J700070 / Data_Science-Tasa_de_abandono_telecom

For this project, I was interestested in using Telecom Churn data to better understand: 1. What factors are important for predicting customer churn? 2. How well can we predict customer churn? 3. How different models are affected by the imbalance in the data.

Data Science: Telecom Churn Prediction Project

Table of Contents

1. Installation
2. Project Motivation
3. File Descriptions

Installation

The code should run with no issues using Python versions 3.9.

Project Motivation

For this project, I was interestested in using Telecom Churn data to better understand:

1. What factors are important for predicting customer churn?
2. How well can we predict customer churn?
3. How different models are affected by the imbalance in the data.

File Descriptions

There is one notebook available here to showcase work related to the above questions. The notebook is exploratory in searching through the data pertaining to the questions above, and proposes a machine learning solution for predicting customer churn. Markdown cells were used to assist in walking through the thought process for individual steps. The dataset used in this project is included as churn.csv.