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Customer Churn Prediction | Interactive ML App with Streamlit

python pandas scikit-learn streamlit joblib eda jupyter classification data science

Customer Churn ML App Screenshot

In this blog post, I’ll walk you through how I built a predictive machine learning app that determines whether a telecom customer is likely to churn. This project covers data preprocessing, model tuning, deployment with Streamlit, and creating an end-to-end experience ready to showcase to hiring managers.

🧠 What It Does

This app allows users to:

  • 📥 Input customer info (Age, Gender, Tenure, Monthly Charges)
  • 🧠 Run a K-Nearest Neighbors classification model in real-time
  • 📊 View the churn probability and prediction (Likely/Unlikely)
  • 🖥️ Interact with a professional web-based UI powered by Streamlit

It’s fully deployed and ready to demo live.

🚀 Live Demo

🖥️ Try the App → Launch Here

🛠️ Tech Stack

Tool / LibraryPurpose
Pandas, NumPyData preprocessing and manipulation
Matplotlib, SeabornExploratory data analysis (EDA)
scikit-learnModel building, scaling, and evaluation
joblibSaving and loading models efficiently
StreamlitFront-end deployment and interaction
Jupyter NotebookDevelopment and experimentation

🔍 Why I Built It

This project was part of my personal journey to master practical machine learning for business analytics. Customer churn is a critical problem in many industries, and I wanted to explore:

  • How to detect churn signals from behavioral data
  • How to build models that are understandable and useful
  • How to deploy ML projects in a professional way using Streamlit

💡 How It Works

  1. Performed EDA on a telecom customer dataset
  2. Handled missing values, duplicates, and categorical encoding
  3. Selected key features: Age, Gender, Tenure, and Monthly Charges
  4. Standardized input data using StandardScaler
  5. Trained a KNN classifier with GridSearchCV to find best k
  6. Saved the model and scaler as .pkl files
  7. Built a Streamlit UI for input and real-time prediction
  8. Deployed the app to Streamlit Cloud

📄 Project Repository

👉 View the Full GitHub Repo

This includes:

  • 📁 All code, data, and Streamlit app files
  • ✅ Pretrained .pkl model and scaler
  • 💬 Well-documented notebook with training process
  • 🔗 Deployment-ready requirements.txt

📚 What I Learned

  • 🧪 How to build ML pipelines using scikit-learn
  • 🎯 The importance of feature selection in classification
  • 🧠 How to tune KNN with GridSearchCV
  • 🌐 How to deploy Python models with Streamlit
  • 🛠️ How to organize and publish an ML project professionally

🧠 Final Thoughts

This was a solid project for practicing model development, interpretability, and deployment — all skills critical for data analyst and data science roles.

If you’re building your ML portfolio, I highly recommend creating projects like this that:

  • 🎯 Solve real problems
  • 🧪 Use industry-standard tools
  • 🌍 Are fully interactive and deployed

“Don’t just train models. Make them usable, visual, and impactful.”