@datascibykashi: Today we break down how Machine Learning models are actually built. Not magic. Not guessing. Just steps. Simple. Clear. Repeatable. 10 Steps to Build a ML Model: 1. Understand the problem (Classification? Regression? Prediction?) 2. Collect relevant and reliable data 3. Clean the data (missing values, duplicates, noise) 4. Explore the data (patterns, trends, correlations) 5. Preprocess & Transform (scaling, encoding, splitting) 6. Select the right algorithm (based on your use-case) 7. Train the model (feed the data, adjust parameters) 8. Evaluate performance (accuracy, precision, recall, RMSE etc.) 9. Tune / Optimize (hyperparameters, feature selection) 10. Deploy the model (API, app, dashboard, real-world use) Most people stop at step 7. But real data scientists go until step 10. That’s where impact lives. 🔥 If you’re following this journey… You’re not just learning ML. You’re training your brain to think like a Data Scientist. Keep going. One step a day. You got this. 🚀🤍 #Day78 #machinelearningjourney #datascience #machinelearning #ml #python #Tech #studywithme #bigdata #ai #deeplearning #dataanalysis #studentlife #motivation #tiktoktech #kashilearning #roadtodataScientist #sportsontiktok

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