Multiple Classifier to predict the decision of client in front of a deposit term, and defining the best axes to work on to attract more customers

In the following python project, the data concerns a bank's marketing strategy, The purpose is to figure out how possible this strategy could attract more clients to term deposit subscription. A term deposit is a fixed-term investment where the customer deposits funds in an account at a financial institution, and carry short-term maturities ranging from one month to a few years.

The data we got is mixed data, it contains in total 21 categorical and numerical features. The project aims to exploit these informations about clients to predict whether they will choose a term deposit, to specify the principal axes on which the bank can rely on to attract more customers for such a product.

The project aims for EXPLORATION and then EXPLOITATION. We will import the bank marketing data, manipulate, and perform some descriptive statistics, and graphical exploring, then prepare it for the step of modeling and concluding main results.

The classifiers used are :

• KNN classifier
• Decision Tree classifier
• Bagging classifier
• Random Forest classifier -Logistic Regression

The reference of the data and the description of its features is found in the following link: https://archive.ics.uci.edu/ml/datasets/Bank+Marketing