01 — Problem
What was the objective?
The goal was to identify customers at risk of churning from customer, service, contract, and billing information.
ProjectMachine Learning
Customer Churn Prediction
A machine learning application that estimates customer churn likelihood from customer, service, contract, and billing information.
Problem & Solution
From a defined problem to a practical solution.
02 — Solution
What was built?
Built a machine learning application that preprocesses the data, trains and evaluates classification models, and delivers churn predictions through Streamlit.
Approach / Workflow
How the project moves from input to outcome.
01
Data Preprocessing
02
Feature Engineering
03
Model Training
04
Evaluation
05
Prediction
06
Streamlit Deployment
Tools & Technologies
Technologies used to build the project.
PythonPandasNumPyScikit-learnJoblibStreamlit
Key Work
What went into building the project.
01
Data preprocessing
02
Exploratory analysis
03
Feature engineering
04
Classification modeling
05
Model evaluation
06
Model serialization
07
Prediction workflow
08
Streamlit deployment
Project Preview
A closer look at the application.
Project Links
Explore the source code and live application.
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