A package for statistically rigorous scientific discovery using machine learning. Implements prediction-powered inference.
Multiple hypothesis testing in Python
Analysis platform for large-scale dose-dependent data
pMoSS (p-value Model using the Sample Size) is a Python code to model the p-value as an n-dependent function using Monte Carlo cross-validation. Exploits the dependence on the sample size to characterize the differences among groups of large datasets
Implementation of backward elimination algorithm used for dimensionality reduction for improving the performance of risk calculation in life insurance industry.
collection of utility functions for correlation analysis
Strategies for analyzing the distribution of datasets, switching the data towards a normal distribution testing different manual transformations and Box-Cox transformation.
Correspondence Analysis with python
Online Multiple Hypothesis Testing
MyWork
Python package to perform statistical hypothesis tests.
This project analyzes the effects of smoking on gene expression, focusing on the interaction between **Smoking Status** and **Gender** using a 2-way ANOVA framework. The results, visualized through a p-value histogram, highlight genes with potential differential responses.
Companion Code for the Medium Article on top Python Data Science Interview Questions.
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
Method of Combined P-value to identify Differential Expressed Gene in dataset
This project involves comparing the effectiveness of two bidding methods, "maximum bidding" and "average bidding," through an A/B test. The goal is to determine whether the "average bidding" method yields more conversions compared to the existing "maximum bidding" method.
Estimation of the Shapiro-Wilk test using the Monte Carlo method.
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