R markdown - Samenvattingen, Aantekeningen en Examens

The main objective is to write a fully executed R-Markdown program performing clustering using DBSCAN and Mixture model on the MNIST / Fashion MNIST (apparel) images that are 28 x 28 pixels resolution. Make sure to describe the final hyperparameter settings of all algorithms that were used for comparison purposes. You are required to clearly display and explain the models that were run for this task and their effect on the reduction of the Cost Function.

Write a fully executed R-Markdown program and submit a pdf / word or html file performing classification task using Random Forest and XGBoost algorithms on the Binary response variable from the Santander Bank Case Study. Make sure to try various hyperparameter settings of the two algorithms to find the best available models. You are required to clearly display and explain the models that were run for this task and their effect on the reduction of the Cost Function.

The main objective for this week is to write a fully executed R-Markdown program performing clustering using SOM and LLE on the image data containing MNIST (digits) and Fashion MNIST (apparel) images that are 28 x 28 pixels resolution. Make sure to describe the final hyperparameter settings of all algorithms that were used for comparison purposes.

Write a fully executed R-Markdown program and submit a pdf / word or html file performing regression task using GAM algorithms on the primary response variable in the Nutrition Case Study. Make sure to try various hyperparameter settings to find the best available models. You are required to clearly display and explain the models that were run for this task and their effect on the reduction of the Cost Function.

The main objective is to write a fully executed R-Markdown program performing regression prediction for the response variable using the best models found for LASSO, Ridge and Elastic Net techniques predicting the response variable in the Nutrition case study. Make sure to describe the final hyperparameter settings of all algorithms that were used for comparison purposes. You are required to clearly display and explain the models that were run for this task and their effect on the reduction of t...

Write a formal report on your findings from the last several weeks for the classification of the Santander Bank Case Study. The main objective is to write a fully executed R-Markdown program performing classification using the best models found for logistic regression, SVM, Random Forest and XGBoost algorithms, and comparing the values of their cost functions and accuracy scores. Make sure to describe the final hyperparameter settings of all algorithms that were used for comparison purposes. ...

The main objective is to write a fully executed R-Markdown program performing regression prediction for the response variable using the best models found for kNN, Random Forest and XGBoost techniques predicting the response variable in the Nutrition case study. Make sure to describe the final hyperparameter settings of all algorithms that were used for comparison purposes. You are required to clearly display and explain the models that were run for this task and their effect on the reduction of...

The main objective is to write a fully executed R-Markdown program performing EDA on the given data on the human activity recognition experiment collected through smart watches.

Write a fully executed R-Markdown program and submit a pdf / word or html file performing classification task on the Binary response variable from the Santander Bank Case Study. Make sure to try several permutations of the model before finding the best available model. You are required to clearly display and explain the models that were run for this task.

Write a fully executed R-Markdown program and submit a pdf / word or html file performing classification task on the Binary response variable from the Santander Bank Case Study. Make sure to try various hyperparameters of the SVM algorithm to find the best available model. You are required to clearly display and explain the models that were run for this task and their effect on the reduction of the Cost Function.