🚀 Week 3 of Machine Learning Zoomcamp Complete!🎉
🔍 Predicting Churn with Logistic Regression: Why It Matters ?
In any business, the churn rate is a key indicator of customer retention. By using logistic regression, we can predict churn in a data-driven way, making it possible to address potential customer losses proactively. 📊
This classic machine learning classification technique allows us to analyze customer behavior, identify risk factors, and make informed decisions to reduce churn. Through feature importance methods like mutual information, correlation, and risk ratio, we can better interpret our model’s predictions and act on them
For this project we used <a href="https://www.kaggle.com/datasets/blastchar/telco-customer-churn" target="_blank" rel="noopener nofollow">Telco Customer Chrun Dataset</a> from Kaggle
Discussing these topics with fellow learners at <a href="https://www.linkedin.com/company/datatalks-club/" target="_blank" rel="noopener nofollow">DataTalksClub</a> . Huge thanks to <a href="https://www.linkedin.com/in/agrigorev/" target="_blank" rel="noopener nofollow">Alexey Grigorev</a> for creating such a great learning community!
I also completed the Week 3 homework! 🎓 Feel free to check out my solutions and code here: <a href="https://github.com/RuiFSP/ml-zoomcamp/blob/main/03-classification/homework03.ipynb" target="_blank" rel="noopener nofollow">github-homework03</a>
On to week 4! 💪
#DataScience #MachineLearning #CustomerRetention #LogisticRegression #Churn

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