Portfolio Optimization and Quantitative Strategic Asset Allocation in Python

The effect quantity is decomposed into water pressure component, temperature component and aging component, and the diagram is made automatically. Refer to Prof. Wu Zhongru's Safety Monitoring Theory & It's Appliaction of Hydraulic Structures

Tools for developing OLS regression models

The goal of these examples is to analyse the given datasets to determine whether some models can be established for purposes of prediction, to assess how stepwise prediction behaves with respect to a personally chosen model and determine an unknown trend in the cereal dataset.

Training a predictive model to forecast the house sale price in Ames, Iowa using Supervised Machine Learning Multiple Linear Regression algorithm with Stepwise Regression feature selection.

The given dataset contains electricity consumer household information. This information has been used to predict the amount to be paid by the consumer with the help of regression model selection and validated with feature importance.

Tugas besar analisa data

This repository contain about stepwise regression from scratch using python

Sports Analytics in Python

Sports Analytics in R (Step-wise Regression and Subset Selection Regression)

Regression models for predicting bitcoin price

Forest-Fire-StepwiseRegression The relationships between the ‘Probability of Forest Fire’ in Algeria and its various weather components have been estimated.

Analysis of survey data collected by M. Weisend in the Thar Desert, India. Includes example analysis using stepwise regression and ANCOVA.

This analysis is based on the multivariate normal prior

Classification of movie rankings

Stepwise regression fits a logistic regression model in which the choice of predictive variables is carried out by an automatic forward stepwise procedure.

Contains a wide-ranging collection of compressed sensing and feature selection algorithms. Examples include matching pursuit algorithms, forward and backward stepwise regression, sparse Bayesian learning, and basis pursuit.

Performing EDA and building model that predicts the selling prices of new homes at a Colorado ski resort

My role in this group project was to perform regression analysis on quarterly financial data to predict a company's market capitalization. I used R to develop ordinary least squares (OLS), stepwise, ridge, lasso, relaxed lasso, and elastic net regression models. I first used stepwise and OLS regression to develop a model and examine its residual…

Identifying the most influential food groups on COVID-19 recovery rate: exploratory data analysis and statistical modeling