| --- |
| title: PredictingCustomerChurn |
| sdk: gradio |
| emoji: 🚀 |
| colorFrom: red |
| colorTo: yellow |
| short_description: A model for predicting telecom churn |
| --- |
| # Predicting Telco Customer Churn using IBM dataset |
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| 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. |
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| ## 📊 Project Overview |
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| The notebook performs the following tasks: |
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| - **Data Preprocessing** |
| - Categorical encoding using LabelEncoder. |
| - Feature scaling using StandardScaler. |
| - Dropping irrelevant or low-impact features. |
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| - **Exploratory Data Analysis (EDA)** |
| - Correlation analysis. |
| - KDE plots for feature distribution. |
| - Heatmap for multivariate correlation. |
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| - **Model Building** |
| - **Random Forest Classifier** |
| - **Logistic Regression** |
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| - **Model Evaluation** |
| - Classification Report |
| - Confusion Matrix |
| - Accuracy, Brier Score Loss, ROC AUC Score |
| - SHAP analysis for model interpretability |
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| ## 🧰 Technologies & Libraries |
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| - Python |
| - Pandas |
| - Seaborn |
| - Matplotlib |
| - Scikit-learn |
| - SHAP |
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| > **Note:** The file data.csv is the dataset got from Kaggle [telco-customer-churn](https://www.kaggle.com/datasets/blastchar/telco-customer-churn) |
