Risk Management

Here you’ll find everything related to machine learning for risk management, whether it’s Python tutorials or conceptual articles.

Machine learning can be used in risk management in a variety of ways, including:

  1. Credit risk analysis: Machine learning algorithms can predict the likelihood that a borrower will default on a loan, allowing banks and other lenders to make informed lending decisions.
  2. Fraud detection: Fraud detection is about detecting fraudulent activities, such as fake transactions or forged documents. It can then provide recommendations or take action to prevent them from impacting the business.
  3. Risk identification: Machine learning can identify potential risks in a business, such as supply chain disruptions or market shifts. It can then take actions to mitigate these risks.
  4. Portfolio optimization: Machine learning can optimize the allocation of assets in a portfolio, taking into account factors such as risk and return.

Tuning Model Hyperparameters with Grid Search at the Example of Training a Random Forest Classifier in Python

hyperparameter tuning titanic dataset machine learning

Are you struggling to find the best hyperparameters for your machine learning model? With Python’s Scikit-learn library, you can use grid search to fine-tune your model and improve its performance. In this article, we’ll guide you through the process of hyperparameter tuning for a classification model, using a random decision forest that predicts the survival … Read more

Flight Delay Prediction using Azure Machine Learning

flight delay prediction azure machine learning

If you travel a lot, you’ve probably already experienced this – you’re in a hurry on your way to the airport trying to catch a flight, only to find out that your flight is delayed. Wasn’t it great to know when a flight will be delayed in advance? We can use past flight delay data … Read more