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. |
- 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
- Data analysis. Understand the consumers needs and preferences!
- Leverage on deep learning techniques to make better predictions
- 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)
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.
- 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
- Correlation Analysis
- Label Encoding
- One Hot Encoding
- Data Partitioning
- Class Re-Sampling
- Data Normalisation
- Feature Selection
- 1 Baseline Model
- 4 ANN Model
- 2 RNN Model
- 1 LSTM Model
- Learning Rate ✅
- Epoch ✅
- Dropout ✅
- Batch Size ✅
- 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
- 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