Hands-on Data Preparation and Feature Engineering in Python | Zomato Case Study

📋 In this video, we dive into the crucial aspect of feature engineering and data preparation using a real-world food delivery app dataset. 🛵📦

🔍 Tackling Categorical Features
Our dataset is rich with categorical features. Watch as we handle them in the smartest ways possible, ensuring we get the most out of our data. 📈💡

⚠️ Dealing with Data Issues
We face several challenges like missing data, missing values in the target column, numbers represented as strings, and categorical features with high cardinality. Learn how we address these issues step-by-step. 🚧🛠️

💡 Deriving Meaningful Features
We don't just clean data; we derive meaningful features that provide a rock-solid foundation for understanding the business. See how we transform raw data into valuable insights! 💼🔍

🌟 Join Us on This Data Adventure!
Whether you're a beginner or an experienced data scientist, this video is packed with practical tips and techniques for mastering feature engineering and data preparation. Let's turn messy data into actionable insights together! 🚀📊

Dataset Link - https://www.kaggle.com/datasets/prana...

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