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: 2 months 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 (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-07-29T13:13:37.000Z (almost 2 years ago)
- Last Synced: 2025-05-30T22:46:37.701Z (about 1 year 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
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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