Johns Hopkins University Bloomberg School of Public Health: Data Science Specialization Program: Statistical Inference Course Peer Review project repo: date created 60806

Моделирование реализаций случайного процесса, статистические оценки математического ожидания и дисперсии случайного процесса, статистическая оценка автокорреляционной функции случайного процесса, однородная цепь Маркова, предельные вероятности состояний цепи Маркова, распределение вероятностей состояний цепи Маркова

Introduction to the R programming language.

I use R to analyze survey data to position popular consumer electronics brands using Principal Component Analysis, uncovering key perceptual attributes and providing insights for targeted marketing strategies.

Built a model to predict the value of a given house in the Boston real estate market using various statistical analysis tools. Identified the best price that a client can sell their house utilizing machine learning.

Bias and Variance tradeoff : Learning from data course

Exemplos de Algoritmos desenvolvidos em Java

An R package to compute a confidence interval for the sample variance. Don't assume normality.

Cmpe343_hw_Bogazici_University

Contains some Test of Statistics

Repository for the First-Second Moments Learning project. In this repo you will find an implementation of a learning model to learn the relationship between time-series model parameters and the first two moments of its outputs

Analysis of variance for repeated measures

Python Package for Gaussian and Binomial Distribution

a function named calculate() that uses Numpy to output the mean, variance, standard deviation, max, min, and sum of the rows, columns, and elements in a 3 x 3 matrix.

Implementation of Linear Regression Algorithm to give best fit model for a given set of data

Advance python Math Library which performs all the basic and scientific math operations such as cos, sin, tan etc. It also includes basic statistical functions such as mean, median, mode, standard deviation etc

Customer Segmentation

This project is about analysis of Fundraising Campaigns to discover various factors responsible after a successful campaign.

Example showcasing why dropping a column for one-hot-encoded data is appropriate and example showing the effects variance has on multi-regression coefficients.