In the journey of a successful credit scoring implementation, multiple stakeholders and different personas are involved at different steps – Business Inputs, Dataset procurement, Data Analysis, Predictive Machine Learning, Data Storytelling, and Dashboarding. H2O.AI platforms such as DriverlessAI and H2O Wave help in automating a lot of these steps in the overall lifecycle of the project. In this article, we will look at how to use these platforms to develop high quality machine learning models for credit scoring and a business facing applications to consume the results.
We will use a public dataset for this article obtained from Kaggle, it contains credit details of about 250,000 borrowers along with the target column – if they managed to repay the loan or not. The goal is to use this dataset and train a machine model that banks can use to predict the risk score of the customers. Following table shows the column descriptions.
H2O Driverless AI is an Automatic Machine Learning platform that can be used to build trustable, transparent, and production ready machine learning Models for a variety of data science problems such as – forecasting, regression , classification , nlp etc. It is pre-configured with 150+ recipes (algorithms and techniques) for models, transformations, and scoring. In addition to these, one can also bring their own algorithms, feature engineering code to enhance the model building process.
To develop a newCredit Scoring Model, we will first add the dataset in Driverless AI which can be added via file upload (or any data connector) in the Datasets tab. We start a new Driverless AI experiment by clicking
Predict on the dataset and define the target column as
IsBadCredit . There are a few other optional parameters which can be changed, example the three knobs in the bottom suggests Accuracy, Time, Interpretability. These knobs can be fine tuned in an iterative manner to change the modelling, feature engineering strategies. Finally, we click the
LAUNCH EXPERIMENT button to start the auto machine learning experiment.
In about 6 mins, Driverless AI had already created 20 models on 130 features and one can also observe the AUC score on the validation dataset (as it performs cross-validation internally). Most of the features are generated automatically by Driverless AI, using an evolutionary technique inspired by genetic algorithms.
After about 15 mins, the experiment finished with 0.8662 AUC by evolving both the algorithm hyperparameters (tuning) + engineered features.
Once the experiment completes, Driverless AI provides several valuable artifacts such as – Experiment Auto Report Documentation, Machine Learning Explainability, and Model Scoring Pipeline.
One can fine tune this experiment with more tweaks and customizations in an iterative manner, however if it is acceptable to the banks, one can now use this model and can generate the prediction scores for any new data. Many business users prefer an interface to consume the results with summary statistics and visualizations. In fact, for a use case like this where multiple users / personas are involved, there is a need to create an app. Let’s now look at how we can leverage H2O Wave to build a business ready application to display the model predictions on a new dataset along with a few visualizations.
H2O Wave is an open-source Python development framework that makes it fast and easy for users to develop real-time interactive AI apps with sophisticated visualizations. H2O Wave accelerates development with a wide variety of user-interface components and charts, including dashboard templates, dialogs, themes, widgets, and many more.
CreditScoreApp and create a python file named
Let’s now add different cards to make the app layout. In the homepage, we will add
def add_header_card(box): return ui.header_card(box=box, icon='UserFollowed', icon_color='Yellow', title="Credit Scoring App", subtitle="Generate Credit Score Predictions using Driverless AI" ) def add_sidebar_card(box, customer_ids): id_choices = [ui.choice(_, _) for _ in customer_ids] return ui.form_card(box=box, items = [ui.text_xl(content='Select Customer Record'), ui.dropdown(name='customer_id', label='ID', choices=id_choices), ui.button(name='predict', label='Generate', primary=True)])
The updated code of
show_homepage will look like this:
Notice the box parameter given as the first input of every function call. Box defines the size and location of a card, it uses the following format:
Column Row Width Height . The code we added so far will generate the following interface with three cards.
Now let’s start adding the cards in the Content Card to make a business dashboard. We will add multiple cards in different rows. Following shows the structure of the dashboard content. We add following sub-cards in this dashboard:
H2O Wave provides native Visualizations and many pre-built templates to display stat cards, gauge cards, plots etc.
def add_bar_chart(box, title, plot_type='interval'): return ui.plot_card(box=box, title=title, data=data('xvalue yvalue'), plot=ui.plot([ui.mark(type=plot_type, x='=xvalue', y='=yvalue', color='=yvalue')]))
Following will generate a Credit Scoring App which can be accessed by different users to get the credit scores of different customers.
At the start of the app, we want to take our test dataset and make predictions using the trained driverlessai experiment. For this task, we will use Python’s Client. Following is the snippet that I used to integrate both of these platforms to make the predictions.
import driverlessai class DriverlessPredict: def __init__(self, config): self.dai, self.exp = self.dai_connect(config) def dai_connect(self, config): dai = driverlessai.Client(address = config['address'], username = config['username'], password = config['password']) exp = dai.experiments.get(config['experiment_key']) return dai, exp def dai_predict(self, input_path): dai_table = self.dai.datasets.create(input_path, force=True) pred_path = self.exp.predict(dai_table).download('datasets/', overwrite=True) return pd.read_csv(pred_path)
To make the predictions on the test dataset, we first connect to driverless ai instances by providing username, password, and url address. Then we pass the dataset to make predictions in the
dai_predict functions. This returns a new dataframe with an extra column – predictions.
In this article, we looked at how we can create a machine learning model in Driverless AI. We then used the model to create a business facing application using H2O Wave . The source code for this application is hosted here .