Python for Data Science: Predicting Credit Card Default Exercise

Introduction
Prerequisites
Setup
Overview
Step 1: Loading the Data
Step 2: Exploratory Data Analysis
Step 3: Data Preprocessing
Step 4: Model Building
Step 5: Model Evaluation
Conclusion

Introduction

In this tutorial, we will learn how to predict credit card default using Python and various data science techniques. The goal is to build a predictive model that can accurately predict whether a credit card holder will default in making their payments.

By the end of this tutorial, you will have a clear understanding of the data science workflow involved in building a prediction model, from loading the data to evaluating the model’s performance.

Prerequisites

Before you begin, make sure you have the following prerequisites:

Basic knowledge of Python programming language
Familiarity with pandas, numpy, and scikit-learn libraries
Understanding of data preprocessing and exploratory data analysis concepts
Jupyter Notebook or any other Python IDE installed on your system

Setup

To get started, follow these steps to set up your Python environment:

Install Python: Download and install the latest version of Python from the official website (https://www.python.org/).
Install Required Libraries: Open your terminal or command prompt and run the following command to install the necessary libraries:
```
 pip install pandas numpy scikit-learn
```
Download the Dataset: Download the credit card default dataset from [insert_dataset_link_here].

With the prerequisites and setup complete, we can now proceed with the tutorial.

Overview

Here is an outline of the steps we will follow to predict credit card default:

Loading the Data: We will load the credit card default dataset into a pandas DataFrame.
Exploratory Data Analysis: We will perform some initial analysis on the dataset to gain insights into the data.
Data Preprocessing: We will clean and preprocess the data to prepare it for model building.
Model Building: We will build a predictive model using scikit-learn’s machine learning algorithms.
Model Evaluation: We will evaluate the performance of the model using suitable evaluation metrics.

Let’s dive into each step in detail.

Step 1: Loading the Data

First, we need to load the credit card default dataset into a pandas DataFrame. The dataset contains information about credit card holders, including various attributes such as limit balance, age, education, and payment history.

To load the data, follow these steps:

Import the necessary libraries:
```
 import pandas as pd
```
Load the dataset into a DataFrame:
```
 data = pd.read_csv('credit_card_default.csv')
```
Replace credit_card_default.csv with the actual file path of the dataset on your system.

Step 2: Exploratory Data Analysis

Before diving into model building, it is essential to understand the data and gain insights from it. Exploratory Data Analysis (EDA) helps us understand the structure and characteristics of the dataset.

Here are a few EDA tasks you can perform:

Check the dimensions of the dataset:
```
 print(data.shape)
```
This will print the number of rows and columns in the dataset.
Explore the first few rows of the dataset:
```
 print(data.head())
```
This will display the first few rows of the dataset.
Check the data types of the columns:
```
 print(data.dtypes)
```
This will display the data types of each column.
Check for missing values:
```
 print(data.isnull().sum())
```
This will display the number of missing values in each column.

Performing these tasks will give you a better understanding of the dataset and help you identify any data quality issues.

Step 3: Data Preprocessing

Data preprocessing involves cleaning and transforming the data to prepare it for model building. It includes tasks such as handling missing values, encoding categorical variables, and scaling numerical features.

Here are a few preprocessing tasks you can perform:

Handling missing values:
```
 data.dropna(inplace=True)
```
This will drop any rows with missing values from the dataset.

Encoding categorical variables:

 data = pd.get_dummies(data, columns=['education', 'marital_status'], drop_first=True)

This will encode categorical variables using one-hot encoding.

Scaling numerical features:

 from sklearn.preprocessing import StandardScaler
	
 scaler = StandardScaler()
 data[['limit_balance', 'age']] = scaler.fit_transform(data[['limit_balance', 'age']])

This will standardize the numerical features using the StandardScaler from scikit-learn.

Performing these preprocessing tasks will ensure that the data is in a suitable format for model building.

Step 4: Model Building

Now that we have preprocessed the data, we can proceed with building a predictive model. In this tutorial, we will use the logistic regression algorithm to predict credit card default.

Here are the steps to build a logistic regression model:

Split the data into input features and target variable:

 X = data.drop('default', axis=1)
 y = data['default']

Split the data into training and testing sets:

 from sklearn.model_selection import train_test_split
	
 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

This will split the data into a 80-20 train-test split.

Train the logistic regression model:

 from sklearn.linear_model import LogisticRegression
	
 model = LogisticRegression()
 model.fit(X_train, y_train)

This will train the logistic regression model on the training data.

Step 5: Model Evaluation

Once the model is trained, we need to evaluate its performance on unseen data. There are various evaluation metrics available to assess the model’s performance, such as accuracy, precision, recall, and F1 score.

Here is an example of evaluating the model using accuracy: ```python from sklearn.metrics import accuracy_score

y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)
``` This will print the accuracy of the model on the test set.

You can explore other evaluation metrics and choose the ones that are most appropriate for your problem.

Conclusion

In this tutorial, we learned how to predict credit card default using Python and various data science techniques. We covered the entire workflow, from loading the data to evaluating the model’s performance.

By following the steps outlined in this tutorial, you should now have a good understanding of the data science workflow involved in building a predictive model for credit card default prediction.

Remember, building and evaluating predictive models is an iterative process, and there is always room for improvement. Keep experimenting with different algorithms, features, and evaluation metrics to improve the model’s performance.

Published: 30 January 2020