Predictive Analytics for a Bank Marketing Campaign

Objective

This project will build a machine learning model to predict the outcome of a marketing campaign for each client of a bank. This will allow the bank to focus its marketing efforts on the clients that are most likely to respond positively to the campaign.

Four types of classification algorithms will be tested:

1. Logistic Regression
2. Random Decision Forest
3. K-Nearest Neighbours
4. Support Vector Classification

Repository Contents

This repository contains a report for each stage of the project:

1. Project Proposal
2. Data Cleaning, Exploration and Preparation
3. Feature Selection
4. Predictive Modeling
5. Project Presentation
6. Final Project Report

It also contains the Python code (within a Jupyter notebook) and the dataset.

Executing the Code

Download the Jupyter notebook and the dataset files from this repository.

Place the dataset in the top directory of the “C” drive, or modify the Jupyter notebook so that it reads the dataset from the location of your choice.