Reference no: EM133696813

Artificial Intelligence and Insights

Specifications

The data is related to direct marketing campaigns of a banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be ('yes') or not ('no') subscribed. Your task is to use deep learning to develop an Al model (i.e., predictive model) which is able to predict if the client will subscribe (yes/no) to term deposit (variable y).

Use bank marketing dataset above to:

• Implement a data cleansing pipeline including (e.g., (1) automatically reading a csv file, (2) searching for missing values and (3) imputation of missing values if there are any)
• Implement a feature selection technique in R as part of your assignment. You are allowed to use the existing feature selection models in R
or using the published algorithms and implementing them in R

• Apply a deep learning model on your cleaned data to predict likelihood of customer to subscribe into a term deposit

• Report deep learning performance Submit a zip file containing:

• R code file that contains the implementation of above requirements.

• A word file only (not pdf) that documents your R code. Include deep learning performance report (4th part mentioned above) in the word file.