Customer Churn Prediction | XGBoost | Python | Data Science | Machine Learning | Tutorial

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Customer Churn Prediction Using XGBoost

In this comprehensive tutorial, we guide you through the process of building a customer churn prediction model using the powerful XGBoost classification algorithm. Starting with importing necessary libraries like Pandas, NumPy, and scikit-learn, we'll explore a Kaggle dataset by Mustafa Bataun Hermes, preprocess the data, and handle categorical and numerical values. Learn step-by-step how to scale the data, split it into training and test sets, and finally build and evaluate the churn prediction model. Perfect for data science enthusiasts looking to enhance their skills in machine learning and data preprocessing.

Customer Churn Prediction, XGBoost Tutorial, Machine Learning, Data Science, Kaggle Dataset, Python Tutorial, Data Preprocessing, Pandas, NumPy, scikit-learn, Classification Algorithm, Predictive Modeling, Data Analysis, Data Engineering, Churn Analysis, Step-by-Step Guide, Coding Tutorial, AI, Customer Retention, Data Mining

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Very helpful tutorial. You should definitely make more videos!

SaimonAlam

Great work brother got some errors on the hyper parameter tunning though

Lubula-On-Tech

Customer Churn Prediction | XGBoost | Python | Data Science | Machine Learning | Tutorial

Customer Churn Prediction | XGBoost | Python | Data Science | Machine Learning | Tutorial

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