- 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% |
| Qwen3 32B LLM [OpenAI] | — |
- It ingests
4000entries to predict churn risk with visual insights, AI-generated explanations and emails.
| Purpose | Technologies |
|---|---|
| Core Tech |     |
| Frontend & Framework |     |
| Backend + DB |     |
| Other Libraries |     |
DB_Backend_Demo.mp4
- Clone repo
-
pip install -r requirements.txt - Store below in a secrets.toml file under a .streamlit folder :
GROQ_API_KEY = ""
SUPABASE_URL = ""
SUPABASE_SERVICE_ROLE_KEY= ""
EMAILJS_PUBLIC_KEY= ""
EMAILJS_TEMPLATE_ID= ""
EMAILJS_SERVICE_ID= ""
-
streamlit run main.py
This project is licensed under the MIT License.
