This problem involves building a binary classification model to predict the probability of a client subscribing to a term deposit. Given a set of features, such as client data and last contact information, etc., and a binary output feature, it is a supervised learning problem.
The goal is to minimise the calls by eliminating those who will not subscribe to the Term Deposit. This way, a company can save huge amounts of money and redirect to where necessary.
The Repository contains two files namely train.csv and test.csv for Training and Testing the model.
Alternatively, you can download the dataset from here.
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Clone the repository using
git clone https://github.com/mukulvain/Term-Deposit-Prediction.git -
Install the Dependencies using
pip install -r requirements.txt
- Mukul Jain (200001050)
- Nilay Ganvit (200001053)
Under the guidance of Dr. Aruna Tiwari, Professor, Computer Science and Engineering, IIT Indore.
- Patwary, M. J., Akter, S., Alam, M. B., & Karim, A. R. (2021). Bank Deposit Prediction Using Ensemble Learning. Artificial Intelligence Evolution, 42-51.
- Muslim, M. A., Dasril, Y., Alamsyah, A., & Mustaqim, T. (2021, June). Bank predictions for prospective long-term deposit investors using machine learning LightGBM and SMOTE. In Journal of Physics: Conference Series (Vol. 1918, No. 4, p. 042143). IOP Publishing.
- Term Deposit Subscription Prediction | Thean C. Lim.
- Borugadda, P., Nandru, P., & Madhavaiah, C. (2021). Predicting the success of bank telemarketing for selling long-term deposits: An application of machine learning algorithms. St. Theresa Journal of Humanities and Social Sciences, 7(1), 91-108.