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2025-01-23 Update From: SLTechnology News&Howtos shulou NAV: SLTechnology News&Howtos > Development >
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This article mainly explains "how to write a telecom customer churn prediction model with Python". Interested friends may wish to have a look. The method introduced in this paper is simple, fast and practical. Let's let the editor take you to 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 SeniorCitizendf ["SeniorCitizen"] = df ["SeniorCitizen"] .replace ({1: "Yes" 0: "No"}) # replacement value TotalChargesdf ['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 tenuredef transform_tenure (x): if x
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