Customer Churn Prediction with Machine Learning

🎯 In this video, we walk through a complete machine learning project to predict customer churn using Python, Random Forest and Logistic Regression. We cover everything from data cleaning, feature engineering, SMOTE balancing, to model evaluation and strategic implications. 🚀 Whether you're a data science beginner or looking to level up your ML skills, this project covers it all. 📌 Timestamps: 00:00 – Intro & Topic Overview 00:15 – Project Overview 00:52 – Initial Data Cleaning 01:40 – Churn Imbalance 02:18 – Converting Columns + Drop Missing 03:03 – One-Hot Encoding 03:53 – Feature Engineering (4 new features) 05:29 – Skewness, Kurtosis Analysis 06:25 – Feature Selection 07:54 – Train-Test Split + SMOTE + Scaling 09:22 – Model Training (Random Forest) 10:14 – Evaluation (Accuracy, Confusion Matrix) 11:48 – Precision vs Recall Trade-off 12:50 – Final Thoughts & Strategic Takeaways 🔧 Technologies: Pandas, Scikit-Learn, SMOTE, Matplotlib 📊 Dataset: Telecom Customer Churn (https://www.kaggle.com/datasets/blastchar/telco-customer-churn) 🤝 Interested in collaborating or need help with a similar Machine Learning or Data Science project? I'm open to freelance opportunities and would love to contribute to meaningful data-driven solutions. Feel free to reach out if you have something in mind — always happy to chat! 📬 Contact: [[email protected]] 👉 If you need a clean and complete notebook with a professional LaTeX-ready report, feel free to check out the product at this link: https://mapml.gumroad.com/l/ml-churn-baseline #ChurnPrediction #machinelearningproject #datasciencebasics #randomforest #CustomerChurn