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Telecom Customer Churn Analysis and Prediction

Project Overview

This is an analysis about customer attention for a Telecommunication Company, based on the details about its customers' interactions with the services. The dataset is available on Kaggle, and the analysis is done on Google Colab.

Main methods:

  • Data Wrangling with pandas
  • EDA with seaborn
  • Feature Selection with SelectFromModel and SelectFromKBest
  • Sample Balancing with RandomOverSampler
  • Classification with Machine Learning algorithms, Ensemble Methods, and Multilayer Perception Neural Network

Dataset:

As mention above, the dataset is available on Kaggle. The dataset contains 7043 customers' records, with 21 recorded features.

Variable Description Type
customerID Customer ID object
gender Whether the customer is a male or a female object
SeniorCitizen Whether the customer is a senior citizen (1) or not (0) int64
Partner Whether the customer has a partner or not (Yes, No) object
Dependents Whether the customer has dependents or not (Yes, No) object
tenure Number of months the customer has stayed with the company int64
PhoneService Whether the customer has a phone service or not (Yes, No) object
MultipleLines Whether the customer has multiple lines or not (Yes, No, No phone service) object
InternetService Customer’s internet service provider (DSL, Fiber optic, No) object
OnlineSecurity Whether the customer has online security or not (Yes, No, No internet service) object
OnlineBackup Whether the customer has online backup or not (Yes, No, No internet service) object
DeviceProtection Whether the customer has device protection or not (Yes, No, No internet service) object
TechSupport Whether the customer has tech support or not (Yes, No, No internet service) object
StreamingTV Whether the customer has streaming TV or not (Yes, No, No internet service) object
StreamingMovies Whether the customer has streaming movies or not (Yes, No, No internet service) object
Contract The contract term of the customer (Month-to-month, One year, Two year) object
PaperlessBilling Whether the customer has paperless billing or not (Yes, No) object
PaymentMethod The customer’s payment method (Electronic check, Mailed check, Bank transfer (automatic), Credit card (automatic)) object
MonthlyCharges The amount charged to the customer monthly float64
TotalCharges The total amount charged to the customer object
Churn Whether the customer churned or not (Yes or No) object

Project Directory:

| - customer_churn                                        
|   -- dataset                                              Contains the raw dataset 
|     --- WA_Fn-UseC_-Telco-Customer-Churn.csv
|   -- src                                                  Contains the source codes
|     --- Telco_Customer_Churn.ipynb
|   -- README.md                                            Project Overview
|   -- LICENSE                                              MIT License

Future Considerations:

This analysis can be better visualized with additional tools like Tableau to make an easy-to-digest dashboad.