Python - Data Cleaning

The data cleaning process of Telco Customer Churn Data using Python.

The Telco customer churn data contains information about a fictional telco company that provided home phone and Internet services to 7043 customers in California in Q3. It indicates which customers have left, stayed, or signed up for their service.

Data Content

Each row represents a customer, each column contains customer’s attributes described on the column Metadata.

The data set includes information about:

• Customers who left within the last month – the column is called Churn
• Services that each customer has signed up for – phone, multiple lines, internet, online security, online backup, device protection, tech support, and streaming TV and movies
• Customer account information – how long they’ve been a customer, contract, payment method, paperless billing, monthly charges, and total charges
• Demographic info about customers – gender, age range, and if they have partners and dependents

Objective

To obtain the clean data that are ready to be used in Machine Learning.

Scope

The data cleaning process consists of:

1. Converting the data type to its approriate contextual content
2. Handling Missing Values and Duplicated Rows/Columns
3. Outlier Management
4. Feature Transformation
   • Correcting inconsistent data
   • One-Hot Encoding
   • Ordinal Encoding
   • Scaling
   • Converting numerical type data to categorical data, and used Ordinal Encoding to further simplify the analysis and modelling