This project is a showcase of skills obtained in creating a supervised learning capstone project to predict if a telecom customer would churn or not. As mentioned, we have a target variable Churn available in the dataset and using exploratory data analysis and supervised learning model comparisons with hyperparameter tuning, we pick and choose the best performing models for our dataset. This was done in a Juypter notebook in Python on Google colab and then downloaded here.
-
Notifications
You must be signed in to change notification settings - Fork 0
lvang77/ML
Folders and files
Name | Name | Last commit message | Last commit date | |
---|---|---|---|---|
Repository files navigation
About
No description, website, or topics provided.
Resources
Stars
Watchers
Forks
Releases
No releases published
Packages 0
No packages published