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🎓 Full Course HERE 👉: https://sds.courses/ai-az How do you know if your predictive model is actually working? In this video, we break down the Cumulative Accuracy Profile (CAP) curve, a powerful method used to evaluate and compare machine learning models. Unlike random selection, CAP curves help optimize customer targeting, increase sales, and improve decision-making by ranking customers based on purchase probability. Course Link HERE: https://sds.courses/ai-az You can also find us here: Website: https://www.superdatascience.com/ Facebook: / superdatascience Twitter: / superdatasci LinkedIn: / superdatascience Contact us at: [email protected] 💡 By the end of this video, you’ll understand: ✔️ What the CAP curve is and why it’s important ✔️ How to use it for customer segmentation and predictive modeling ✔️ Why it outperforms random selection in marketing campaigns ✔️ How to visually compare different predictive models ✔️ The key difference between CAP and ROC curves 📌 Chapters: 00:00 - Introduction to the CAP Curve 00:37 - The Random Selection Method Explained 01:12 - Predicting Customer Responses with Basic Probability 01:53 - Can We Improve Our Predictions? 02:31 - Building a Predictive Model for Customer Targeting 03:37 - Ranking Customers Based on Purchase Probability 04:42 - How Predictive Models Improve Response Rates 05:54 - The CAP Curve Explained Visually 06:28 - Comparing Different Models Using CAP Curves 07:36 - Random Selection vs. Poor vs. Good Models 08:18 - The Ideal Model: A Perfect Prediction? 09:24 - What Happens If Your Model Is Worse Than Random? 09:55 - CAP vs. ROC Curve – What’s the Difference? 11:03 - Final Thoughts and Next Steps 📢 Hashtags: #DataScience #MachineLearning #CAPCurve #PredictiveModeling #MarketingStrategy #AI #BusinessGrowth #CustomerTargeting #SalesOptimization #MarketingTips #BigData #Analytics #DeepLearning #MarketingTech #ConversionOptimization