A Python package for the statistical analysis of A/B tests.

PyImpetus is a Markov Blanket based feature subset selection algorithm that considers features both separately and together as a group in order to provide not just the best set of features but also the best combination of features

Machine learning for beginner(Data Science enthusiast)

Implement a momentum trading strategy in Python and test to see if it has the potential to be profitable

R package for computing multiple hypothesis tests on rows/columns of a matrix or a data.frame

Data driven fault detection in chemical processes: Application to Tennessee Eastman Plant

🧐 This project analyzes Amazon Fine Food Reviews to investigate whether negative reviews are more emotionally intense and lexically repetitive than positive ones. Using R, we apply sentiment analysis and lexical diversity metrics to uncover patterns in consumer review language.

Supervised classification to predict rock facies and a T-test flow to evaluate the prediction performance.

This is an initiative to help understand Statistical methods and Machine learning in a naive manner. You will find scripts, and theoretical contents required to clarify concepts, especially for bio-informatic students.

Marketing Campaigns A/B Testing on Jupyter Notebook

High School SSVEP-BCI Research Project to improve classification accuracy of captured EEG signals

This repository is created for storing the components of Statistical Tests of One Pop, Two Pops and Three or more pops using Python.

This project implements in Python some common statistical analysis methods used in data analysis, including Entropy, Mutual Information, Kolmogorov–Smirnov test, Kullback-Leibler divergence (KLD), AB tests (Mann-Whitney U and t-tests)

Pancreatic Cancer Biomarkers Identification Codes & Files

Tumor prediction from microarray data using 10 machine learning classifiers. Feature extraction from microarray data using various feature extraction algorithms.

about statistical techniques for Data Science

writR: is an R package for automated inferential testing (for group differences) and reporting based on parametric assumptions, which are tested automatically for test selection.

Retrieving, Processing, and Visualizing Data with Python

I will include two ways of t tests that compare conversion rate and click through rate of two groups