统计分析课程实验作业/包含《统计分析方法》中因子分析，主成分分析，Kmeans聚类等典型算法的手写实现

Demo on the capability of Yandex CatBoost gradient boosting classifier on a fictitious IBM HR dataset obtained from Kaggle. Data exploration, cleaning, preprocessing and model tuning are performed on the dataset

Implementation of Lo and MacKinlay's statistical tests from A Non Random Walk Down Wall Street

Gene Set Enrichment Class Analysis for heterogeneous RNA sequencing data

GAUSS implementation of ICSS from Sansó et al. "Testing for Changes in the Unconditional Variance of Financial Time Series"

This is the repo for the project in Combinatorial Decision Making and Optimization at @unibo: optimizing a stock portofolio by using linear and quadratic optimization functions.

Hamming Network implementation using pca implementation for reduction all from scratch

In this note, we will give the recursive formulas for sample mean and sample variance, and their generalized forms for batch updates.

Techniques For Feature Selection

The Chinook Data Analysis Project leverages PostgreSQL, Python, and Google Spreadsheets to explore and analyze the Chinook music store database. Insights will be presented through Tableau Dashboards and Stories. Stay tuned for updates as the project evolves.

In this note, we will analyze the variance of stepwise sample means (SSM).

Performing Statistical Tests and Analysis in R

💊 AB Testing Between Versions

projeto de calculadora capaz de fazer o calculo de: variância, desvio padrão, Fibonacci, logaritmos e fatorial além das operações básicas

In this project, I've used College/University data to perform Principal Component Analysis and provide its implications to the business

An employer has tasked a data analyst with utilizing the provided raw data to generate insightful visual representations. The goal is to extract valuable insights that can contribute to enhancing the overall performance of the company.

✈️ Variance analysis of flight delays to conclude which one of the biggest airline companies in Brazil is the best

This repository contains a Python implementation of Principal Component Analysis (PCA) for dimensionality reduction and variance analysis. PCA is a powerful statistical technique used to identify patterns in data by transforming it into a set of orthogonal (uncorrelated) components, ranked by the amount of variance they explain.

Algorithm based on Matlab.