Enabling easy statistical significance testing for deep neural networks.

Grammars suitable for lark parser and Hypothesis

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

Course included such topics, as Data Preprocessing, Exploratory Data Analysis (EDA), Statistical Data Analysis (SDA), Data Collection and Storage (PostgreSQL), Business Analytics, Making Business Decisions Based on Data (Hypotheses testing), How to Tell a Story Using Data (Presentation and Data Visualization - Maplotlib, Seaborn, Plotly), Automa…

🏠 This repository contains data analysis scripts for the 2022 American Community Survey (ACS) focusing on individuals aged 25 and over in North Carolina, based on 75,340 observations. This repository offers valuable insights into demographic and economic patterns across North Carolina's urban areas.

Resampling-Based Hypothesis Testing for Python

Implement statistical hypothesis tests with Python easly

Data Science Statistics Interview Questions

An R project that investigates whether different genres of songs have significantly different durations through the use of a one-way ANOVA test and post hoc significance tests conducted over an excerpt of a dataset consisting of 1 million popular songs compiled by The Echo Nest and a lab at Columbia University.

Idealvalues ​​is a tool you can use for hypothesis testing. Use Idealvalues ​​to find a sum value within an array of numbers.

How to Use Hypothesis and Pytest for Robust Property-Based Testing in Python

Hypothesis-Test in python

To import data from multiple sources, clean and wrangle data, perform exploratory data analysis (EDA), and create meaningful data visualizations. I will then predict future trends from data by developing linear, multiple, polynomial regression models & pipelines and learn how to evaluate them.

Sales of products in four different regions is tabulated for males and females. Find if male-female buyer rations are similar across regions

Codes in paper "A Multivariate Robust Test for Genetic Pleiotropy".

Exploring and analyzing the dataset to draw meaningful insights. Conducted hypothesis testing, resolved data anomalies, and crafted a predictive model using KNN Classifier. Addressed key questions, such as demographic-based variations in loan status and predicting loan-to-value ratios. Ensured data integrity for informed decision-making.

A basic program for data analysis of 1 parameter, it can be extended and optimized. Program done collaborating with Alessandro Namar

A collection of Statistics and ML notebooks useful for Graduate Students

Portfolio of statistics models, inferential statistics and different topics usign R and python.

Trying out the hypothesis library for property-based testing