Bank Customer Classification Based on Reactions to Marketing Campaigns

Overview

This project aims to classify bank customers based on their reactions to various marketing campaigns. By analyzing customer data, we can predict how likely they are to respond positively to different types of marketing strategies, helping the bank improve its campaign targeting and overall marketing effectiveness.

Table of Contents

1.	Project Description
2.	Dataset
3.	Installation
4.	Usage
5.	Methodology
6.	Results
7.	Contributing
8.	License
9.	Contact

Project Description

The project uses historical marketing data from the bank, including customer demographics and previous marketing campaign responses. It involves:

•	Data preprocessing and cleaning
•	Exploratory Data Analysis (EDA)
•	Feature engineering
•	Building classification models to predict customer behavior
•	Model evaluation and tuning

The ultimate goal is to identify key customer segments and predict how they will respond to future campaigns.

Dataset

The dataset used for this project contains information about bank customers and their response to various marketing campaigns. The key features include:

•	Customer demographics (age, job, marital status, education, etc.)
•	Bank-related information (balance, number of loans, etc.)
•	Previous interactions with the bank’s marketing campaigns
•	Response to the marketing campaign (target variable)