Welcome to the "Churn Prediction" repository! This project focuses on predicting customer churn using a variety of traditional machine learning models implemented with scikit-learn. Whether you're new to machine learning or looking to enhance your skills, this repository provides a great opportunity to explore concepts like decision trees, random forests, logistic regression, and support vector machine classifier.
In this repository, you will find a comprehensive school assignment that delves into churn prediction in the context of customer retention. By utilizing tools such as confusion matrices, cross-validation, hyperparameter tuning, and more, we aim to build robust models that can accurately predict churn by leveraging the power of machine learning.
Explore a range of topics related to churn prediction and machine learning covered in this repository:
- Confusion Matrix
- Crossvalidation
- Decision Trees
- Hyperparameter Tuning
- Logistic Regression
- Machine Learning
- Model
- Pipeline
- Random Forest
- Scikit-Learn
- SVM Classifier
If you're ready to dive into the world of churn prediction and machine learning, follow these simple steps to get started with this repository:
- Clone the repository to your local machine.
- Install the necessary dependencies using
pip install -r requirements.txt. - Open the Jupyter notebook or Python script to explore the code and data provided.
- Run the code and experiment with different models and parameters to enhance your understanding of churn prediction.
The data used in this project is sourced from real-world customer information, carefully curated to analyze patterns that indicate potential churn. By working with this dataset, you'll gain valuable insights into customer behavior and predictive modeling techniques.
Our experiments with various machine learning models have yielded promising results in predicting customer churn. By fine-tuning parameters, optimizing pipelines, and analyzing model performance, we have achieved accuracies that showcase the effectiveness of these predictive techniques.
We welcome contributions from the open-source community to further enhance this churn prediction project. Whether you have suggestions for improving model performance, insights on new features to include, or code optimizations, your input is valuable in advancing this research area.
For any questions, feedback, or suggestions related to this repository, feel free to reach out to us. We value your input and are here to assist you on your journey through churn prediction and machine learning.
📌 Check the "Releases" section for the latest updates and enhancements in this repository.
Let's collaborate and harness the power of machine learning to predict churn effectively! 🚀