LightningChart PythonCredit Risk Modeling
TutorialImplementation of a credit risk modeling application with LightningChart Python
Written by a human | Updated on April 23rd, 2025
Credit Risk Modeling in Python
What is credit risk assessment?
Credit risk assessment is the evaluation of the likelihood that a borrower will default on their loan obligations. It is a crucial part of financial risk management, helping institutions minimize losses due to bad loans.
How have financial institutions benefited from machine learning applications for credit risk assessment?
Traditional methods often rely on manual evaluation and simple statistical models, which can be time-consuming and less accurate. Machine learning models, such as logistic regression and random forests, automate the process, providing faster and more accurate predictions by analyzing large datasets and identifying complex patterns.
CatBoost Model for Credit Risk Assessment
We will use several machine learning models, including the CatBoost Classifier, LGBM Classifier, Random Forest Classifier, XGB Classifier, and Stacking Classifier for their simplicity, interpretability, and ability to handle large datasets and complex relationships. However, the best model is the CatBoost Classifier, which will be the primary focus of this project.
CatBoost Classifier is a machine learning (ML) model based on the gradient boosting decision tree (GBDT) framework. This framework is a popular ML technique used for both classification and regression tasks. The CatBoost Classifier stands out due to its powerful, versatile, and efficient performance in classification tasks, especially where the dataset includes categorical data. One of its key advantages is the ability to process categorical data directly without extensive preprocessing, making it particularly useful for credit risk modeling where such data is prevalent.
LightningChart Python
Overview of LightningChart Python
LightningChart is a high-performance charting library designed for real-time data visualization. Its Python wrapper allows for seamless integration with data analysis and machine learning workflows.
Features and chart types to be used in the Project
LightningChart Python offers a variety of chart types, each designed to handle specific types of data visualization needs. In this project, we use the following chart types to visualize stock price prediction data:
- Bar Chart: Used for visualizing categorical data as bars, making it easy to compare different categories side by side.
- Stacked Bar Chart: Allows for visualizing the composition of each category, showing how individual parts contribute to the whole.
- Grouped Bar Chart: Similar to the bar chart, but groups bars together based on additional categories, facilitating comparison within groups.
- Pie Chart: This kind of chart visualizes proportions and percentages between categories by dividing a circle into proportional segments, providing a clear view of category distribution.
- Box Plot: This chart type is used for visualizing data groups through quartiles. It is used to visualize the distribution of data based on statistical measures like quartiles, median, and outliers, providing insights into the data spread and variability.
Performance Characteristics
LightningChart’s performance is unmatched, handling millions of data points with ease and maintaining smooth user interactions. One of the standout aspects of LightningChart Python is its performance. The library is optimized for handling large volumes of data with minimal latency, which is crucial for financial applications where data needs to be processed and visualized in real time to inform trading decisions.
Setting Up Python Environment
Installing Python and Necessary Libraries
To begin with credit risk modeling in Python, you need to set up your development environment. Start by installing Python from the official Python website. LightningChart Python requires Python version 3.10 or higher. To get the documentation and the license, please visit LightningChart Python Website. Once Python is installed, you can use pip, the Python package installer, to install the necessary libraries. LightningChart Python can be installed from the Python Package Index (PyPI) using pip:
pip install lightningcharts random numpy pandas scikit-learn# Importing the libraries
import lightningchart as lc
import random
lc.set_license('my-license-key')
import numpy as np
import pandas as pd
import statsmodels.api as sm
from sklearn.preprocessing import LabelEncoder, StandardScaler
from sklearn.metrics import classification_report, accuracy_score, precision_score, recall_score, f1_score, roc_auc_score
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import ExtraTreesClassifier, StackingClassifier
from scipy.stats import probplot
from feature_engine.outliers import Winsorizer
from feature_engine.selection import DropConstantFeatures, DropDuplicateFeatures
from sklearn.pipeline import Pipeline
from xgboost import XGBClassifier, XGBRFClassifier
from catboost import CatBoostClassifier
from lightgbm import LGBMClassifier
from imblearn.over_sampling import BorderlineSMOTE
from collections import Counter
from yellowbrick.classifier import ClassPredictionError
from feature_engine.selection import DropCorrelatedFeaturesOverview of Libraries Used
- LightningChart: For advanced data visualization capabilities.
- NumPy: A fundamental package for numerical computation in Python.
- Pandas: Essential for data manipulation and analysis.
- Scikit-learn: A robust library for machine learning, providing tools for data mining and data analysis.
- Scikit-learn: Data mining and data analysis.
Setting Up Your Development Environment
Set up an Integrated Development Environment (IDE) such as Jupyter Notebook, PyCharm, or Visual Studio Code. This will help in organizing and running your code efficiently.
Loading and Processing Data
How to Load the Data Files
Data can be sourced from well-known databases like Kaggle, the world’s largest data science and machine learning community.
import pandas as pd
# Loading the dataset
data = pd.read_csv("./credit_risk_dataset.csv")
df.head()Handling and preprocessing the data
Preprocessing involves cleaning the data, handling missing values, and transforming it into a format suitable for machine learning models. Here’s how to preprocess the data:
df = df.drop_duplicates()
df = df.dropna()
df.isnull().sum()Validation of the Study
In this project, we initially used five different models to assess their validity and performance in predicting credit risk. After evaluating the results, we selected the CatBoost Classifier as the best model for making predictions. Below are the performance metrics for each model tested:
Visualizing Data with LightningChart
LightningChart Python allows for the creation of highly interactive and customizable charts. This library is useful for financial data visualization due to its performance and feature set. Here are some of the LC charts below:
Creating the charts
To visualize credit risk modeling charts, you can create various charts using LightningChart Python:
import lightningchart as lc
import random
# Initialize LightningChart and set the license key
lc.set_license('my-license-key')
# Create a BarChart with stacked data using LightningChart
chart = lc.BarChart(title='Age Distribution by Loan Status')Customizing visualizations
LightningChart offers extensive customization options. You can adjust the colors, add markers, or integrate real-time data updates to enhance the visualization:
chart = lc.BarChart(
vertical=True,
theme=lc.Themes.White,
title='Age Distribution by Loan Status'
)
chart.set_data_stacked(
categories,
[
{'subCategory': 'Good Loan Status', 'values': hist_good},
{'subCategory': 'Bad Loan Status', 'values': hist_bad},
{'subCategory': 'Overall Loan Status', 'values': hist_overall}
]
)
chart.set_value_label_display_mode('hidden')
chart.add_legend().add(chart)
chart.open()Conclusion
This project demonstrated how to build a credit risk model using Python and visualize the results with LightningChart. The use of machine learning models improved prediction accuracy, while LightningChart provided high-quality data visualizations.
Benefits of using LightningChart Python for visualizing data
LightningChart Python’s high performance and extensive feature set make it an excellent choice for visualizing stock market data. Its ability to handle large datasets with minimal latency ensures that traders have access to real-time data, allowing them to make timely and informed decisions.
Ahmad Omid
Data Science Python Developer
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