https://github.com/urbanclimatefr/telecom-customer-churn-prediction

Supervised learning algorithm was used to build churn prediction model to help solve a telecoms company's customer churn problem.
https://github.com/urbanclimatefr/telecom-customer-churn-prediction

churn-prediction decision-trees genetic-algorithm jupyter-notebook python supervised-learning telecom-churn-prediction

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Supervised learning algorithm was used to build churn prediction model to help solve a telecoms company's customer churn problem.

• Host: GitHub
• URL: https://github.com/urbanclimatefr/telecom-customer-churn-prediction
• Owner: urbanclimatefr
• Created: 2022-05-22T07:10:42.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2022-05-22T07:25:00.000Z (over 2 years ago)
• Last Synced: 2024-11-05T14:27:29.756Z (12 days ago)
• Topics: churn-prediction, decision-trees, genetic-algorithm, jupyter-notebook, python, supervised-learning, telecom-churn-prediction
• Language: Jupyter Notebook
• Homepage:
• Size: 1.78 MB
• Stars: 3
• Watchers: 1
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Telecom Customer Churn Prediction

- The source files consist of 2 parts. 

- The first part is 2022_02_20_churn_summative_part_1.ipynb

- The input file required for part I is cell2celltrain_Small_6k.csv

- The second part is 2022_02_26_churn_summative_part_2.ipynb

- The input file required for part II are:

- 1) df_imputed.csv

- 2) features_selected_new.txt

- The formal written report is report.pdf.

- The requirement of this project is in Assessment Brief.pdf.

- Supervised learning algorithm was used to build churn prediction model to help solve a telecoms company's customer churn problem.

- Decision tree classifiers and optimisation techniques were used for feature selection. 

- The genetic algorithm was applied to a telecoms customer dataset consisting of 6380 rows and 57 features. 

- The Python programming language, Jupyter notebook and scikit-learn python package were used.