Project 2: Prediction Challenge

DAPI Project 2: Predictions for Highway Operations

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DAPI Project 2: Predictions for Highway Operations

DAPI Analytics Project

J.C. Bonilla

Project and presentation due on
4/19/2024
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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 your model to recommend amenities for an upcoming highway. Recommendations and insights must be linked to the features of importance of your selected model.

In addition, we will evalute the performance of your model using the team submission file. This file consists a prediction of 2684 drivers with a 1 if the accept the coupon and 0 if they do not. This file will be evaluated using accuracy and f-score

Project and presentation due on
4/19/2024
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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 and submit the following:

Data Review, EDA, and Feature engineering: your aproach for understanding, preparing and transforming data pre-modeling

A 15min Presentation: summary of the business problem, work completed, results, and recommendations

Model Generation in R: development of models, tuning, and selection a best model

Predictions: predictions on a set of new users. Visualize results as a histogram.Use this file name: team#_submission.csv (TIP: Don’t change the layout of the submission file, only add your predictions)

Grading

Presentation: 20%

Model performance and technical approach: 50%

Recommendations: 30%

Presentations Expectations

Presentation which includes :15min

Business problem you are trying to solve

Your approach premodeling: (1) Data review (2) EDA and (3) Feature Engineering

Review of modeling attempts(please use a table to summarize attempts)

Selected model and feature of importance

Model interepretation

Implications and recommendations

Datasets

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`Data for model training`⁠
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https://drive.google.com/file/d/127K7mX2zsxZ1sxdLsJPkj4yNXs4RhqBp/view⁠

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Data for scoring and submission process

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https://drive.google.com/drive/folders/1h7ThlBk73BRfdOmQowXvjzxfpx8G-m8k⁠

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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)