This repository contains the practical application assignment for Module 5. Additional details are provided in this activity's Juniper Notebook.

Notebook link: https://github.com/ksolivenhub/BH-PCMLAI-Module5/blob/de536bc25e102c975dfbb53ca70b43e1b0e814d1/prompt-ksoliven-fin.ipynb

• There is a higher percentage of people not accepting Bar Coupon.
  • Delta is at 17.62%
• The acceptance rate of people going less than 3 times monthly is more likely than more than 3 times monthly
  • Acceptance rate at ~81%
• Compared to all others who accepted the Bar Coupon, those who go to Bar once (1) monthly and over the age of 25 have a significant presence in the data set
  • Acceptance rate at ~28%
• The acceptance rate of people who went to the Bar once (monthly) and does not have a passenger kid has a significant presence in the data set
  • Acceptance rate at ~48%
  • For this data sample, it is not affected by Age or Marital Status as the values are equal
• An observation made is that Bar > 3 does not contain most of the data sample entries

Based on this sampled data, Drivers who accepted the coupons could be hypothesized as follows:

1. Bar < 3 contains most of data sample entries
2. Most drivers that go to the bar less than thrice (3) monthly is more likely to accept/use the coupon
3. Drivers are more likely to accept/use coupon if they have a Kid as a passenger

Core question: What are the best performing coupon groups?

1. Are the coupon expiration dates effective?
2. What is the best time/season to release the coupons for marketing?
3. Who to send the coupons?
   • This is based on the following factors:
     • Income
     • Marital Status
     • Has Children
     • Education
     • Gender
     • Age

*Based on the sampled data and the overall results of this analysis from Q1 to Q3, the following assertions can be made:

1. The Coupon types that have the best performance (based on analyzed features) are the following:
   • Restaurant(<20)
   • Coffee House
   • Carry out & Take away
2. The higher the Coupon validity duration, the higher chance that consumers will accept the coupon
   • A likely scenario is if the expiry is too long, the acceptance rate will decrease
     • The condition above most likely have an ideal threshold/range to maintain a positive acceptance rate
     • There is not enough data to predict this parameter
3. The data set provided for this activity contains useful information to predict consumer behaviour in a Sunny weather
   • There is a positive correlation between temperature and coupon acceptance
4. For the best performing Coupon groups, it is ideal to offer them between 10 AM to 6 PM as a high coupon acceptance were observed
5. Cheap Restaurants and Coffee House have similar characteristics relative to time and temperature on a Sunny day
   • Visits are more frequent before noon (7AM to 10PM) on a warm temperature
   • After 6 PM, visits for these parameters are also seen during the cold temperature range
6. Carry out & Take way is not dependent on temperature
7. To maximize potential profit, any marketing campaign related to these best perfoming coupons should primarily target the following consumers:
   • Age between 21-31 and 50+
   • Marital Status of Single, Unmarried, and Married Partners
   • No Children
     • It would be interesting to explore/confirm if this parameter's acceptance rate is indeed higher when driving with Kids
   • Income is below $62,500 and above $100,000

1. Consider that the following types of establishments will generate the most coupon acceptance rate and leverage this knowledge to the owner's advantage
   • Restaurant(<20)
   • Coffee House
   • Carry out & Take away
2. Use the predicted parameters above to improve success rates of Coupon marketing campaigns
3. Gather more data related to coupon acceptance in other weather/time conditions to create a full view of which businesses are running successful campaigns.
4. Explore the Trip Attributes in relation with Coupon and User Attributes as this was not incorporated on this analysis.