churn predictor application utilizing:

| Name of model | Accuracy | |--------------------------------------|------------| | Decision Tree | 79.13% | | K-Nearest Neighbors (KNN) | 82.00% | | Naive Bayes | 82.25% | | Random Forest Classifier | 83.75% | | Support Vector Machine (SVM) | 84.13% | | XGBoost Classifier | 84.25% | | XGBoost + SMOTE Classifier | 83.87% | | Voting Classifier | 83.63% | | GPT OSS 120B LLM [OpenAI] | — |

It ingests 4000 entries to predict churn risk with visual insights, AI-generated explanations and emails.

Tech Stack

| Purpose | Technologies | |----------------------|--------------| | Core Tech | | | Frontend & Framework | | | Backend + DB | | | Other Libraries | |

Database + authentication

https://github.com/user-attachments/assets/9aea195d-7073-4813-9a08-3648790b84ce

Quick Start

Clone repo
```
pip install -r requirements.txt
```
Store below in a secrets.toml file under a .streamlit folder :

GROQAPIKEY = ""
SUPABASE_URL = ""
SUPABASESERVICEROLE_KEY= ""
EMAILJSPUBLICKEY= ""
EMAILJSTEMPLATEID= ""
EMAILJSSERVICEID= ""

```
streamlit run main.py
```

Research references + custom dataset badge-links

License

This project is licensed under the MIT License.

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