README.md

metadata

title: PredictingCustomerChurn
sdk: gradio
emoji: 🚀
colorFrom: red
colorTo: yellow
short_description: A model for predicting telecom churn

Predicting Telco Customer Churn using IBM dataset

This project applies machine learning techniques to predict customer churn using a dataset containing customer behavior and subscription details. The aim is to identify customers likely to leave a service and gain insights through model interpretability using SHAP values.

📊 Project Overview

The notebook performs the following tasks:

• Data Preprocessing

  • Categorical encoding using LabelEncoder.
  • Feature scaling using StandardScaler.
  • Dropping irrelevant or low-impact features.

• Exploratory Data Analysis (EDA)

  • Correlation analysis.
  • KDE plots for feature distribution.
  • Heatmap for multivariate correlation.

• Model Building

  • Random Forest Classifier
  • Logistic Regression

• Model Evaluation

  • Classification Report
  • Confusion Matrix
  • Accuracy, Brier Score Loss, ROC AUC Score
  • SHAP analysis for model interpretability

🧰 Technologies & Libraries

• Python
• Pandas
• Seaborn
• Matplotlib
• Scikit-learn
• SHAP

Note: The file data.csv is the dataset got from Kaggle telco-customer-churn