shreyan-12345 / bank-term-deposit-subscription-prediction-model

We are provided with the client data such as : age of the client, their job type, their marital status, etc. Along with the client data, we are also provided with the information of the call such as the duration of the call, day and month of the call, etc. Given this information, our task is to predict if the client will subscribe to term deposit.

Bank-Term-Deposit-Subscription-Prediction-Model

We are provided with the client data such as : age of the client, their job type, their marital status, etc. Along with the client data, we are also provided with the information of the call such as the duration of the call, day and month of the call, etc. Given this information, our task is to predict if the client will subscribe to term deposit. Two datasets are given - test.csv and train.csv. The target variable is the 'subscribed' one. The test dataset doesn't contain the target variable. The model is first validated in the train dataset and then fitted into the test dataset for the predictions. This model has two algorithms in it - Logistic Regression and Decision tree. Both are for Supervised Learning. The first one is for linear decision boundary and the second one is for non-linear decision boundary.