Explore the numerical predictors and outcome (FARE) by creating a correlation table, heat map, and examining some scatterplots between FARE and those predictors.

1 Assignment 3 ICBE486 Special Topics in Business Economics Due: November 12 , 202 1 before Noon. Instructions There is 1 Big question with 3 sub -question on this assignment. All questions involve coding, so start early. Please submit your completed homework to Google Classroom by Noon, Fri day, November 12 , 202 1 with subject: ” ICBE 486 –your name –” Submit your homework as the Jupiter Notebook code and figure, all in a.zip folder. (so the TA’s can easily split it up for grading). Include your name and email address on each set. Question1: Predicting Airfare on New Routes. The followi ng problem takes place in the United States in the late 1990s, when many major US cities were facing issues with airport congestion, partly as a result of the 1978 deregulation of airlines. Both fares and routes were freed from regulation, and low -fare car riers such as Southwest (SW) began competing on existing routes and starting nonstop service on routes that previously lacked it. Building completely new airports is generally not feasible, but sometimes decommissioned military bases or smaller municipal a irports can be reconfigured as regional or larger commercial airports. There are numerous players and interests involved in the issue (airlines, city, state and federal authorities, civic groups, the military, airport operators), and an aviation consulting firm is seeking advisory contracts with these players. The firm needs predictive models to support its consulting service. One thing the firm might want to be able to predict is fares, in the event a new airport is brought into service. The firm starts wi th the file Airfares.csv, which contains real data that were collected between Q3 — 1996 and Q2 — 1997. The variables in these data are listed in Table 6.13, and are believed to be important in predicting FARE. Some airport -to -airport data are available, but m ost data are at the city -to -city level. One question is that will be of interest in the analysis is the effect that the presence or absence of Southwest has on FARE. Description of Variables for Airfare Example S_CODE Starting airport’s code S_CITY Starting city E_CODE Ending airport’s code E_CITY Ending city COUPON Average number of coupons (a one -coupon flight is a nonstop flight, a two -coupon flight is a one -stop flight, etc.) for that route NEW Number of new carriers entering that route between Q3 — 1996 and Q2 — 1997 VACATION Whether (Yes) or not (No) a vacation route SW Whether (Yes) or not (No) Southwest Airlines serves that route 2 HI Herfindahl index: measure of ma rket concentration S_INCOME Starting city’s average personal income E_INCOME Ending city’s average personal income S_POP Starting city’s population E_POP Ending city’s population SLOT Whether or not either endpoint airport is slot -controlled (this is a measure of airport congestion) GATE Whether or not either endpoint airport has gate constraints (this is another measure of airport congestion) DISTANCE Distance between two endpoint airports in miles PAX Number of passengers on that route during period of data collection FARE Average fare on that route Tasks: (1) Explore the numerical predictors and outcome (F ARE) by creating a correlation table , heat map, and examining some scatterplots between FARE and those predictors. What seems to be the best single predictor of FARE? (2) Explore the categorical predictors (Excluding the first four) by c reat ing the pivot tab le with the average fare in each category. Which categorical predictors seems best for predicting FARE? (3) Find a model with the regression model for predicting the average fare on a new route: (3.1) Convert categorical variables into dummy variables. Then, parti tion the data into training and test sets. The model will be fit to the training data and evaluated on the test set. (3.2) Use regression to predict the model . You can ignore the first four predictors (S_CODE,S_CITY,E_CODE,E_CITY). Report the estimated results. Then, compute the RMSE and MAE of train and test data. (3.3) Use the Lasso Regression to reduce the number of predictors. Report the estimated results. Then, compute the RMSE and MAE of train and test data. (3.4) Again, now use the Ridge Regression to reduce the numb er of predictors. Report the estimated results. Then, compute the RMSE and MAE of train and test data. (3.5) Compare the predictive performance of the three models: Regression, Lasso, and Ridge. Which one you select? And Why?.

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