Why Feature Engineering is a an important skill for data science and machine learning
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AI for Techies on Instagram: "Feature Engineering: Strategy > Preprocessing Why it matters: → Models learn from features, not raw data → Better features = better accuracy & insights → Often more impactful than the model itself Key techniques: 🔹 Transform – Fix skewness, scale data 🔹 Extract – Structure the unstructured (PCA, BERT) 🔹 Interact – Reveal hidden patterns (e.g., area = length × width) Pro tip: Start with EDA. Let stats + domain knowledge guide you. Validate with RFE, mutual info, o
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Aarti Sinha on Instagram: "Day 42, Feature Engineering Series: Unleashing the power of Scikit-Learn’s Pipeline that is used to combine the data preprocessing steps into a single unit. Use of pipeline ensures that the data preprocessing steps and modelling steps are executed in the correct order thus making it less complex. #featureengineering #datapreprocessing #pipeline #machinelearningmodels #featureselection#statistics #datascience #faang #trainning #CodeLife” Data Scientist,Data Engineer, Da
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