{"repo":"Soumilgit/XYZ-Bank-Customer-Churn-Predictor","free":true,"listed":false,"github":"https://github.com/Soumilgit/XYZ-Bank-Customer-Churn-Predictor","clone":"git clone https://github.com/Soumilgit/XYZ-Bank-Customer-Churn-Predictor.git","description":"Modular full-stack ML project leveraging Groq API, Streamlit, Supabase, JSON, SciPy, SciKit-Learn, Plotly & EmailJS, alongside libraries - NumPy, Pandas, Utils, OS, Base64, Re, Pillow & DateTime.","language":"Jupyter Notebook","stars":33,"topics":["jupyter-notebook","python","streamlit","naive-bayes-classifier","random-forest-classifier","smote","xgboost-classifier","decision-tree-classifier","svm-classifier","openai"],"license":"MIT","category":"ai-agents","readme_excerpt":"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 Quick Start 1. Clone repo : 3. Install required libraries : 5. Store below in a secrets.toml file under a .streamlit folder : 4. Run the application : Research references + custom dataset badge-links License This project is licensed under the MIT License.","default_branch":null,"files":null,"tree":[],"storefront":"/r/Soumilgit","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/Soumilgit/XYZ-Bank-Customer-Churn-Predictor/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}