- Mateusz Jakubczak
- Krzysztof Olipra
- Karol Oleszek
Will client open a deposit? - binary classification based on telemarketing data.
Project structure:
- bank.csv - UCI dataset
- ssn.py - artificial neural network implementation
- compare_methods.py - comparative study of alternative methods
- compare_methods_report.txt - detailed report from comparative study
Data was downloaded from open machine learning dataset repository.
Dataset was gathered from marketing activities of Portugeese commercial bank.
Client cold calls was main marketing activity.
Classification objective:
- Predict whether client will open a deposit after telemarketing call
- age
- job
- is married
- education
- has defaulted on loan
- has mortgage
- has loans
- has home phone
- time from last contact
- weekday of last contact
- call time
- number of previous client calls
- were previous calls successful
- quarterly unemployment variance
- monthly CPI
- Consumer Confidence Index
- euribor 3 rate
- employees in economy
S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014
S. Moro, R. Laureano and P. Cortez. Using Data Mining for Bank Direct Marketing: An Application of the CRISP-DM Methodology. In P. Novais et al. (Eds.), Proceedings of the European Simulation and Modelling Conference - ESM'2011, pp. 117-121, Guimaraes, Portugal, October, 2011. EUROSIS. [bank.zip]
Other approaches:
- Decision trees
- Naive Bayes
- K-nearest neighbors
- Support Vector Machines
| Method | Accuracy |
|---|---|
| SSN | 64% |
| Decision tree | 68% |
| Naive Bayes | 68% |
| KNN | 60% |
| SVC | 58% |
