1. Final thoughts
Congratulations on completing the course! You have arrived at your destination, a hyper-parameter tuned model, that predicts customer churn for a cell phone company.
2. Chapter 1
The first stop along your learning journey, you defined customer churn and leveraged exploratory data analysis. Here you used descriptive statistics and data visualizations to make sure certain model assumptions were met.
3. Chapter 2
The second stop you learned to drop unnecessary columns, convert binary text columns to 0's and 1's, one hot encoding, and scaling of your data.
4. Chapter 3
The third stop you learned several important topics such as: making predictions, splitting the data into training and test sets, and model metrics like confusion matrix, roc curves, and area under the curve.
5. Chapter 4
The last stop along your learning journey, you tuned your churn model's hyperparameters with both grid and random search,
6. Chapter 4: Part 2
identified the important features, and added additional features to improve the model's performance.
7. Churn Workflow
Now you are able to tackle a real world churn problem and apply the knowledge you gained in this course.You may also want to enrich what you've learned through further research and study.
8. Recommended next steps
On this slide there are a couple of courses from DataCamp that I recommend.
9. Additional resources
And on this slide there are some additional resources that I also recommend.
10. Great Work!
Because of your determination you've successfully arrived at your destination. You can now apply this workflow to solve customer churn in your industry. Happy mining, and thank you for your time!