- Project Predict Customer Churn of ML DevOps Engineer Nanodegree Udacity
The purpose of this project is to split the churn_notebook.ipynb into production ready python code. The code have to be clean and efficient and has to meeting pep 8 guidelines.
The project consist of following files and directories:
churn_notebook.ipynb: The notebook that has to be converted inot production ready code. churn_library.py: Library of functions to find customers who are likely to churn.
| Classes | Description |
|---|---|
| Encoder | Utility class that implement encoder functions for a panda dataframe |
| ExploratoryDataAnalysis | Class that implement exploratory data analysis functions (eda) |
| ClassificationReport | Class to print and save a classification report for a model |
| TreeFeatureImportances | Class that implement feature importances functions for tree models |
| RocCurvePlot | Class that compare two models by plotting the roc curve plot |
| RandomForestGridSearchClassifier | Class that implement GridSearch for RandomForest classifier |
| MLPipeline | The MLPipeline class for the churn library |
| __main__ | Execute the MLPipeline for the setup in churn_notebook.ipynb |
| churn_script_logging_and_tests.py: Unit tests for the churn_library.py functions. | |
| Function | Description |
| ------------- | ------------- |
| __main__ | Run all tests using unittest framework and log the resuts. |
| Guide.ipynb: Guide for this exercise | |
| pytest.ini: Setup file for unittest framework | |
| Readme.md: The readme file for this project | |
| requirements_py3.6.txt: The Python requirements file for this project if running v3.6 | |
| requirements_py3.8.txt: The Python requirements file for this project if running v3.8 |
data: Folder containing the input data bank_data.csv for the project images: Output folder for the eda analysis and prediction results logs: Log folder for the churn_library.log models: The model folder for the generted churn prediction models
To run the MLPipeline execute folowing in the console:
python churn_library.py
The following parameters can be changed in the file for the pipeline:
- TARGET: The target coloumn
- CAT_COLUMNS: Categorical columns of interest
- QUANT_COLUMNS: Quantitative columns of interest
By running the pipeline the log will be shown in the console and following files will be created as result:
Folder images/eda: histogram_Churn.png: Churn as univariate, quantitative plot histogram_Customer_Age.png: Customer Age as univariate, quantitative plot bar_plot_Marital_Status.png: Marital Status as univariate, categorical plot distribution_Total_Trans_Ct.png: Total Trans Ct as distributions plot heatmap.png: Pairwise correlation of columns as a bivariate plot
Folder images/results: classification_report_logistic_regression.png: Classification report for logistic regression model classification_report_random_forest.png: Classification report for best random forest model roc_curve.png: The ROC curves of logistic regression - and random forest model feature_importances.png: The feature importances of best random forest model shap_values.png: Shape values of best random forest model
Folder models: logistic_model.pkl: The logistic regression model random forest_model.pkl: The best random forest model
To run the unit tests execute folowing in the console:
python churn_script_logging_and_tests.py
or
pytest
By running the tests all files from MLPipeline will be created as well as a log file in the folder churn_library.log: The results from the unit tests and the logs from the MLPipeline
To install the requirements for the project run:
Workspace run
python -m pip install -r requirements_py3.6.txt
Local run
python -m pip install -r requirements_py3.8.txt
Run following to check the lint rules for the python files:
pylint churn_library.py
pylint churn_script_logging_and_tests.py
and to check how thwy conform to the PEP 8 style guide run (following will fix the files):
autopep8 --in-place --aggressive --aggressive churn_library.py
autopep8 --in-place --aggressive --aggressive churn_script_logging_and_tests.py