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.

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

Hamming Network implementation using PCA implementation from scratch

Hamming Network implementation using pca implementation for reduction all from scratch

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.

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

Performing Statistical Tests and Analysis in R

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

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

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

💊 AB Testing Between Versions

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.

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

Techniques For Feature Selection

💻Anomaly detection can be 👨‍💻treated as a statistical📉 task as an outlier analysis📊. But if we develop a machine learning model📈, it can be automated and as usual, can save a lot of time🕐

Gene Set Enrichment Class Analysis for heterogeneous RNA sequencing data

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

Feature Selection in ML using Python

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

Clustering data ruspini menggunakan k-means dan menganalisa cluster menggunakan variance