Thread by @abhi1thakur

🚀 If you are starting with machine learning / deep learning and get a new dataset to work on, either on kaggle or in real-world or just for fun. There are a few things you must always take care of to squeeze the most out of your model and make it awesome: ⬇️⬇️⬇️
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🔹 Look at the data carefully. Do EDA.
🔹 Look at the targets. See how they are distributed and what kind of problem this is.
🔹 Choose the right metric to evaluate your models
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🔹 Split the data into folds. You can use this for cross validation or for hold out based validation
🔹 Build a first basic model. This is going to be your baseline.
🔹 Now try to improve on the baseline by adding new features
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🔹 To add new features, go back to data. Look at the EDA. That's why its quite important
🔹 When you think you have reached a limit with feature engineering, try different models
🔹 Keep log of all the scores, features and models
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🔹 When you think you have reached a limit with different models, try feature selection
🔹 Done with feature engineering and feature selection? You will realize that by following above steps, you have also chosen a few best models that work well with your data
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🔹 Now its time to do hyperparameter optimization and squeeze the last few drops from your best models
🔹 Wrap everything in docker, so that it's reproducible.
🔹 Build a simple api endpoint or a fully-fledged web application to serve your model and brag about it ;) 6/6