Predicting Telco Churn

Planning the project

Goals

The goal of this project is to determine drivers that indicate if customers from Telco are more likely to leave the company and to construct a Machine Learning classification model that most accurately predicts customer churn.

Deliverables will include:

• This repo containing:
  • A Jupyter Notebook detailing the process to create this model
  • Files that hold functions to acquire and prep the data
  • This Readme.md detailing project planning and exection, as well as instructions for project recreation
• Final model created to predict if a customer will churn
• CSV file with customer_id, probability of churn, and prediction of churn

Some Context

Why is customer loyalty important? What is the cost of churn over time? According to Patrick Campbell from ProfitWell,

"Even seemingly small, single-figure increases in churn rate can quickly have a major negative effect on your company’s ability to grow. What’s more, high churn rates are more likely to compound over time."

Data Dictionary

After prepping the dataframe, the variables are the following:

Feature	Definition	Data Type
contract_type_id	monthly, year, or two-year	int - (0-2)
payment_type_id	type of payment	int - (0-2)
customer_id	unique identifier	object
partner	has partner or not	int - boolean
dependents	has dependents or not	int - boolean
phone_service	one or multiple lines, or no service	int - (0-2)
multiple lines	multiple lines or not	object
internet_service_type	DSL, fiber optic, or no service	object
online_security_1	security or not	int - boolean
online_backup	backup or not	int - boolean
device_protection	protection or not	int - boolean
tech_support_1	support or not	int - boolean
streaming_tv	streaming or not	int - boolean
streaming_movies	streaming or not	int - boolean
contract_type	monthy, 1 year, 2 year	object
paperless_billing	paperless or mailed bills	int - boolean
monthly charges	in USD	float
churn	customer has left the company or not	int - boolean
tenure (months or years)	length the customer has remained	int for months, float for years
internet_service_type_id_orig	DSL, fiber optic, or no service	int - (0-2)
tech_support_orig	tech support or not	int - boolean
internet_service_type_2	DSL or not	int - boolean
internet_service_type_3	Fiber Optic or not	int - boolean
payment type	check or bank transfer	object
online_security_orig	security or not	int - boolean

Inital Questions and Hypotheses

Questions

• Are customers more likely to churn if they have fiber optic?
• If customers have both fiber and tech support, would they stay?

Hypotheses

Is there a difference between the means of monthly_charges for fiber customers who churn and those who don't? Null Hypothesis: There is no difference between monthly charges for fiber customers who churn and those who do not Alternate Hypothesis: There is a difference between monthly charges for fiber customers who churn and those who do not

Is there a difference between the means of monthly_charges for fiber customers who have tech support and those who don't? Null Hypothesis: There is no difference between the means of monthly charges for fiber customers who have tech support and those who don't Alternate Hypothesis: There is a difference between the means of monthly_charges for fiber customers who have tech support and those who don't

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Project Steps

Acquire & Prepare

acquire.py

Data is acquired from the company SQL database, with credentials required. Functions are stored in the acquire file, which allows quick access to the data. Once the acquire file is imported, it can be used each time to access the data

prepare.py

• Converted select values of "No" and "Yes" to 0 and 1
• Dropped "total_charges" as it was redundant, "gender" and "senior_citizen" because they were not significant
• Created "tenure_months" and "tenure_years" columns, both calculated from tenure
• Created dummy variables from 'internet_service_type_id', 'online_security', and 'tech_support' columns

Explore

• Finding which features have the highest correlation to churn
• Testing hypothesis with T-test
• Visualizing churn with plots

Model

After splitting and exploring the data, we progress to modeling.
With the train data set, try four different classification models, determining which data features and model parameters create better predictions.

• 2 different Logistic Regression Models
• Decision Tree
• Random Forest

Evaluate the best model on the test data set

Outcome

• The first Logistic Regression Model had the best reults, if only slightly
• That model performed even better on the test data

How to Reproduce

• Read this README.md
• Download the aquire.py, prepare.py, and project_report.ipynb into your working directory
• Run the project_report.ipynb notebook