This notebook builds a machine learning model to predict if a credit card application will get approved or not based on customer attributes.
The credit card approval dataset is obtained from the UCI Machine Learning Repository. It contains information on 690 applicants including demographic features like Gender, Age, Debt, Income, and attributes like Credit Score.
The main steps involved in building the prediction model are:
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Data inspection: Checking for missing values, data types, and statistical properties. Missing values are imputed accordingly.
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Preprocessing: Converting categorical features to dummy variables, splitting them into train and test sets, and feature scaling using the pandas get_dummies method.
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Model Building: Fitting a Logistic Regression model on the training data.
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Evaluation: Evaluating classifier performance using accuracy score and confusion matrix.
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Tuning: Gridsearching over Logistic Regression hyperparameters like tol and max_iter to find the best model.
The final Logistic Regression model achieves 100% accuracy in predicting credit card approvals on the test set. The confusion matrix shows perfect classification for both approved and denied status.
The analysis provides a simple machine-learning workflow for a classification problem. The final model demonstrates excellent predictive power