Predict Employee Turnover

[ramana2074]

Related Issues or bug

-There was an issue related to class imbalance in the employee churn dataset, which could lead to biased model predictions, especially towards the majority class. This was addressed through stratified sampling during train-test splits and additional analysis of model performance using metrics like recall and F1-score.
Fixes: #246

Proposed Changes

• Info about Changes:
  Added data preprocessing steps to handle categorical variables by encoding them into numerical features.
  Implemented class balance visualization using Yellowbrick to detect class imbalance and influence our sampling strategy.
  Built and trained a Decision Tree Classifier and Random Forest Classifier with interactive controls for hyperparameter tuning using the interact function.
  Evaluated models using training and validation accuracy, alongside feature importance plots to identify key factors influencing employee churn.

Additional Info

• This fix also addresses the challenge of overfitting in decision trees by introducing Random Forests, which reduce variance and improve model generalization. The class imbalance issue was mitigated through careful evaluation of recall and F1-score in addition to accuracy.

[github-actions]

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[github-actions]

🎉🎉 Thank you for your contribution! Your PR #289 has been merged! 🎉🎉