Motivation
You are part of an analytics team that is trying to inform amenities design for an interstate highway by evaluation drivers’ preferences. In other words, what type of amenities should be build along the highway.
The data comes from an experiment where a promotional coupon is offered to a driver. The promotion is based on a type of amenity such as Bar, Carry out & Take away, Coffee House, or Restaurant. If the driver accepts the promotion it can be inferred that their preference is toward the same type of amenity service along the highway.
With the coupon a survey is also conducted. The survey describes different driving scenarios including the destination, current time, weather, passenger, etc., and then ask the driver whether he/she will accept the promotion.
Challenge
Run a predictive model to assess the likelihood of a driver accepting the promotional coupon. Then, interpret which amenities are more important for your positively predicted class.
In addition, using the team submission file, score 2684 drivers as with a 1 if the accept the coupon and 0 if they do not. This file will be evaluated using accuracy and f-score
Teams
Submission Requirements
This project is competition style, asking teams to build a predictive model, tune it, and score a set of 2685 records. In addition, teams need interpret and provide operations analytics recommendations which should emerge from the data and the results of a predictive modeling.
At the end, teams should complete the following:
Presentation: summary of the business problem, work completed, results, and recommendations Analytics case: what business problem are you solving (be creative) Model: a tuned model with performance metrics and results Scores: predictions actions on a set of new users. Visualize results as a histogram Grading
Model performance and technical approach: 50% Deliverables
Data review & feature engineering Review of models (please use a table to summarize attempts) Selected model and feature of importance Implications and recommendations team#_submission.csv (TIP: Don’t change the layout of the submission file, only add your predictions) Project and presentation due on
Datasets
Data for scoring and submission process
id: unique number giving to the driver doing the survey destination: No Urgent Place, Home, Work passanger: Alone, Friend(s), Kid(s), Partner (who are the passengers in the car) weather: Sunny, Rainy, Snowy time: 2PM, 10AM, 6PM, 7AM, 10PM coupon: Restaurant(<$20), Coffee House, Carry out & Take away, Bar, Restaurant($20-$50) expiration: 1d, 2h (the coupon expires in 1 day or in 2 hours) age: 21, 46, 26, 31, 41, 50plus, 36, below21 maritalStatus: Unmarried partner, Single, Married partner, Divorced, Widowed education: Some college - no degree, Bachelors degree, Associates degree, High School Graduate, Graduate degree (Masters or Doctorate), Some High School occupation: Unemployed, Architecture & Engineering, Student, Education&Training&Library, Healthcare Support,Healthcare Practitioners & Technical, Sales & Related, Management, Arts Design Entertainment Sports & Media, Computer & Mathematical, Life Physical Social Science, Personal Care & Service, Community & Social Services, Office & Administrative Support, Construction & Extraction, Legal, Retired,Installation Maintenance & Repair, Transportation & Material Moving,Business & Financial, Protective Service,Food Preparation & Serving Related, Production Occupations,Building & Grounds Cleaning & Maintenance, Farming Fishing & Forestry income: $37500 - $49999, $62500 - $74999, $12500 - $24999, $75000 - $87499, $50000 - $62499, $25000 - $37499, $100000 or More, $87500 - $99999, Less than $12500 Bar: never, less1, 1~3, gt8, nan4~8 (feature meaning: how many times do you go to a bar every month?) CoffeeHouse: never, less1, 4~8, 1~3, gt8, nan (feature meaning: how many times do you go to a coffeehouse every month?) CarryAway:n4~8, 1~3, gt8, less1, never (feature meaning: how many times do you get take-away food every month?) RestaurantLessThan20: 4~8, 1~3, less1, gt8, never (feature meaning: how many times do you go to a restaurant with an average expense per person of less than $20 every month?) Restaurant20To50: 1~3, less1, never, gt8, 4~8, nan (feature meaning: how many times do you go to a restaurant with average expense per person of $20 - $50 every month?) toCoupon_GEQ15min:0,1 (feature meaning: driving distance to the restaurant/bar for using the coupon is greater than 15 minutes) toCoupon_GEQ25min:0, 1 (feature meaning: driving distance to the restaurant/bar for using the coupon is greater than 25 minutes) direction_same:0, 1 (feature meaning: whether the restaurant/bar is in the same direction as your current destination) direction_opp:1, 0 (feature meaning: whether the restaurant/bar is in the same direction as your current destination) Y: 1, 0 (whether the coupon is accepted) Tips
When building recommendation for predictive models, you MUST consider feature of importance in your model
