https://github.com/code-str8/customer-chun-prediction
This data science project is designed to categorize potential customers of a telecommunications company, determining whether they are likely to discontinue their services (churn) or remain with the company.
https://github.com/code-str8/customer-chun-prediction
machine-learning pipelines visualization
Last synced: 8 months ago
JSON representation
This data science project is designed to categorize potential customers of a telecommunications company, determining whether they are likely to discontinue their services (churn) or remain with the company.
- Host: GitHub
- URL: https://github.com/code-str8/customer-chun-prediction
- Owner: Code-str8
- License: mit
- Created: 2023-11-28T12:05:21.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-02-19T18:08:14.000Z (almost 2 years ago)
- Last Synced: 2025-02-06T10:29:32.937Z (about 1 year ago)
- Topics: machine-learning, pipelines, visualization
- Language: Jupyter Notebook
- Homepage:
- Size: 2.51 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Customer-Chun-Prediction
Customer Churn Prediction
Objective:
The primary objective of the Telco Customer Churn Prediction project is to develop a machine learning model that can predict whether a customer is likely to churn or not based on various attributes and behaviors. By identifying key drivers of churn, the project aims to provide actionable insights to reduce customer attrition and enhance customer retention strategies.
Business Context:
The telecommunications industry is currently facing significant challenges, including high customer churn rates and intense competition. In order to address these issues, it is crucial for the business to understand the factors influencing customer churn. This project aligns with the business goal of improving customer retention by leveraging predictive analytics.
Data:
The dataset comprises various attributes related to customer demographics, usage patterns, and service-related information. Key features include gender, senior citizen status, partner and dependent status, tenure, type of services used (e.g., internet, streaming), contract details, billing preferences, payment methods, and financial details such as monthly and total charges.
Methodology:
The project will involve exploratory data analysis (EDA) to gain insights into the dataset, identify patterns, and understand feature importance. Subsequently, a machine learning model will be developed using classification algorithms to predict customer churn. The model will be trained on historical data, and its performance will be evaluated using relevant metrics.
Expected Outcomes:
Identification of key features influencing customer churn.
Development of a predictive model capable of classifying customers into churn and non-churn categories.
Provision of actionable insights to improve customer retention strategies.
Enhanced understanding of customer behavior and preferences.
Stakeholders:
Stakeholders involved in this project include telecommunications executives, marketing teams, and customer relationship management teams. The insights gained from the predictive model can inform targeted marketing efforts and customer engagement initiatives to reduce churn and foster long-term customer loyalty.
Article
An article was publised on this project on medium. Kinldy access it https://medium.com/@ndunda.alex/telco-customer-churn-prediction-1fcb08582e60 .
Thank you.