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Ml Course ⭐ 168

Starter code of Prof. Andrew Ng's machine learning MOOC in R statistical language

Generates and evaluates D, I, A, Alias, E, T, G, and custom optimal designs. Supports generation and evaluation of mixture and split/split-split/N-split plot designs. Includes parametric and Monte Carlo power evaluation functions. Provides a framework to evaluate power using functions provided in other packages or written by the user.

Highcharts_trendline ⭐ 77

HighCharts demo of scatter plot, including a trend line

The Elements Of Statistical Learning ⭐ 46

This repository contains R code for exercices and plots in the famous book.

Machine Learning Matlab ⭐ 46

Matlab implementation of Machine Learning algorithms

Regression Wasm ⭐ 32

Testing doing basic regression with web assembly

Automobile Dataset Analysis ⭐ 20

This project analyzes and visualizes the Used Car Prices from the Automobile dataset in order to predict the most probable car price

Multiple linear regression with statistical inference, residual analysis, direct CSV loading, and other features

Peroxide_gallery ⭐ 9

Examples of Peroxide (Rust numeric library)

Realestate ⭐ 9

Contains a set of Python scripts for real estate data analysis, visualization, and fitting.

Capstoneproject_house_prices_prediction ⭐ 7

Understand the relationships between various features in relation with the sale price of a house using exploratory data analysis and statistical analysis. Applied ML algorithms such as Multiple Linear Regression, Ridge Regression and Lasso Regression in combination with cross validation. Performed parameter tuning, compared the test scores and suggested a best model to predict the final sale price of a house. Seaborn is used to plot graphs and scikit learn package is used for statistical analysi

Knu_datascience101 ⭐ 5

경북대학교 IT대학 컴퓨터학부 데이터과학기초(2018년2학기) 강의예제코드

3d Plot Python Interpolation ⭐ 5

How to plot a 3D surface with python by interpolation

Regression With Python And R ⭐ 5

Regression with Python & R.

Predicting Diamond Prices ⭐ 5

Project 1.1 : A diamond distributor has recently decided to exit the market and has put up a set of 3,000 diamonds up for auction. Seeing this as a great opportunity to expand its inventory, a jewellery company has shown interest in making a bid. To decide how much to bid, the company’s analytics team used a large database of diamond prices to build a linear regression model to predict the price of a diamond based on its attributes. You, as the business analysts, are tasked to apply that model t

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