How to write a telecom customer churn prediction model with Python

This article mainly explains "how to use Python to write a telecom customer churn prediction model". The content of the article is simple and clear, and it is easy to learn and understand. Please follow the editor's train of thought to study and learn "how to write a telecom customer churn prediction model with Python".

01. Business understanding

Loss of customers refers to those who have used products or services, due to loss of interest in products and other reasons, no longer use products or services.

Telecom service companies, Internet service providers and insurance companies often use customer churn analysis and customer churn rate as one of their key business indicators, because the cost of retaining an old customer is much lower than that of acquiring a new one.

Forecasting analysis uses the customer churn prediction model to predict customer churn by evaluating the risk tendency of customer churn. Because these models generate a ranking list of churn probability, for potential high-probability customers, they can effectively implement customer retention marketing plans.

Below we will teach you how to write a telecom subscriber churn prediction model with Python. Here are the specific steps and key codes.

02. Data understanding

The analysis data comes from IBM Sample Data Sets, which calculates consumption data from a telecom company over a period of time. There are 7043 customer profiles, each of which contains 21 fields, including 1 customer ID field, 19 input fields and 1 target field-Churn (Yes represents loss, No represents non-loss). The input field mainly contains the following three dimensional indicators: user profile index, consumer product index, consumption information index. The details of the fields are as follows:

03. Data reading and overview

First import the required package.

Df = pd.read_csv ('. / Telco-Customer-Churn.csv') df.head ()

Read into the data set

Df = pd.read_csv ('. / Telco-Customer-Churn.csv') df.head ()

04. Preliminary data cleaning

First of all, carry on the preliminary data cleaning work, including error value and exception value processing, and divide the category type and numeric field type, in which the cleaning part includes:

OnlineSecurity, OnlineBackup, DeviceProtection, TechSupport, StreamingTV, StreamingMovies: error value handling

TotalCharges: exception value handling

Tenure: custom sub-box

Define category and numeric fields

# error value handling repl_columns = ['OnlineSecurity',' OnlineBackup', 'DeviceProtection',' TechSupport','StreamingTV', 'StreamingMovies'] for i in repl_columns: df [I] = DF [I] .replace ({' No internet service': 'No'}) # replace value SeniorCitizen df ["SeniorCitizen"] = df ["SeniorCitizen"] .replace ({1: "Yes" 0: "No"}) # replacement value TotalCharges df ['TotalCharges'] = df [' TotalCharges'] .replace (', np.nan) # TotalCharges null value: small amount of data Directly delete df = df.dropna (subset= ['TotalCharges']) df.reset_index (drop=True, inplace=True) # reset index # convert data type df [' TotalCharges'] = df ['TotalCharges'] .astype (' float') # convert tenure def transform_tenure (x): if x