# Introduction In this challenge, we will build a machine learning classification model to predict the credit card holder's risk status based on their historical billing information, age, gender, education level, and marital status. The objective is to achieve an accuracy of at least 0.8 on the testing dataset. We will be using the provided training dataset to train the model and then make predictions on the testing dataset. Therefore, we need to preprocess the data using Pandas and utilize the classification prediction models provided by scikit-learn. The final prediction results should be stored in the `credit_risk_pred.csv` data file, where each record corresponds to a predicted risk status.
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