Customer:

ConnectTel is a leading telecommunications company at the forefront of innovation and connectivity solutions. With a strong presence in the global market, ConnectTel has established itself as a trusted provider of reliable voice, data, and Internet services. Offering a comprehensive range of telecommunications solutions, including mobile networks, broadband connections, and enterprise solutions, ConnectTel caters to both individual and corporate customers, they are committed to providing exceptional customer service and cutting-edge technology.

Problem:

ConnectTel Telecom Company faces the pressing need to address customer churn, which poses a significant threat to its business sustainability and growth. The company’s current customer retention strategies lack precision and effectiveness, resulting in the loss of valuable customers to competitors.

To overcome this challenge, ConnectTel aims to develop a robust customer churn prediction system for which I was contacted to handle as a Data Scientist.

By leveraging advanced analytics and machine learning techniques on available customer data, the company seeks to accurately forecast customer churn and implement targeted retention
initiatives. This proactive approach will enable ConnectTel to reduce customer attrition, enhance customer loyalty, and maintain a competitive edge in the highly dynamic and competitive telecommunications industry.

Solution:

Data Collection and Preparation:
I load the data. Clean the data, handled missing values, and prepare it for analysis.

Feature Engineering:
I identified and create meaningful features from the collected data that could potentially predict churn. These features include customer demographics, usage frequency, customer service interactions, payment history, etc.

Exploratory Data Analysis (EDA):
I analyze and visualize the data to understand patterns, correlations, and relationships between different features and churn.

Model Building:
I selected appropriate machine learning algorithms; Logistic Regression, Decision Trees, Random Forests, for churn prediction.
I split the data into training and testing sets and trained the models on the training data.

Model Evaluation:
I evaluated the trained models using appropriate metrics; accuracy, precision, recall, F1-score, using the test data.
I tuned hyperparameters and adjust models to improve performance.

Predictive Modeling:
I used the trained model to predict churn for new or existing customers.
I set thresholds for churn probability to identify customers at risk.

Deployment and Monitoring:
I deployed the model into production to make predictions on incoming customer data.

Below is the python notebook that shows the EDA and machine learning models: