Call-center discrete event simulation project mostly done using numpy and simple data structures.
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Updated
Aug 5, 2022 - Python
Call-center discrete event simulation project mostly done using numpy and simple data structures.
Scraping Austrian statistics platform STATatlas for public available data using selenium
A package for using statistical distributions.
An implementation of latent dirichlet allocation on Wikipedia pages
Research Repository
cloze complition using N-grams in python
Statistical Digital Signal Processing and Modeling
Analyse efficacy of your own methods for calculating confidence interval
Python scripting examples to clean, extract and display statistical relationships from a dataset
Implementation of Joint Fairness Model with Applications to Risk Predictions for Under-represented Populations
Generator of new data with the same probability distribution and statistics from a frecuency table or a dataset or or alternatively bothboth.
python module, showcasing computation (as part of a learning process) of some common statistical methods including mininum sample size, confidence interval estimation methods for mean or proportion, hypothesis testing mehods and regression models witth metrics and test suites
The ideal way to evaluate binary classification models.
A one-dimensional lattice to model noise-induced memory
N-gram language models for estimating the probability of strings given training data.
This repository contains code which accompanies the paper: Assessing Model Behaviour on Extreme Counterfactuals (https://www.researchgate.net/publication/360912277_Assessing_Model_Behaviour_on_Extreme_Counterfactuals)
Assignments completed for my Machine Learning course: Topics include probability and statistics proofs, MLE/MAP parameter estimation, EM Algorithm, Bayes Theorem implementations, gradient descent methods, Neural Networks and Deep Learning.
In this repository, basic statistics and statistical analysis methods are applied in python programming language.
A statistical model of the COVID-19 vaccination campaign. It segments the population into agnostics, pro-, and anti-vaccines. Vaccination is modeled as a Poisson process, and social pressure on the population can change their views on vaccines. The model can faithfully reproduce real-world data.
Training Materials for Data Science With Python
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