**NOTE - Please see the Jupyter notebook(.ipynb file) for complete explanation and in-depth Exploratory Data Analysis **
Project goals -
- Data cleansing and preprocessing.
- Data visualization and Exploratory Data Analysis
- Statistical analysis of the data.
- Model generation for prediction of customer churn behavior.
- Application of Logistic Regression, SVM-Linear, SVM-RBF and Random Forest algorithms on data and performance comparison.
Project Description -
Data source - Kaggle & IBM sample dataset community. Dataset - Prediction of user behavior to retain customers. The dependent variable have binary value, 1 - churned and 0 - not or true/false. 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.
To execute - python churn_rate.py
*Data was in a Csv file format. For other formats use other read function of pandas. *Update the file path to local directory before running the file.
**************************************************** END OF FILE ******************************************************