This competition is about predicting whether a customer will change telecommunications provider, something known as "churning".here to find a project report

Customer churn is one of the most important fields in the business analysis and the critical goals of businesses. It is easier and less expensive to focus on retaining existing customers than acquire new ones. Increasing customer retaintion rates by a few % could increase profits significantly. Predicting customer churn will help a business to retain their exisiting customers, and also expand identfying potential customers. The heart of churn management lies in being able to identify the early warning signals from potential attritors. Machine learning will provide a one of solutions and businesses will make proactive movements to stop it from happening.

1. The objecttive of this project
2. Data details
3. Domain
4. Exploring data
5. Cleaning data and Feature engneering
6. Modeling and evaluation
7. The competition and final scores
8. Challenges and augmentations

• project_report.md

• data Raw dataset and cleaned dataset.
• images Images used in the report
• model Stored a turned XGB model.
• notebooks Jupyternotebooks for data visualisation, cleaning data, EDA, modeling and turning.
• utils
  • utils_cleaning_data.py Utils used for cleaning_data.py
  • utils_modeling.py Utils used for modeling.py

1. cleaning_data.py
2. data_analysis.py
3. modeling.py
4. parameter_turning.py
5. loaded_model.py

• project_report.md Project findings
• requirements.txt Software Requirements