Embark on a journey through the intricacies of customer churn analysis with a focus on precision. In this project, we delve into the realm of predicting customer churn, seeking not only accuracy but a precision-driven approach. Using advanced machine learning techniques, particularly the CatBoost classifier, we aim to uncover nuanced patterns that contribute to customer attrition. Explore the depths of our analysis as we decode the dynamics influencing customer retention, ensuring precision in our predictions.
- Witness the power of the CatBoost classifier in accurately predicting customer churn based on a comprehensive set of features.
- Delve into the intricacies of model performance through a detailed confusion matrix visualization, providing a clear picture of true positives, true negatives, false positives, and false negatives.
- Gain insights into precision, recall, and F1-score, allowing a nuanced understanding of the model's performance across different metrics.
- Explore the Receiver Operating Characteristic (ROC) curve, visualizing the trade-off between true positive rate and false positive rate, complemented by the AUC-ROC score for a holistic evaluation of model efficacy.
Download the Customer Churn Dataset from the repository.
Upload the downloaded dataset to your Google Drive. This ensures convenient access to the dataset during the project.
Open the provided Jupyter Notebook on your local machine or in a Google Colab environment.
If using Google Colab, configure Google Drive connection by following the instructions in the notebook. This allows access to the dataset stored in Google Drive.
Execute the notebook cells sequentially to perform tasks such as loading the dataset, preprocessing data, training the CatBoost model, and evaluating model performance.
Analyze accuracy, confusion matrix, classification report, and AUC-ROC curve visualizations within the notebook to gain insights into customer churn predictions.
Modify the notebook to experiment with different features, models, or preprocessing techniques. Iterate on the project to enhance its capabilities and tailor it to your specific dataset or business requirements.
If you encounter any issues or wish to suggest improvements for this project, kindly submit an issue using the "Issues" tab above. In case you'd like to contribute a fix, please submit a pull request (PR) and reference the corresponding issue you created.