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A classification project for forecasting the customers who are likely to churn. Random forests, XGBoost, SVM's used.

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Telecom-Churn-Prediction

A classification project for forecasting the customers who are likely to churn. Random forests, XGBoost, SVM used.

Sections in the notebook:

  1. Data Scrubbing/Cleaning: Columns Renaming & Missing Values Treatment

  2. Exploratory Data Analysis: Correlation, Univariate & Bivariate Analysis using Visualizations

  3. Pre Model Building Steps: Feature Enginerring and Multicollinearity Check

  4. Model Building: Logistic Regression

  5. Model Building: Random Forests- Deriving feature importances and building RF Model using best parameters from Grid Search CV

  6. Model Building: SVM's- Standardizing Data and then running SVM using best parameters from Grid Search CV

  7. Model Building: XGBoost using using best parameters from Random Search CV

  8. Model Comparisons/Evaluation using Classification Report and ROC-AUC Curve

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A classification project for forecasting the customers who are likely to churn. Random forests, XGBoost, SVM's used.

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