This repository contains a machine learning model for predicting default payment in credit card clients. The model is built using a dataset containing various features of credit card clients and their default payment status.
The dataset used for training and evaluation contains the following columns:
ID: Client IDLIMIT_BAL: Credit limitSEX: Gender (1 = male, 2 = female)EDUCATION: Education level (1 = graduate school, 2 = university, 3 = high school, 4 = others)MARRIAGE: Marital status (1 = married, 2 = single, 3 = others)AGE: Age of the clientPAY_0 to PAY_6: History of past payment status (PAY_0 = September 2005, PAY_2 = August 2005, ..., PAY_6 = April 2005)BILL_AMT1 to BILL_AMT6: Bill statement amount (BILL_AMT1 = amount of bill statement in September 2005, ..., BILL_AMT6 = amount of bill statement in April 2005)PAY_AMT1 to PAY_AMT6: Amount of previous payment (PAY_AMT1 = amount paid in September 2005, ..., PAY_AMT6 = amount paid in April 2005)default.payment.next.month: Default payment status (1 = default, 0 = non-default)
The model is trained using the following steps:
- Data Preprocessing: The dataset is preprocessed to handle missing values, encode categorical variables, and scale numerical features.
- Feature Selection: Relevant features are selected for training the model.
- Model Selection: Various machine learning algorithms are evaluated, and the best performing algorithm is selected.
- Model Training: The selected algorithm is trained on the preprocessed dataset.
- Model Evaluation: The trained model is evaluated using appropriate evaluation metrics to assess its performance.
To use the trained model for prediction:
- Clone the repository to your local machine.
- Load the trained model using the provided file (model.pkl).
- Prepare input data with the same features used during model training.
- Use the loaded model to make predictions on the input data.