Credit loan risk model project assigned by Pregrad

🚀 Credit Loan Risk Model Predictor | Machine Learning Project Demo

In this video, I present my Credit Loan Risk Predictor, a Machine Learning project designed to predict whether a loan applicant is likely to be low risk or high risk based on their financial and personal information. This project demonstrates the complete ML workflow, including data preprocessing, model training, evaluation, and deployment using an interactive web application.

🔍 Project Overview:
The goal of this project is to help financial institutions make better loan approval decisions by predicting credit risk using historical loan data. The model analyzes multiple features such as income, loan amount, credit history, employment status, and other financial indicators to determine the risk level of an applicant.

🧠 Technologies and Tools Used:
• Python – Core programming language
• Pandas & NumPy – Data cleaning and preprocessing
• Scikit-learn – Machine learning model building
• Random Forest Classifier – Risk prediction model
• Joblib – Model saving and loading
• Streamlit – Web app interface for user interaction

⚙️ Key Features:
• Data preprocessing and feature engineering
• Machine learning model training and testing
• Accurate credit risk prediction
• Interactive Streamlit web application
• Real-time prediction based on user input

📊 Model Workflow Covered in Video:

1. Data collection and preprocessing
2. Feature selection and encoding
3. Model training using Random Forest
4. Model evaluation and accuracy testing
5. Saving the trained model using Joblib
6. Building and running the Streamlit app
7. Live prediction demonstration

🎯 Purpose of the Project:
This project demonstrates how machine learning can be applied in finance to automate and improve loan approval decisions, reduce financial risk, and support smarter decision-making.

📌 Ideal for:
• Machine Learning beginners
• Data Science students
• Portfolio projects
• Interview demonstrations

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Видео Credit loan risk model project assigned by Pregrad канала Tech Field