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