This project uses machine learning to predict whether a person is eligible for a credit card based on their financial and personal background. It’s designed to help banks and financial institutions make faster, data-driven decisions while reducing manual effort and human error.
📘 What This Project Is About Before banks give someone a credit card, they usually check things like income, employment, past loan behavior, and spending habits. Instead of doing this manually, this project uses a smart algorithm that learns from past data to decide who is likely to be a responsible credit card user.
The goal is to build a reliable model that takes a person’s information and returns:
✅ Eligible — they are likely to manage the credit card well
❌ Not Eligible — they may pose a risk
📊 What Kind of Data Is Used? The model is trained on a dataset that includes:
Age
Occupation
Annual income
Number of credit cards or loans
Payment history (on-time or missed)
Spending and transaction habits
This data helps the model understand what good vs. risky profiles look like.
🛠️ Tools and Technologies Python — the main programming language
Pandas & NumPy — for cleaning and handling data
Matplotlib & Seaborn — for data visualization
Scikit-learn — for building and testing the machine learning models
Jupyter Notebook — to keep the code organized and interactive
🔍 What the Project Does Step-by-Step Data Preprocessing: The data is cleaned — missing values handled, categories converted to numbers, and features scaled.
Data Visualization: Charts and graphs are used to find important trends (e.g., people with high income and no missed payments are usually eligible).
Model Building: A classification algorithm (like Random Forest or Logistic Regression) is trained using the cleaned data.
Prediction: Once trained, the model can predict eligibility for new users based on their information.
Evaluation: The model is tested to make sure it works well on new data using accuracy, precision, recall, and other metrics.