Telco Dataset Customer Churn Classification Project

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Project Goals:

-Acquire Data: from a SQL database, create an acquire.py module with necessary functions
-Clean data and create a prepare.py module with necessary functions
    -ie: look at null-values and either drop or impute values or rows where appropriate.
    -assign scalar values to columns containing strings to allow modeling to work

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Business Goals

• Construct a ML classification model that accurately predicts what features are drivers in Telco Customer Churn.
• Document process well enough to be presented or understood while reading.

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Initial Hypotheses:

- Payment_type is a driver and correlated with churn

- Having multiple lines is a driver and correlated with churn

- Internet_type is a driver and correlated with churn

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Audience

• Codeup Data Science students

Project Deliverables

• A final report notebook
• A final report notebook presentation
• All necessary modules to make my project reproducible

Project Context

• The Telco dataset I'm using came from the Codeup SQL database.

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Data Dictionary:

Summary: 7043 observations(rows), Each observation represents a single customer with 21 variables

CustomerID: A unique ID that identifies each customer.

Gender: The customer’s gender: Male, Female

Senior Citizen: Indicates if the customer is 65 or older: Yes, No

Partner: Indicate if the customer has a partner: Yes, No

Dependents: Indicates if the customer lives with any dependents: Yes, No. Dependents could be children, parents, grandparents, etc.

Tenure Months: Indicates the total amount of months that the customer has been with the company by the end of the quarter specified above.

Phone Service: Indicates if the customer subscribes to home phone service with the company: Yes, No

Multiple Lines: Indicates if the customer subscribes to multiple telephone lines with the company: Yes, No

Internet Service: Indicates if the customer subscribes to Internet service with the company: No, DSL, Fiber Optic, Cable.

Online Security: Indicates if the customer subscribes to an additional online security service provided by the company: Yes, No

Online Backup: Indicates if the customer subscribes to an additional online backup service provided by the company: Yes, No

Device Protection: Indicates if the customer subscribes to an additional device protection plan for their Internet equipment provided by the company: Yes, No

Tech Support: Indicates if the customer subscribes to an additional technical support plan from the company with reduced wait times: Yes, No

Streaming TV: Indicates if the customer uses their Internet service to stream television programing from a third party provider: Yes, No. The company does not charge an additional fee for this service.

Streaming Movies: Indicates if the customer uses their Internet service to stream movies from a third party provider: Yes, No. The company does not charge an additional fee for this service.

Contract: Indicates the customer’s current contract type: Month-to-Month, One Year, Two Year.

Paperless Billing: Indicates if the customer has chosen paperless billing: Yes, No

Payment Method: Indicates how the customer pays their bill: Bank Withdrawal, Credit Card, Mailed Check

Monthly Charge: Indicates the customer’s current total monthly charge for all their services from the company.

Total Charges: Indicates the customer’s total charges, calculated to the end of the quarter specified above.

Churn Label: Yes = the customer left the company this quarter. No = the customer remained with the company. Directly related to Churn Value.

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Executive Summary - Conclusions & Next Steps

• I found that all of the classification models I created performed better than my baseline prediction of 73%.
• I chose the Random Forest model as my best model with a 89% accuracy rate for predicting my target value, Churn_Yes. This model outperformed my baseline score of 73% accuracy, so it has value.
• Some initial exploration and statistical testing revealed that further exploration of tenure, monthly charges and total charges might help my models predict with even more accuracy, and with more time, I would like to further explore those features.

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Plan

• (Using Faith Kane's streamlined process in her Iris Classification project, I will proceed with the following plan- minor changes to adjust for the Telco Dataset)
• Create README.md with data dictionary, project and business goals, and initial hypotheses.
• Acquire data from the Codeup SQL Database and create a an acquire.py module.
• Clean and prepare data for the first iteration through the pipeline, MVP preparation. Create a prepare.py module, and prepare data in Final Report Notebook by importing and using the funtion.
• Clearly define hypotheses, set an alpha, run the statistical tests needed, reject or fail to reject the Null Hypothesis, and document findings and takeaways.
• Establish a baseline accuracy and document well.
• Train three different classification models.
• Evaluate models.
• Choose the model with that performs the best and evaluate that single model.
• Create csv file with the measurement id, the probability of the target values, and the model's prediction for each observation in my test dataset.
• Document conclusions, takeaways, and next steps in the Final Report Notebook.

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Reproduce this project:

• You will need your own env file with database credentials along with all the necessary files listed below to run my final project notebook.
• Read this README.md
• Download the aquire.py, prepare.py, and final_report.ipynb files into your working directory
• Add your own env file to your directory. (user, password, host)
• Run the final_report.ipynb notebook