Skip to content

Latest commit

 

History

History
17 lines (14 loc) · 893 Bytes

README.md

File metadata and controls

17 lines (14 loc) · 893 Bytes
Raw

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
    1. 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.