The notebooks in this repository document a step-by-step application of the framework to a real-world use case and dataset - predicting customer churn. This is a critical need for subscription-based businesses and an ideal application of machine learning.
- Prediction engineering
- State business need
- Translate business requirement into machine learning task by specifying problem parameters
- Develop set of labels along with cutoff times for supervised machine learning
- Feature Engineering
- Create features - predictor variables - out of raw data
- Use cutoff times to make valid features for each label
- Apply automated feature engineering to automatically make hundreds of relevant, valid features
- Modeling
- Train a machine learning model to predict labels from features
- Use a pre-built solution with common libraries
- Optimize model in line with business objectives
The final results of our model are shown below:
| ROC AUC | Sensitivity | Specificity | F1 Score |
|---|---|---|---|
| 0.83 | 82% | 89%% | 0.89 |
Kaggle Link - https://www.kaggle.com/harmanbhutani/eda-bankprediction?scriptVersionId=23584175
My Presentation Link - https://docs.google.com/presentation/d/1L-ubLUQKIO7bp4FpdiqM0bPxty6WokvO-p691loXjC4/edit?usp=sharing