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Project 2: Prediction Challenge
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DAPI Project 2: Predictions for Highway Operations

DAPI Analytics Project

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
Search
Team
Name
Email
One
5
Ali,Raveed
Bhosale,Parth
Modi,Devansh
Zhao,Shanshan
Two
5
Arumugam,Sricharan
Bodapati,sasank
Patnam,Akhil
Zhang,Zicheng
Three
5
Chauhan,Gargi
Chen,Lu Yu
Cordero,Jose Miguel
Dhanrajani,Gopal
Singh,Trishita
Four
5
Chen,Qing
El Boulli Rguibi,Yosra
Gandhi,Rushil
Gohil,Mehul Suket
Talwande,Purva
Five
5
Gowda,Hithesh
Lin,Yuqing
Manoharan,Siddharth
Tian,Yuting
Six
5
Mathialagan,Poorna Kumar
Mehta,Yashita
Woo,Elizabeth

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
Presentation: 20%
Model performance and technical approach: 50%
Recommendations: 30%

Deliverables
Presentation:10min
Business problem
Data review & feature engineering
Review of models (please use a table to summarize attempts)
Selected model and feature of importance
Scores distributions
Implications and recommendations
Slides
R code
team#_submission.csv (TIP: Don’t change the layout of the submission file, only add your predictions)

Datasets


Data for scoring and submission available by
4/1/2022

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
temperature:55, 80, 30
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)
gender: Female, Male
age: 21, 46, 26, 31, 41, 50plus, 36, below21
maritalStatus: Unmarried partner, Single, Married partner, Divorced, Widowed
has_Children:1, 0
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

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