⚒ Prospecting ⚒

This project started as an effort to predict a 'prospect score' for each business in a list of current and (predominantly) potential customers.

While the initial goal was to provide a list to help prioritize sales opportunities (ex. rank order prospects by state), I also had some ideas about tying in Google Sheets to help with my typical ML workflow (data profiling > clean/transform > performance reporting > delivery of final predictions > revisiting column treatments >> etc). OAuth 2.0 is used for Google API authentication when using the SheetsApi and DriveApi classes, and the usual Sheets sharing options exist if you want to invite collaborators.

I'll be updating this README and documentation in general...In the interim - as an example of how Google Sheets is used, the following table outlines the spreadsheets and tabs which I have found to be useful. While I cannot share my original prospecting dataset, I used an old Innocentive challenge dataset as an example.

spreadsheet	sheet	note
projectname_metadata	metadata	Control logic for column processing treatments; used by Python to inform how each column is processed. The functions in process.py rely on information from this tab.
	raw_descr	Descriptive information about raw data (df_raw)
	clean_descr	Descriptive information about cleaned dataset (df_clean)
---	---	---
projectname_model_reporting	session_report	Summarizes model performance, plan to make this the main performance tab. A "session" represents an instance of a "ModelSession" class instance which is used to share access to train/test sets.
	cv_results	If GridSearchCV is used, the GridSearchCV.cv_results_ reports are saved here (shows performance by fold for each parameter set evaluated)
	model_types	A simple lookup table, used by Python script as a reference when building the report for the session_report tab
	_plots
---	---	---
projectname_predictions	predictions	Final predictions, with probabilities
	lookupmaster	A lookup table with master list of prospects / entities of interest, or misc information to join with predictions
	README	Intend to use as an FYI tab, to provide overview of health of predictions made (ex. highlight number of correct/incorrect predictions, etc)

Overview

• The project directory contains:

        .
        ├── .dockerignore
        ├── .gitattributes              # For CRLF correction
        ├── .gitignore
        ├── credentials/                # Not necessarily best practice, but convenient
        │   ├── README.md
        │   └── certs/
        │       └── README.md
        ├── data/
        │   ├── README.md
        │   └── tmp/                    # Logs saved here
        │       ├── README.md
        │       └── joblib/             # Used by scikit learn when running in Docker container
        │           └── README.md
        ├── Dockerfile                  # See README_detail.md for more info
        ├── LICENSE.md
        ├── jupyter_notebook_config.py  # See README_detail.md for more info
        ├── mplimporthook.py            # Used by Dockerfile
        ├── notebooks/                  # Jupyter Notebooks
        ├── prospecting
        │   ├── __init__.py
        │   ├── env.py                  # Check here for environment variables required
        │   ├── utils.py
        │   ├── api.py                  # Google Sheets and Google Drive API classes
        │   ├── process.py              # Data cleaning functions, relies on info in metadata tab
        │   ├── model.py
        │   ├── report.py
        │   ├── errors.py
        │   └── version.py
        ├── README.md
        ├── requirements_nonconda.txt   # Used by Dockerfile
        ├── scripts
        │   └── hash_jupyter_pw.py      # Create hashed password to use with Docker container
        ├── start-notebook.sh           # Used by Dockerfile
        ├── start.sh                    # Used by Dockerfile
        └── start-singleuser.sh         # Used by Dockerfile

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