Replicating Gertler-Karadi 2015 paper using ECB data. Here i do the first part ,using the instrument OIS_1y and testing it on policy variable de1y.

Vanguard ETF monthlyy returns

Analysing the risk premium for mid cap stocks listed on Nasdaq

Using ADS Business Confidence Index

Fama-Macbeth using rolling window for Beta Estimation , based on the usual cross-sectional regression and insights from Prof Kevin Shepherd

Fama-Macbeth regression with stocks following the procedure by Prof Kevin Shepherd

Rolling Window CAPM mode

Performance over the period time, Effects of Fama-French and S&P 500 Index

Portfolio performance for selected consumer discretionary stocks

Consumer Discretionary Stocks and Portfolio performance based on equal weighted scheme

Selected Energy Stocks and Portfolio Performance based on equal Weighted Scheme

Selected Health Care Stocks and Portfolio performance based on Equal Weighted scheme

Selected Banking Stocks and Portfolio Performance over the period of time with equal weighted scheme

Selected Tech Stocks and using equal Weighted Scheme to see their performance

Using the data on ETFs, replicating Jonathan Regenstein for 2021

Understanding of factor model with portfolio

Here we see the Performance of it over the period of time

Allokation für Golden Butterfly Portfolio- Visualization of Portfolio Returns and its Komponente and followed by invesment growth

Practice of All Weather Portfolio based on Ray Dalio' approach

PimaIndianDiabetes2 dataset - is used and the model fit is the xgboost, and the preprocessing uses smote for imbalanced classification problem from the themis package. There has been an improvement in the results in comparison to those done earlier

PimaIndianDiabetes2 dataset - using Random forest model with Smote

Understanding the imbalanced nature of the classification data, so subsequent changes have been done in the preprocessing. model used have been random forest from ranger library

First part of practice of mixed models using the lme4 package for random effects

Random Forest model has been used

the data is on diabetes from mlbench library. Xgboost model has been used for prediction

Data is PimaDiabetes2 , which is corrected version of PimaDiabetes dataset. Random Forest model has been applied to predict the occurrence of diabetes.

Breast Cancer data, which is a unbalanced classification dataset. the mode used has been random forest , and sampling techniques - under, over and rose have been used to resolve the issue of unbalanced classification.

Complete Analysis with Tidy models - model used is Random Forest(ranger package)

BreastCancer data from library mlbench, used Tidymodels

Breast Cancer data from UCI repository, it is a large dataset. The key aspect is it is a unbalanced classification problem.