I hereby confirm that I have uploaded my essay for assignment "The MSD Case"

Run the Python code again in Colab. No need to run code snippets that are related to plotly. Because the plots don’t seem to appear in Colab.

Let us focus on the forecasting of the Paris demand. In the existing code, we use 61 days to evaluate the forecast performance of a model with and without holiday effects. It turns out that the model with holiday effects dominates the initial model, and this makes intuitively sense because the model becomes more flexible. To make this point stronger, do the forecast comparison again but rather for 61 days we ask you to change this to the 90 days. 

What is the root mean square error without holidays ?

Run the Python code again in Colab. No need to run code snippets that are related to plotly. Because the plots don’t seem to appear in Colab.

Let us focus on the forecasting of the Paris demand. In the existing code, we use 61 days to evaluate the forecast performance of a model with and without holiday effects. It turns out that the model with holiday effects dominates the initial model, and this makes intuitively sense because the model becomes more flexible. To make this point stronger, do the forecast comparison again but rather for 61 days we ask you to change this to the 90 days. 

What is the mean absolute error with holidays ?

Run the spreadsheet solver VRP_Spreadsheet_Solver.xlsm again, but in the location sheet increase the delivery amount by 10 for each of the 20 customers. 

-What is the total profit?

Run the spreadsheet solver VRP_Spreadsheet_Solver.xlsm again, but in the location sheet increase the delivery amount by 10 for each of the 20 customers. 

-How many vehicles are required to deliver all the packages?

I completed this Phase and I am ready to access the next Phase 

We have trained a few supervised learning algorithms to predict if an individual will subscribe to a new Netflix service that is less expensive but contains advertisements. This allows reaching out to populations that don’t watch traditional television anymore. The test set target data and the corresponding predictions of three algorithms (random forest, neural network, k-nearest neighbors) are given in supervised_learning_computing_errors.xlsx. Given that the performance metric is accuracy as measured by the percentage of correct predictions, which is your preferred algorithm?

We have trained a few supervised learning algorithms to predict if an individual will subscribe to a new Netflix service that is less expensive but contains advertisements. This allows reaching out to populations that don’t watch traditional television anymore. The test set target data and the corresponding predictions of three algorithms (random forest, neural network, k-nearest neighbors) are given in supervised_learning_computing_errors.xlsx. Given that the performance metric is accuracy as measured by the percentage of correct predictions, which is your preferred algorithm?

We have trained a few supervised learning algorithms to predict if an individual will subscribe to a new Netflix service that is less expensive but contains advertisements. This allows reaching out to populations that don’t watch traditional television anymore. The test set target data and the corresponding predictions of three algorithms (random forest, neural network, k-nearest neighbors) are given in supervised_learning_computing_errors.xlsx. Given that the performance metric is accuracy as measured by the percentage of correct predictions, which is your preferred algorithm?

We have trained a few supervised learning algorithms to predict if an individual will subscribe to a new Netflix service that is less expensive but contains advertisements. This allows reaching out to populations that don’t watch traditional television anymore. The test set target data and the corresponding predictions of three algorithms (random forest, neural network, k-nearest neighbors) are given in supervised_learning_computing_errors.xlsx. Given that the performance metric is accuracy as measured by the percentage of correct predictions, which is your preferred algorithm?