t-test

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

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

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.

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

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

Marketing Campaigns A/B Testing on Jupyter Notebook

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

Calculate independent samples t test using summary statistics

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

Test the phenomenon of Stroop Effect

OCS (BP): Examine global patterns of obesity across rural and urban regions

A user-friendly app for performing various statistical analyses. Easily calculate and visualize key statistics including mean, median, standard deviation, and more. Ideal for quick and accurate data analysis.

An analysis of titanic dataset from Kaggle using Python pandas and mathplotlib. Includes the definition of questions to be answered, detailed description of the exploratory steps, and communication of conclusions.

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

about statistical techniques for Data Science

Statistical analysis of vehicle production metrics with R

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

🧐 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.