Part - A
DOMAIN: Medical
Medical research university X is undergoing a deep research on patients with certain conditions. University has an internal AI team. Due to confidentiality the patient’s details and the conditions are masked by the client by providing different datasets to the AI team for developing a AIML model which can predict the condition of the patient depending on the received test results.
DATA DESCRIPTION:
The data consists of biomechanics features of the patients according to their current conditions. Each patient is represented in the data set by six biomechanics attributes derived from the shape and orientation of the condition to their body part.
PROJECT OBJECTIVE:
Demonstrate the ability to fetch, process and leverage data to generate useful predictions by training Supervised Learning algorithms.
Part B
DOMAIN: Banking, Marketing
CONTEXT: A bank X is on a massive digital transformation for all its departments. Bank has a growing customer base whee majority of them are liability customers (depositors) vs borrowers (asset customers). The bank is interested in expanding the borrowers base rapidly to bring in more business via loan interests. A campaign that the bank ran in last quarter showed an average single digit conversion rate. Digital transformation being the core strength of the business strategy, marketing department wants to devise effective campaigns with better target marketing to increase the conversion ratio to double digit with same budget as per last campaign.
DATA DICTIONARY:
ID: Customer ID
Age: Customer’s approximate age.
CustomerSince: Customer of the bank since. [unit is masked]
HighestSpend: Customer’s highest spend so far in one transaction. [unit is masked]
ZipCode: Customer’s zip code.
HiddenScore: A score associated to the customer which is masked by the bank as an IP.
MonthlyAverageSpend: Customer’s monthly average spend so far. [unit is masked]
Level: A level associated to the customer which is masked by the bank as an IP.
Mortgage: Customer’s mortgage. [unit is masked]
Security: Customer’s security asset with the bank. [unit is masked]
FixedDepositAccount: Customer’s fixed deposit account with the bank. [unit is masked]
InternetBanking: if the customer uses internet banking.
CreditCard: if the customer uses bank’s credit card.
LoanOnCard: if the customer has a loan on credit card.
PROJECT OBJECTIVE: To build a Machine Learning model to perform focused marketing by predicting the potential customers who will convert using the historical dataset.