A statistics package with a variety of bootstrap and other resampling tools

A statistics package with a variety of bootstrap and other resampling tools. This repository is synced to the same-named repository owned by GNU-Octave. It exists to facilitate publication of the developmental version of the statistics-resampling toolbox at MathWorks FileExchange.

IU Lessons

Predict and prevent customer churn in the telecom industry with this project. Leverage advanced analytics and ML on a diverse dataset to build a robust classification model. Gain a deep understanding of customer behavior and identify key factors influencing churn. Clone the repository, explore insights, and enhance customer retention startegies.

Mediante un Análisis de Regresión Lineal Multiple se identifican los factores que afectan el nivel de colesterol.

solving problems with data analysis, hypotheses and the most used statistical tests in ecology

Performed rigorous preprocessing, and data cleaning, and conducted exploratory data analysis to identify trends, patterns, and outliers, leading to valuable insights. Employed various statistical methods concepts to get insights about the data for prediction.

Analyze Data with Python | Hypothesis Testing with Scipy

ANOVA_diamonds_analysis

My Python learning experience 📚🖥📳📴💻🖱✏

This project aims to analyze a dataset of sales transactions from a UK-based e-commerce store specializing in gifts and homewares over one year. With 500,000 rows and 8 columns, including transaction numbers, dates, product details, prices, quantities, customer identifiers, and countries, the analysis focuses on understanding customer behavior

Hypothesis Testing

Explore Python implementations of predictive modeling techniques like F-test, t-test, ANOVA, linear square estimation, autocorrelation, and least squares in this practical-driven GitHub repository

Using machine learning technique of K-Nearest Neighbors and visualization tools, our group predicted credit score classification for bank customers based on 10+ features.

I will use Excel to perform statistical analysis for the user study results in this project. Specifically, I will use the Real Statistics Resource Pack for performing the analysis.

Embark on a journey of data-driven insights with our diabetes research project. Leveraging Python's pandas, matplotlib, and scikit-learn, we preprocess, visualize, and analyze 330 health features. Employing logistic regression, decision trees, KNN, and SVM, we predict diabetes with precision.

This repository contains the solutions for two assignments covering statistical analysis using R. Each assignment consists of exercises aimed at reinforcing statistical concepts and R programming skills.

This study delves into the intricate dynamics between salary, education, and occupation, employing both one-way and two-way ANOVA techniques to uncover their relationships. Additionally, it explores the multifaceted dataset on colleges through Principal Component Analysis (PCA).

Which variables are significant in predicting the demand for shared electric cycles in the Indian market and How well those variables describe the electric cycle demands

In this repository, discover the intricacies of the ANOVA test and its various types, essential for informed decision-making. Dive into practical demonstrations of each ANOVA test using Python, with a focus on visualizing their application on COVID-19 data. Let's embark on a journey to explore and understand statistical analysis in Python!!