The objective of this notebook is to answer whether or not a customer will subscribe to the term_deposit depending on a set of features.

The data used herein is found and described on the UCI Machine Learning Repository http://archive.ics.uci.edu/ml/datasets/Bank+Marketing#

It consists of the following columns:

• age (numeric)
• job : type of job (categorical: 'admin.','blue-collar','entrepreneur','housemaid','management','retired','self-employed','services','student','technician','unemployed','unknown')
• marital : marital status (categorical: 'divorced','married','single','unknown'; note: 'divorced' means divorced or widowed)
• education (categorical: 'basic.4y','basic.6y','basic.9y','high.school','illiterate','professional.course','university.degree','unknown')
• default: has credit in default? (categorical: 'no','yes','unknown')
• housing: has housing loan? (categorical: 'no','yes','unknown')
• loan: has personal loan? (categorical: 'no','yes','unknown')

• contact: contact communication type (categorical: 'cellular','telephone')
• month: last contact month of year (categorical: 'jan', 'feb', 'mar', ..., 'nov', 'dec')
• day_of_week: last contact day of the week (categorical: 'mon','tue','wed','thu','fri')
• duration: last contact duration, in seconds (numeric). Important note: this attribute highly affects the output target (e.g., if duration=0 then y='no'). Yet, the duration is not known before a call is performed. Also, after the end of the call y is obviously known. Thus, this input should only be included for benchmark purposes and should be discarded if the intention is to have a realistic predictive model.

• campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact)
• pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric; 999 means client was not previously contacted)
• previous: number of contacts performed before this campaign and for this client (numeric)
• poutcome: outcome of the previous marketing campaign (categorical: 'failure','nonexistent','success')

• emp.var.rate: employment variation rate - quarterly indicator (numeric)
• cons.price.idx: consumer price index - monthly indicator (numeric)
• cons.conf.idx: consumer confidence index - monthly indicator (numeric)
• euribor3m: euribor 3 month rate - daily indicator (numeric)
• nr.employed: number of employees - quarterly indicator (numeric)

To prepare the data for the algorithms , a Principal component approach is applied on the data which has been encoded using onehot encoding

The methods applied include a Logistic Regression algorithm , a Gradient Booster algorithm , and a mulitilayer feed forward neural network using k-fold cross validation to continously correct the error on the previous training set. To identify the best model , the f1 score is useful as the target data is has an imbalanced distribution

The problems with this approach is the use of k-fold cross validation. This is because the data is heavily imbalanced and could lead to da division by 0 error as explained here: https://sebastianraschka.com/faq/docs/computing-the-f1-score.html

This can be addressed by using a stratified fold instead of a k-fold validation approach

From the evaluation of the performance , the multilayer perceptron is the best model for this problem and answers appropriately which kind of customer is likely to subscribe >.5 of the time .

1. Download as zip folder
2. Extract and navigate to folder cd Term_deposits_uptake_predictor
3. Run the module: python3 main.py