An insurance company has provided a historical data set (train.csv). The company has provided with a list of potential customers to whom to market (test.csv). From this list of potential customers, the ML model determines whom to market to and whom not to and saves the same to an Excel file (test_with.predictions excel file).
train, test
A. Documentation_Capstone_Propensify.pdf : It explains the reasoning behind various steps involved in the model training.
Contents present in Documentation_Capstone_Propensify:
- Introduction
- Treating Missing Values
- Feature Engineering
- One hot encoding categoric features and normalizing continuous features
- Choice of sampling
- Choice of metrics and model
- Model
- Utility of the Model
B. SourceCode_Pipeline_Capstone : It contains the pipeline of operations. It generates the required file, i.e., test data with predictions on whom to market and whom not to.
C. test_with.predictions : Excel file with predictions
D. Documentation_Capstone_Propensify.ipynb : Contains the ipynb file of Documentation_Capstone_Propensify.pdf document.
E. propensify_model.joblib : Contains joblib file of the trained model created and used in SourceCode_Pipeline_Capstone
F. preprocessing_pipeline.joblib : Contains joblib file of preprocessing pipeline created and used in SourceCode_Pipeline_Capstone
NOTE: Please change the path to files to run the source code. The current path is set to the path at which files are present in my system. Please run the SourceCode_Pipeline_Capstone ipynb file to get the predictions.