🚀 Day 37 | Project Part 3: Feature Engineering in Machine Learning 🔥| Tamil

In this video, I take you through the complete Feature Engineering process for our Movie Revenue Prediction project.
👉 We handle missing values, scale numeric features, extract date-based features, encode categorical data, and prepare the dataset for modeling.

This step is the backbone of any ML project – the better your features, the better your model! 💯

📌 Topics covered:

Numeric transformations (Imputer + Scaler)

Date features (Month & Quarter effects)

Genre & Company encoding (Multi-hot & Top-N)

Categorical language handling

Why we still need Pipelines & ColumnTransformer

🔗 Stay tuned for the next part where we’ll train and evaluate the model!

📌 Resources:
🔗 Kaggle Dataset: https://www.kaggle.com/datasets/asaniczka/tmdb-movies-dataset-2023-930k-movies?resource=download
📂 Code File (Day 37): https://drive.google.com/drive/folders/1T31Tju9UTMOPy06aKFKNAYPBFEp91Nd5?usp=drive_link

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Видео 🚀 Day 37 | Project Part 3: Feature Engineering in Machine Learning 🔥| Tamil канала SkillEdge Coaching (தமிழில்)