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customer-churn_calculator

This repository contains a full end-to-end data science project analyzing customer churn for a telecommunications company. Using real-world data, we built a machine learning model that predicts which customers are likely to cancel their service — a key insight that can help businesses reduce churn and protect recurring revenue.


Project Overview

Churn — when a customer leaves a service — is one of the most important metrics for subscription-based businesses. This project uses a dataset from a Telco provider to model churn risk based on customer demographics, billing data, and service usage.

Goals:

  • Understand which features are most predictive of churn
  • Build and evaluate classification models
  • Generate actionable business insights for retention strategies

Files in this Repository

File Description
data_wrangling.ipynb Data cleaning and early exploration
eda.ipynb Exploratory Data Analysis, feature distributions, statistical testing
Pre-processing Work and Model.ipynb Data prep, feature selection, model building & evaluation
Cleansed_Telco_Customer_Churn.csv Final modeling dataset (7% rows removed for quality)
README.md You're reading it :)

Key Techniques Used

  • Data Cleaning: Removed noisy or inconsistent records
  • Statistical Testing: Used t-tests to select significant features (p < 0.05)
  • Feature Engineering: Dropped multicollinear features using VIF
  • Modeling: Compared Logistic Regression, Random Forest, and SVC
  • Evaluation: ROC AUC, precision/recall, confusion matrix (percent-based)

Results

  • Top Predictors: Tenure, MonthlyCharges, Contract type, and Security services
  • Best Model: Random Forest with ROC AUC of 0.83
  • Business Insight: Customers with high bills and short tenure are most likely to churn

Tech Stack

  • Python 3.9+
  • pandas, numpy, seaborn, matplotlib
  • scikit-learn
  • Jupyter Notebook

Future Work

  • Deploy model as an API or live dashboard
  • Collect time-series service usage data
  • Apply retention strategies to high-risk flagged customers

📬 Contact

Made with ❤️ by Arnav Nambiar
Feel free to connect or reach out if you're interested in collaborating or discussing the project.

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