Churn Prediction
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Strategy
Python, Sklearn, Lighgbm, XGBoost, Pandas, Seaborn
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Design
Data Science, Machine Learning, Classification, Churn Analysis
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Tags
Business Problem
It is expected to develop a machine learning model that can predict customers who will leave the company.
Dataset Info
21 Feature, 7043 Sample
Feature | Definition |
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customerID | Customer ID |
gender | Whether the customer is a male or a female |
SeniorCitizen | Whether the customer is a senior citizen or not (1, 0) |
Partner | Whether the customer has a partner or not (Yes, No) |
Dependents | Whether the customer has dependents or not (Yes, No) |
tenure | Number of months the customer has stayed with the company |
PhoneService | Whether the customer has a phone service or not (Yes, No) |
MultipleLines | Whether the customer has multiple lines or not (Yes, No, No phone service) |
InternetService | Customer’s internet service provider (DSL, Fiber optic, No) |
OnlineSecurity | Whether the customer has online security or not (Yes, No, No internet service) |
OnlineBackup | Whether the customer has online backup or not (Yes, No, No internet service) |
DeviceProtection | Whether the customer has device protection or not (Yes, No, No internet service) |
TechSupport | Whether the customer has tech support or not (Yes, No, No internet service) |
StreamingTV | Whether the customer has streaming TV or not (Yes, No, No internet service) |
StreamingMovies | Whether the customer has streaming movies or not (Yes, No, No internet service) |
Contract | The contract term of the customer (Month-to-month, One year, Two year) |
PaperlessBilling | Whether the customer has paperless billing or not (Yes, No) |
PaymentMethod | The customer’s payment method (Electronic check, Mailed check, Bank transfer (automatic), Credit card (automatic)) |
MonthlyCharges | The amount charged to the customer monthly |
TotalCharges | The total amount charged to the customer |
Churn | Whether the customer churned or not (Yes or No) |
Requirements
catboost==1.0.6
lightgbm==3.1.1
matplotlib==3.5.2
numpy==1.21.5
pandas==1.4.3
scikit_learn==1.1.2
seaborn==0.11.2
xgboost==1.5.0
Files
telco.ipynb - Telco Customer Churn Prediction Notebook