Robust statistics in Python

Interactive statistics analysis app using Python and Streamlit. Perform key statistical tests, visualise distributions, and explore data with ease.

This project analyzes online advertising performance using Exploratory Data Analysis, Hypothesis Testing, and Regression Analysis. It examines key metrics like click-through rates, conversion rates, and ad costs to uncover insights for optimizing ad spend and improving campaign efficiency. Built with Python, Pandas, Scikit-Learn, and Statsmodels.

This repository contains four different hypothesis testing projects, analyzing real-world data to validate assumptions and drive data-driven decisions. Each project applies statistical tests (e.g., t-tests, chi-square, ANOVA) to uncover insights and support business strategies. Built with Python, Pandas, SciPy, and Statsmodels. 🚀

Open-source statistical package in Python based on Pandas

Repositorio para el curso intersemestral "Temas Selectos en Estadística" para la Facultad de Psicología, UNAM.

* Basis EDA * Handling Null/Missing Values * Handling Outliers * Handling Skewness * Handling Categorical Features * Data Normalization and Scaling * Feature Engineering

Chi2 contengency independence test Q5. Fantaloons Sales managers commented that % of males versus females walking in to the store differ based on day of the week. Analyze the data and determine whether there is evidence at 5 % significance level to support this hypothesis. Assume Null Hypothesis as Ho: Independence of categorical variables (% of

Chi2 contengency independence test Q4. TeleCall uses 4 centers around the globe to process customer order forms. They audit a certain % of the customer order forms. Any error in order form renders it defective and has to be reworked before processing. The manager wants to check whether the defective % varies by centre. Please analyze the data at 5

This repository contains my notes of Calculus and Statistics that I taught in the Department of Mathematics at The University of Texas at Tyler.

Comparing Linear Regression with kNN, Decision Tree and Random Forest with Bayesian Inference to Predict Wine Quality in Python.

Data Science - Hypothesis Testing Work

This project predicts healthcare costs and identifies contributing factors using data analysis, machine learning, and SQL data management.

About Performed A/B test and help the company decide whether they should implement the new web page, keep the old page, or run the experiment longer.

Motif Detection for TFBS in Glycolysis and Glyconeogenesis pathways

Performed A/B test and help the company decide whether they should implement the new web page, keep the old page, or run the experiment longer.

Analyzing biological networks using statistical testing to uncover significant differences in protein distributions based on functional relationships.

ANOVA test using python to find out if survey or experiment results are significant and the impact of one or more factors by comparing the means of different samples

Used libraries and functions as follows: