https://github.com/shibasishb2/ensemble-techniques

This project is based on the case study of a telecommunication company, which is facing a customer churn issue. The project aims at understanding the pattern of the data and predicting customers who are going to churn based on multiple variables to help the company in retaining their existing customers.
https://github.com/shibasishb2/ensemble-techniques

adaboost decision-trees eda logistic-regression ml-workflow python random-forest

Last synced: 29 days ago
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This project is based on the case study of a telecommunication company, which is facing a customer churn issue. The project aims at understanding the pattern of the data and predicting customers who are going to churn based on multiple variables to help the company in retaining their existing customers.

• Host: GitHub
• URL: https://github.com/shibasishb2/ensemble-techniques
• Owner: shibasishb2
• Created: 2024-03-10T14:53:57.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2024-07-29T13:13:37.000Z (5 months ago)
• Last Synced: 2024-07-29T16:45:36.450Z (5 months ago)
• Topics: adaboost, decision-trees, eda, logistic-regression, ml-workflow, python, random-forest
• Language: Jupyter Notebook
• Homepage:
• Size: 1.17 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Ensemble-techniques

This project is based on the case study of a telecommunication company, which is facing a customer churn issue. The project aims at understanding the pattern of the data and predicting customers who are going to churn based on multiple variables to help the company in retaining their existing customers. The project was accomplished by building a machine learning workflow that will run autonomously with the CSV file and return the best-performing model.

# Skills & Tools Covered

- EDA

- Logistic regression

- Decision Trees

- Random forest

- XGboost

- Adaboost

- python

- ML workflow