Context You are part of a consulting team collaborating with an airline company that aims to boost its service quality by accurately predicting customer satisfaction levels. The company is committed to utilizing advanced analytics, including decision tree and logistic regression models, to identify and understand the primary factors that drive customer satisfaction. This strategic initiative is designed to refine customer service approaches and tailor marketing campaigns more effectively, ultimately enhancing overall customer satisfaction. Data The provided dataset captures various factors related to customer satisfaction, such as customer type, age, travel type, class, flight distance, and ratings for various services $($seat comfort, food and drink, gate location, etc.$).$ The data contains a survey conducted with $129\mathrm{.}881$ customers during a one$-$year time period. The data dictionary is provided below. $$ satisfaction: Customer satisfaction status $($Factor: "satisfied", "dissatisfied"$).$ Customer Type: Type of customer $($Factor: "Loyal Customer", "Disloyal Customer"$).$ Age: Age of the customer $($Integer$).$ Type of Travel: Reason for travel $($Factor: "Personal Travel", "Business Travel"$).$ Class: Travel class of the customer $($Factor: "Eco", "Eco Plus", "Business"$).$ Flight Distance: Distance of the flight in miles $($Integer$).$ Seat comfort: Rating for seat comfort $($Integer: $0-5).$ Departure$/$Arrival time convenient: Rating for departure$/$arrival time convenience $($Integer: $0-5).$ Food and drink: Rating for food and drink $($Integer: $0-5).$ Gate location: Rating for the gate location $($Integer: $0-5).$ Inflight wifi service: Rating for inflight wifi service $($Integer: $0-5).$ Inflight entertainment: Rating for inflight entertainment $($Integer: $0-5).$ Online support: Rating for online support $($Integer: $0-5).$ Ease of Online booking: Rating for ease of online booking $($Integer: $0-5).$ On$-$board service: Rating for on$-$board service $($Integer: $0-5).$ Leg room service: Rating for leg room service $($Integer: $0-5).$ Baggage handling: Rating for baggage handling $($Integer: $0-5).$ Checkin service: Rating for check$-$in service $($Integer: $0-5).$ Cleanliness: Rating for cleanliness $($Integer: $0-5).$ Online boarding: Rating for online boarding $($Integer: $0-5).$ Departure Delay in Minutes: Departure delay in minutes $($Integer$).$ Arrival Delay in Minutes: Arrival delay in minutes $($Integer$).$ Assignment Overview Your task is to construct a decision tree and logistic regression models to predict customer satisfaction. You will explore how various factors influence customer satisfaction, gaining practical experience in implementing these algorithms for classification tasks. Business Information Systems Department BIS$581$ Business Data Analytics Prof. Santiago Aguirre, Ph$.$D$.$ Tasks A$.$ Submit a Word file generated from R Markdown $($click on document$).$ and save to a Word $1.$ Fit a multiple logistic regression model using $$satisfaction$$ as the dependent variable and all other variables as predictors. Use $70\%$ as training and $30\%$ as validation. $2.$ Interpret the coefficients and assess the significance of the predictors $($z$-$scores and p$-$values$).3.$ Evaluate the logistic regression model fit using the confusion matrix and accuracy. $4.$ Fit a decision trees model using Random Forest method. Use $$satisfaction$$ as the dependent variable and all other variables as predictors. Use $70\%$ as training and $30\%$ as validation. $5.$ Interpret the significance of the predictors based on Mean Decrease Accuracy and Mean Decrease Gini. $6.$ Evaluate the decision trees model fit using the confusion matrix and accuracy. $7.$ Model Comparison: Compare the logistic regression and decision trees models. Discuss which model performs better and why.