Predict the likelihood of loan defaults and get insights with SHAP values.

Photo by Dmitry Demidko on Unsplash

Credit default risk is a measurement that looks at the probability that a loan amount will not be paid back. Using the credit risk data set from Kaggle we can build a model using machine learning to predict the likelihood of a individual not being able to pay back their loan.

I’ve used the Lazy classifier package to test different models with the data and the SHAP package to get a feature importance insights to see what drove the predictions. …

Photo by Manuel Cosentino on Unsplash

I created a simple translation app using streamlit to highlight the simplicity of using the platform to quickly create apps. The app takes English input from a user which is then translated to Japanese.

Using deep learning to Classify Handwritten Digits from Kaggle MNIST data with fastai and Google Colab

Photo by Christopher Burns on Unsplash

After starting fastai’s practical deep learning for coders, I’ve built a simple model in Google Colab with fastai to identify handwritten numbers from the Kaggle MNIST dataset.

The code downloads the MNIST data from Kaggle, creates jpg images and stores it in your google drive. It then trains and tests the model to submit to Kaggle. The final results from this model achieved a public score of 0.97396.

A lot of the motivation for this work was to prepare myself to compete in future Kaggle competitions and to apply the knowledge I’ve learnt so far through fastai’s MOOC. …

Jiro Ishida

Budding Data Scientist by day, mediocre Olympic Weightlifter/Hip-hop Producer by night.

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