ML Project flow
🚀 End-to-End ML Project Flow – From Raw Data to Real-World Impact This is where machine learning becomes more than just models—it becomes a product. Whether you're building a churn prediction system or a recommendation engine, understanding the full journey from data to deployment is critical. Here’s a step-by-step breakdown of what real ML projects look like in the industry 👇 📌 1. Problem Scoping – Start with "Why" What business problem are you solving? What does success look like? Define the objective in measurable terms Identify data needs and project constraints Align with stakeholders early 🧭 2. Data Acquisition – The Foundation The right data beats the best algorithm. Collect from APIs, SQL, CSV, cloud storage, logs Ensure quality, relevance, and ethical sourcing Label carefully (if supervised) 🧹 3. Data Preprocessing – Where the Magic Begins Real-world data is messy. This is where 80% of your time goes. Handle miss...