An End-to-end ML Project with Real Bank Data. For banks, it is always an interesting and challenging problem to predict how likely a client is going to default the loan when they only have a handful of information. In the modern era, the data science teams in the banks build predictive models using machine learning.
For banks, it is always an interesting and challenging problem to predict how likely a client is going to default the loan when they only have a handful of information. In the modern era, the data science teams in the banks build predictive models using machine learning. The datasets used by them are most likely to be proprietary and are usually collected internally through their daily businesses. In other words, there are not many real-world datasets that we can use if we want to work on such financial projects. Fortunately, there is an exception: the** Berka Dataset**.
The Berka Dataset, or the PKDD’99 Financial Dataset, is a collection of real anonymized financial information from a Czech bank, used for PKDD’99 Discovery Challenge. The dataset can be accessed from my GitHub page.
In the dataset, 8 raw files include 8 tables:
In this article you will learn to create databases, manipulate databases, and will also learn some operations on handling databases in MySQL with Python.
Applied Data Analysis in Python Machine learning and Data science, we will investigate the use of scikit-learn for machine learning to discover things about whatever data may come across your desk.
Practice your skills in Data Science with Python, by learning and then trying all these hands-on, interactive projects, that I have posted for you.
Practice your skills in Data Science with Python, by learning and then trying all these hands-on, interactive projects, that I have posted for you.
Practice your skills in Data Science with Python, by learning and then trying all these hands-on, interactive projects, that I have posted for you.