deep-learning-bank-deposit: Bank Marketing 🏦

Topic: Development of An Enhanced Deep Learning Model to Predict Client's Intention to Subscribe to the Bank's Term Deposit

No	Dataset	Information
1	URL	https://www.kaggle.com/datasets/prakharrathi25/banking-dataset-marketing-targets?select=train.csv
2	Dataset Name	Portuguese Bank Direct Marketing
3	File Type	csv file
4	Observation	45,211
5	Features	17
6	Data label	“Yes” referred to bank clients successfully subscribing to the term deposit. “No” referred to bank clients that rejected the subscription.

Introduction

A brief overview into the project domain

• Marketing functions have always been playing a central role in the financial industry, especially in the banking sector
• Retail banks often used direct marketing as a telemarketing strategy to contact potential customers and sell their products
• Crucial for retail banks to ensure that they are targeting groups with a high chance of success

What more could be done?

• Data analysis. Understand the consumers needs and preferences!
• Leverage on deep learning techniques to make better predictions

What is the problem statement of the project?

• Retail banks urgently need a reliable and accurate machine learning model as a competitive advantage to help them predict customer intention to subscribe to term deposits
• Offerings of financial products like providing “term deposits” slightly vary from the other retail banks. In other words, every bank offerings are almost identical)

Aims & Objectives (What do I aim to achive?) 🌟

The Aims

• The overall aim of this project is to enhance retail banks’ marketing effectiveness and reduce marketing costs through the development of a reliable deep learning machine learning model to accurately predict bank clients’ possibilities in subscribing to bank term deposit.

The Objectives

• To identify features that play a major role in affecting the bank clients’ intention to subscribe to the bank term deposit.
• To develop a reliable deep learning technique and predict bank clients’ intention to subscribe to a financial product - bank term deposit.
• To evaluate the performance of the deep learning models with the evaluation metrics benchmarked by past studies.

Initial Data Exploration & Exploratory Data Analysis (EDA)

• Finding out the following:
  • What is the data shape?
  • Are there any missing values?
  • How many categorical / numerical variables are there?
  • What is the dependent variable, how's the distribution?
  • Are there any class imbalance issue?
  • and many more...!
• EDA --> Univariate Analysis & Bivariate Analysis

Data Pre-Processing

• Correlation Analysis
• Label Encoding
• One Hot Encoding
  • Label vs. One-Hot Encoding Short Explaination on Kaggle
• Data Partitioning
• Class Re-Sampling
• Data Normalisation
• Feature Selection

Modelling

Models Developed

• 1 Baseline Model
• 4 ANN Model
• 2 RNN Model
• 1 LSTM Model

Proposed Deep Learning Model Flowchart

Hyperparameters Involved

• Learning Rate ✅
• Epoch ✅
• Dropout ✅
• Batch Size ✅

Performance Evaluation

Critical Analysis

• Among all the models developed, the highest accuracy of 90.29% ✅ was achieved by model 4
• Class imbalance issue was resolved with the application of the SMOTE technique
  • What is SMOTE technique?
• Some evaluation metrics carry more weight as compared to others
• Focus of the retail bank should be on correctly predicting the bank clients that would subscribe to the deposits
• Hence, high sensitivity or TPR will be much more important
  • Banks prefer to correctly predict clients that would most likely purchase their term deposits
  • Banks stand to lose out more in terms of the sales opportunity if highly potential clients are missed out by the model
  • On the other hand, banks could afford to wrongly identifying not interested clients as highly likely to purchase