Predict Customer Churn with Clean Code
Project Description | Install | Usage | Data | EDA | Model | Result | Test | Code Quality | Sequence Diagram
Project Description
This is a project to implement best coding practices.
Source code: vnk8071/clean_code
projects/clean_code
├── README.md
├── churn_library.py
├── churn_script_logging_and_tests.py
├── data
│ └── bank_data.csv
├── images
│ ├── eda
│ │ ├── churn_histogram.png
│ │ ├── customer_age_histogram.png
│ │ ├── heatmap.png
│ │ ├── marital_status_counts.png
│ │ └── total_transaction_histogram.png
│ └── results
│ ├── cv_feature_importance.png
│ ├── lr_classification_report.png
│ ├── lr_rf_roc_curves.png
│ ├── lr_roc_curve.png
│ └── rf_classification_report.png
├── logs
│ └── churn_library.log
├── models
│ ├── logistic_model.pkl
│ └── rfc_model.pkl
└── requirements.txt
| # | Feature | Stack |
|---|---|---|
| 0 | Language | Python |
| 1 | Clean code principles | Autopep8, Pylint |
| 2 | Testing | Pytest |
| 3 | Logging | Logging |
Install
pip install -r requirements.txt
Usage
python churn_library.py
Data
PATH = data/bank_data.csv
Unnamed: 0 CLIENTNUM ... Total_Ct_Chng_Q4_Q1 Avg_Utilization_Ratio
0 0 768805383 ... 1.625 0.061
1 1 818770008 ... 3.714 0.105
2 2 713982108 ... 2.333 0.000
3 3 769911858 ... 2.333 0.760
4 4 709106358 ... 2.500 0.000
... ... ... ... ... ...
10122 10122 772366833 ... 0.857 0.462
10123 10123 710638233 ... 0.683 0.511
10124 10124 716506083 ... 0.818 0.000
10125 10125 717406983 ... 0.722 0.000
10126 10126 714337233 ... 0.649 0.189
[10127 rows x 22 columns]
root - INFO - (10127, 22)
Unnamed: 0 0
CLIENTNUM 0
Attrition_Flag 0
Customer_Age 0
Gender 0
Dependent_count 0
Education_Level 0
Marital_Status 0
Income_Category 0
Card_Category 0
Months_on_book 0
Total_Relationship_Count 0
Months_Inactive_12_mon 0
Contacts_Count_12_mon 0
Credit_Limit 0
Total_Revolving_Bal 0
Avg_Open_To_Buy 0
Total_Amt_Chng_Q4_Q1 0
Total_Trans_Amt 0
Total_Trans_Ct 0
Total_Ct_Chng_Q4_Q1 0
Avg_Utilization_Ratio 0
EDA
Execute the following function to run EDA.
perform_eda
Churn Histogram
Customer Age Histogram
Marital Status
Total Transaction Histogram
Heatmap
Model
Logistic Regression
from sklearn.linear_model import LogisticRegression
lrc = LogisticRegression(solver='lbfgs', max_iter=3000)
Random Forest
from sklearn.ensemble import RandomForestClassifier
rfc = RandomForestClassifier(random_state=42)
Result
Logistic Regression
test results
precision recall f1-score support
0 0.90 0.96 0.93 2543
1 0.71 0.45 0.55 496
accuracy 0.88 3039
macro avg 0.81 0.71 0.74 3039
weighted avg 0.87 0.88 0.87 3039
train results
precision recall f1-score support
0 0.91 0.96 0.94 5957
1 0.72 0.50 0.59 1131
accuracy 0.89 7088
macro avg 0.82 0.73 0.76 7088
weighted avg 0.88 0.89 0.88 7088
Random Forest
test results
precision recall f1-score support
0 0.96 0.99 0.98 2543
1 0.93 0.80 0.86 496
accuracy 0.96 3039
macro avg 0.95 0.90 0.92 3039
weighted avg 0.96 0.96 0.96 3039
train results
precision recall f1-score support
0 1.00 1.00 1.00 5957
1 1.00 1.00 1.00 1131
accuracy 1.00 7088
macro avg 1.00 1.00 1.00 7088
weighted avg 1.00 1.00 1.00 7088
ROC Curve
Feature Importance
For more details, please check the log folder.
Test
pytest --cov=src --cov-report=term-missing --cov-report=xml churn_script_logging_and_tests.py
================================= test session starts ==================================
platform darwin -- Python 3.10.12, pytest-7.4.0, pluggy-1.2.0
rootdir: /Users/macos/projects/Kelvin/ML_DevOps_Engineer/ml-production
collected 5 items
churn_script_logging_and_tests.py ..... [100%]
=================================== warnings summary ===================================
churn_script_logging_and_tests.py::test_train_models
/Users/macos/projects/Kelvin/ML_DevOps_Engineer/ml-production/churn_library.py:345: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). Consider using `matplotlib.pyplot.close()`.
plt.figure(figsize=(15, 8))
-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
======================= 5 passed, 1 warning in 169.16s (0:02:49) =======================
Code Quality
Style Guide - Format your refactored code using PEP 8 – Style Guide. Running the command below can assist with formatting. To assist with meeting pep 8 guidelines, use autopep8 via the command line commands below:
autopep8 --in-place --aggressive --aggressive churn_script_logging_and_tests.py
autopep8 --in-place --aggressive --aggressive churn_library.py
Style Checking and Error Spotting - Use Pylint for the code analysis looking for programming errors, and scope for further refactoring. You should check the pylint score using the command below.
pylint churn_library.py
pylint churn_script_logging_and_tests.py
Docstring - All functions and files should have document strings that correctly identifies the inputs, outputs, and purpose of the function. All files have a document string that identifies the purpose of the file, the author, and the date the file was created.
Sequence Diagram
