This repository is dedicated to churn modeling using decision trees, a popular machine learning algorithm for classification problems. Churn modeling refers to the task of predicting customer churn, which is the phenomenon of customers ceasing their relationship with a business or service provider.
The goal of this project is to develop an effective churn prediction model using decision trees, enabling businesses to identify customers who are likely to churn in the near future. By accurately identifying potential churners, businesses can take proactive measures to retain these customers, ultimately reducing customer attrition and improving overall customer satisfaction.
The repository includes a sample churn dataset, carefully curated to represent a typical churn scenario. The dataset contains relevant features such as customer demographics, usage patterns, transaction history, and churn labels.
You will find an implementation of decision trees, a powerful algorithm for classification tasks. The implementation covers key aspects such as tree construction, attribute selection, and handling categorical and numerical features.
Churn modeling often requires feature engineering to extract meaningful insights from the data. This repository provides examples and techniques for feature selection, feature transformation, and handling missing values.
Explore various evaluation metrics and techniques to assess the performance of your churn prediction model. Understand concepts such as accuracy, precision, recall, and F1-score to measure the effectiveness of your decision tree model.
Fine-tune the performance of your decision tree model by optimizing hyperparameters. Discover techniques such as grid search and random search to find the optimal parameter settings for your dataset.
Gain insights into the decision-making process of your decision tree model by visualizing the constructed trees. Utilize visualization tools and libraries to interpret the model's splits and feature importance.
Learn how to deploy your churn prediction model in real-world scenarios. Explore techniques to integrate your model into production systems, making it ready for usage in a business environment.
This repository aims to provide a comprehensive guide to churn modeling using decision trees, catering to both beginners and experienced data scientists. Whether you're new to machine learning or looking to expand your knowledge, this project will equip you with the necessary tools and techniques to build accurate churn prediction models.
https://churn-modeling-with-decision-trees-5tvkietzz57.streamlit.app/