Building a Predictive Model: Customer Behavior Analysis скачать в хорошем качестве

Building a Predictive Model: Customer Behavior Analysis

•In this video, we move from exploratory data analysis to building a machine learning model for customer churn prediction. This is Part 2 of the SmartBank Data Science Job Simulation, where we focus on training, validating, and evaluating a predictive model using Python and scikit-learn. You’ll see how logistic regression and other classification techniques can be applied to real-world customer data to forecast churn risk. We walk through model training, threshold tuning, ROC-AUC evaluation, cross-validation, and confusion matrix analysis, all within a practical business context. This session demonstrates how data analytics and machine learning can be used to predict customer behavior, support retention strategies, and drive data-driven decision-making. If you're learning data science, transitioning into a data analyst or data scientist role, or looking for real-world machine learning project experience, this walkthrough will help you understand the full modeling pipeline from raw data to business insight. Topics covered: •Customer churn prediction •Machine learning for beginners •Logistic regression and classification models •Model evaluation metrics (ROC-AUC, precision, recall, F1-score) •Cross-validation and threshold tuning •End-to-end data science project Watch Part 1:    • Building a Churn Analysis Project from Scr...   for the full exploratory data analysis and data preparation process. Full Job Simulation: https://www.theforage.com/simulations... Full Code: https://docs.google.com/document/d/1q... 00:00 Introduction 02:00 Recap 04:06 Project goal 07:00 Setting up features for modeling 11:35 Setting up preprocessing pipeline 14:31 Train-Test Split Explained 15:29 Building the Logistic Regression Model 17:49 Model Evaluation Metrics Overview 23:20 ROC-AUC Explained 25:50 Confusion Matrix Breakdown 28:48 Threshold Tuning Step-by-Step 33:08 Improving Recall vs Precision Trade-Off 41:47 Stratified Cross-Validation 42:30 Cross-Validation Results Interpretation 43:34 Business Impact & Model Limitations 44:23 Final Thoughts & What’s Next