Simple analysis workflow to data about default of credit card clients

Abstract

These days I had opportunity of reading some papers about finance data analysis, meaning credit score, default rate and so on. Personally, I want to tackle with cutting-edge way as soon as possible. But, it is important to see from basic flow on this kind of case. So, here, on this article, I'll follow the basic work flow like univariate analytics with Logistic Regression.
To focus on basic flow and some characteristics, I'll ignore some manner to the data and modeling.
This article more or less follows the chapter 2 and 3 of the following article.

• https://edoc.ub.uni-muenchen.de/17143/1/Kraus_Anne.pdf

Perceptron by scikit-learn

Overview

I sometimes use Perceptron, one of the machine learning algorithms, as practice of algorithm writing from scratch.

But in many cases, it is highly recommended to use machine learning library. Although there are not many cases in practice that we use Perceptron, it is not wasted to know how to write Perceptron by the library, concretely scikit-learn.

On this article, I’ll show how to write Perceptron by scikit-learn.

Simple guide to kNN

Personally, I like kNN algorithm much. Because kNN, k nearest neighbors, uses simple distance method to classify data, you can use that in the combination with other algorithms. It can also be one of the first step to study machine learning algorithms because of the simplicity.

On the following articles, I wrote about kNN. But although I myself don’t know the reason, I’ve never touched the simplest usage of kNN, meaning how to use kNN of sklearn’s library.

• kNN by Golang from scratch
• How to write kNN by TensorFlow
• Speed up naive kNN by the concept of kmeans
• CNN + KNN model accuracy

Basic classification example by logistic regression

Basic classification example

Overview

I make classification model of free wine data, following how to deal with it step by step.