Statistical package in Python based on Pandas

Deep learning methods for feature selection in gene expression autism data.

Streamlining statistical analysis by using plotting keywords in Python.

Open-source statistical package in Python based on Pandas

Implementation of various feature selection methods using TensorFlow library.

Scripts to perform pairwise t-test on TREC run files

Comparison of A/B Test and Conversion of Bidding Methods

A F&B manager wants to determine whether there is any significant difference in the diameter of the cutlet between two units. A randomly selected sample of cutlets was collected from both units and measured? Analyze the data and draw inferences at 5% significance level. Please state the assumptions and tests that you carried out to check validit…

Tutorials for BSE classes.

containing projects currently working on it

marketing metrics analysis, A/B test and ttest, CLTV calculation

My this project repository focused on hypothesis testing involving T-test, Chi-square test, Binomial Test, ANOVA, Sample Size Determination with scipy, statmodels modules.

Root Cause analysis to check for disparity in testing performance across different jurisdictions across US

Exploratory data analysis is an approach to analyzing data sets to summarize their main characteristics, often with visual methods. A statistical model can be used or not, but primarily EDA is for seeing what the data can tell us beyond the formal modeling or hypothesis testing task.

Easy statistical testing on the web

MechaCar prototypes Collected summary statistics on the pounds per square inch (PSI) of the suspension coils from the manufacturing lots Ran t-tests to determine if the manufacturing lots are statistically different from the mean population Designed a statistical study to compare vehicle performance of the MechaCar vehicles against vehicles from…

To compute and plot significances (pvalues and asterisks) on bar graphs

This project focuses on predicting whether a customer will default on their credit card payment in the upcoming month. Utilizing historical transaction data and customer demographics, the project employs various machine learning algorithms to distinguish between risky and non-risky customers for better credit risk management.

T-test Independence of sets of variable