Bank Customer Churn Predictor Architecture 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% Qwen3 32B 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 DB_Backend_Demo.mp4 Quick Start 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 Research references + custom dataset badge-links License This project is licensed under the MIT License.
