This project involves the use of machine learning to analyze and predict outcomes of direct marketing campaigns based on banking data. The principal aim of these models is to predict client behavior, particularly whether they will subscribe to a term deposit or not.This project also compares a fully hypertuned logistic regression model with a mlp classifier to compare on the idea of ML vs DL.
The dataset used in this project is the Bank Marketing dataset from the UCI Machine Learning Repository.
The models used in this project are Logistic Regression and XGBoost.
To run this project, follow these steps:
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Clone this repository.
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Install the required dependencies using pip:
pip install -r requirements.txt -
Run the main script:
python main.py
This project is licensed under the terms of the MIT license.