https://github.com/rubynixx/bpp_telecomm_churn

Programming assignment on predicting churn of a customer base using open sample Telecomm customer data.
https://github.com/rubynixx/bpp_telecomm_churn

Last synced: 16 days ago
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Programming assignment on predicting churn of a customer base using open sample Telecomm customer data.

• Host: GitHub
• URL: https://github.com/rubynixx/bpp_telecomm_churn
• Owner: RubyNixx
• Created: 2024-09-09T11:25:14.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-12-27T10:36:05.000Z (over 1 year ago)
• Last Synced: 2025-01-10T17:43:53.112Z (over 1 year ago)
• Language: Jupyter Notebook
• Size: 3.03 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
Topic: Programming for Data Analysts

Assignment: 1 - Produce a python notebook to answer the business problem.

The python file can be found within this repository or alternatively opened in google colab below:

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)]([https://colab.research.google.com/github/USERNAME/REPO/blob/BRANCH/PATH/TO/NOTEBOOK.ipynb](https://colab.research.google.com/drive/1VVJBRud9zGyRA-w-PMuqUZuURGZLU-9I#scrollTo=z6qkxuUH8NmF))

Project: Analysing historic customer churn at BPP Telecom and prediciting future churn.

Background:

BPP Telecom, a leading telecommunications provider headquartered in the UK, has been on a progressive journey, expanding its offerings from traditional phone services to a broad spectrum encompassing high-speed internet and cutting-edge streaming services.

Business Problem:

Despite the diversification and growth of its services, BPP has been encountering a rising tide of customer churn. This escalating issue has begun to erode its customer base and revenues, posing a clear constraint to the company's future growth trajectory.

Data used:

The dataset this model is based on is sourced from Customer information. This dataset captures an array of attributes for each customer, ranging from demographics to service usage, churn and charges. The 2 data files needed are available as files within this repo.

Aim of this notebook:

Extract key insights from the customer data to construct a predictive model anticipating customer churn.