💳 Credit Risk Prediction

This project predicts a customer's credit risk level using Machine Learning.

The goal is to classify customers based on their financial and demographic information into different credit score categories.

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🚀 Project Overview

Banks and financial institutions need to assess whether a customer is risky before approving loans.

This project builds a machine learning model to predict:

• Low Risk
• Average Risk
• High Risk

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🛠️ Tech Stack

• Python
• Pandas
• NumPy
• Scikit-learn
• Joblib

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📁 Project Structure

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📊 Workflow

1️⃣ Data Preprocessing

• Clean column names
• Clean target column
• Encode categorical features
• Split features (X) and target (y)

2️⃣ Model Training

• Train/Test split (80/20)
• Random Forest Classifier
• Evaluate model accuracy
• Save trained model

3️⃣ Prediction

• Load saved model
• Align feature columns
• Predict risk category for new customers

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🧠 Model Used

RandomForestClassifier from Scikit-learn.

Why Random Forest?

• Handles categorical data well
• Robust to overfitting
• Works well without heavy tuning

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📈 How to Run

1️⃣ Clone the Repository 2️⃣ Create Virtual Environment (Recommended) 3️⃣ Install requirements 4️⃣ Train the Model 5️⃣ run streamlit app