https://github.com/rickydoan/machine-learning-risk-model-prediction-classification

This project leverages machine learning to provide insights into loan and credit risk. By analyzing user-provided financial data, it predicts the likelihood of loan default, generates a credit score, and assigns a risk rating. Designed to assist financial institutions and individuals in making informed decisions
https://github.com/rickydoan/machine-learning-risk-model-prediction-classification

classification joblib machine-learning numpy pandas python sklearn-library streamlit

Last synced: about 1 month ago
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This project leverages machine learning to provide insights into loan and credit risk. By analyzing user-provided financial data, it predicts the likelihood of loan default, generates a credit score, and assigns a risk rating. Designed to assist financial institutions and individuals in making informed decisions

• Host: GitHub
• URL: https://github.com/rickydoan/machine-learning-risk-model-prediction-classification
• Owner: RickyDoan
• License: apache-2.0
• Created: 2024-12-08T15:58:29.000Z (about 1 month ago)
• Default Branch: main
• Last Pushed: 2024-12-10T10:09:26.000Z (about 1 month ago)
• Last Synced: 2024-12-10T10:36:24.218Z (about 1 month ago)
• Topics: classification, joblib, machine-learning, numpy, pandas, python, sklearn-library, streamlit
• Language: Jupyter Notebook
• Homepage: https://ricky-ml-risk-model-prediction.streamlit.app/
• Size: 1.49 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Machine_learning_Risk_Model_Prediction

* Play with app via url : https://ricky-ml-risk-model-prediction.streamlit.app/

### This repository contains a Loan and Credit Risk Analysis Tool built using machine learning and Streamlit to predict:

* Probability of Loan Default

* Credit Score

* Risk Rating (Poor, Average, Good, Excellent)

### Key Features

#### 1.Machine Learning Model:

* Training logistic regression model optimized for accuracy.

* Predicts risk metrics based on input financial data.

#### 2.Data Preprocessing:

* Feature engineering : Determine VIF, Corr, WOE & IV .

* Scalable preprocessing pipeline with one-hot encoding and scaling.

####  3.User-Friendly Interface:

* Intuitive sliders and input fields for data entry.

* Real-time predictions displayed dynamically.

#### 4.Tech Stack

* Machine Learning: Scikit-learn, NumPy, Pandas

* Web Framework: Streamlit

* Model Persistence: Joblib

#### 5.How to Use

* Clone the repository.

* Install dependencies from requirements.txt.

* Run the app using streamlit run app.py.

* Feel free to explore and contribute! 🚀

#MachineLearning #CreditRisk #Streamlit