1️⃣ AutoML Vision Goal: Build a customer churn prediction model without coding Approach: Use drag-and-drop AutoML platforms with pre-built algorithms Simplicity Strategy: No coding required Pre-configured models Visual dashboards for understanding --- 2️⃣ Tool Selection Google AutoML (Vertex AI - Free Tier) Pros: Easy UI, strong performance Cons: Limited free usage Teachable Machine (by Google) Pros: Beginner-friendly, fast setup Cons: Limited for structured data Obviously AI Pros: No-code predictive modeling Cons: Paid features for advanced use Best Fit: Obviously AI (for structured classification tasks) --- 3️⃣ Data Preparation Workflow Collect customer data (Excel/CSV format) Clean data: Remove duplicates Fill missing values Format data: Columns = features Rows = records Upload file directly into AutoML tool --- 4️⃣ Model Building Process Select target column (e.g., churn: Yes/No) AutoML automatically: Selects algorithm Trains multiple models Run training with one click Evaluate using: Accuracy Precision Recall --- 5️⃣ Deployment Strategy Export model as API or dashboard Deployment options: Web app integration Share prediction dashboard Use cases: Predict churn in real-time Upload new data for predictions --- 6️⃣ Optimization & Improvement Improve data quality Add more relevant features Remove unnecessary columns Retrain model regularly Use larger dataset for better accuracy --- 7️⃣ Limitations & Challenges Limited customization (no deep control) Accuracy depends on data quality Free tools may have usage limits Not ideal for complex ML problems --- 8️⃣ Beginner-Friendly Workflow 1. Collect data in Excel 2. Clean and organize data 3. Upload to AutoML tool 4. Select prediction target 5. Click “Train Model” 6. Check results 7. Deploy as web app or dashboard --- 9️⃣ Scaling & Next Steps Move to advanced tools like: Google Vertex AI (paid tier) DataRobot Integrate with: CRM systems Websites Add automation: Real-time predictions Scheduled retraining --- 🔟 AutoML Blueprint (FINAL SUMMARY) Best Tool: Obviously AI Biggest Limitation: Limited control over model customization Top Improvement Tip: Focus on better and cleaner data Scalability Path: Upgrade to advanced AutoML platforms + API integration --- ✅ Final Outcome: A simple, no-code ML system that allows beginners to build, deploy, and improve machine learning models easily 🚀