## Unlocking Stock Market Insights with RiskBERT

Predicting stock prices is akin to the attempts of alchemists in the Middle Ages to transmute lead into gold. Just as alchemists sought to unlock the secrets of transformation, statisticians …

Skip to content
# Category: Python

## Unlocking Stock Market Insights with RiskBERT

## Generalized Semantic Regression using Contextual Embeddings

## Guessing Passwords using an LSTM Network

## Building a minimal, cost efficient Dask cluster

## Kernel Regression using the Fast Fourier Transform

## Fast Kernel Density Estimation using the Fast Fourier Transform

## Non-Linear Classification Methods in Spark

## Non-Linear Support Vector Machines (SVM)

## Kernel Regression using Pyspark

## Nonparametric Density estimation using Spark

## Functional Regression with Spark

## Functional Principal Component Analysis with Spark

## 2. Coding the “Educated Guess Procedure”

## 5. Running some tests

A science blog about my spare time data analysis projects.

Predicting stock prices is akin to the attempts of alchemists in the Middle Ages to transmute lead into gold. Just as alchemists sought to unlock the secrets of transformation, statisticians …

In many applications actuaries, data scientists or researchers are confronted with datasets like shown in Table 1. While nominal variables, as in the first or second column, can be used …

Introduction A while ago I applied NLP strategies to implement an algorithm that is capable to guess a password. Since then a new method called transformer was developed and successfully …

In this article we will show a way to do high performance parallel computing at a Kubernetes cluster using task. A primary focus is that we want to archive the …

1. Setup In a previous post it was shown how to speed up the computation of a kernel density using the Fast Fourier Transform. Conceptually a kernel density is not …

1. Setup This Post is about how to speed up the computation kernel density estimators using the FFT (Fast Fourier Transform). Let be be a random sample drawn from an …

In a previous post I covered how to apply classical linear estimators like support vector machines or logistic regression to a non-linear dataset using the kernel method. This article can …

1. Introduction This blog post is about Support Vector Machines (SVM), but not only about SVMs. SVMs belong to the class of classification algorithms and are used to separate one …

1. Kernel Regression using Pyspark In a previous article I presented an implementation of a kernel denisty estimation using pyspark. It is thus not difficult to modify the algorithm to …

1. A Nonparametric Density implementation in Spark One of my previous blog post concerns about nonparametric density estimation. In this post i presented some Matlab code. An advantage of this …

1. Functional Regression Let the covariate be an at least twice continuously differentiable random function defined wlog. on an interval and the corresponding the response. For simplicity we assume centered …

1.) Functional Principal Component Analysis Let be a centered smooth random function in , with finite second moment . Without loss of generality we assume instead of some arbitrary compact …

1. Perform the Analyze To start with, we load the “rockyou.txt.tar.gz” password list using wget. I’m not sure if it is legal to provide a link for the list, therefore …

1. Test the Enviroment 1.1 Simulation of a Brownian Motion The purpose of the first notebook entry is to check if matplotlib is correctly installed. We simulate 20 Brownian Motions …