🔡 Categorical Data Encoding Methods – A Must-Know for Every Data Scientist

Working with real-world datasets? Chances are, you’ve come across categorical variables like “City,” “Gender,” or “Department.” Before you feed this data into machine learning models, encoding is essential.

📘 In This Blog, You’ll Learn:
✅ What is Categorical Data?
✅ Difference Between Nominal vs Ordinal
✅ Popular Encoding Techniques:
 • Label Encoding
 • One-Hot Encoding
 • Ordinal Encoding
 • Binary & Target Encoding
✅ When to Use Which Encoding
✅ Best Practices & Pitfalls to Avoid

🎓 Who Should Read This?
✔️ Data Science Beginners & Enthusiasts
✔️ Final-Year Engineering & MCA Students
✔️ ML Practitioners Working with Tabular Data
✔️ Bootcamp & Project-Based Learners

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👉https://updategadh.com/data-science-tutorial/categorical-data-encoding-methods/

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Predict the rent of a house based on features like location, size, number of rooms, and more using Machine Learning! A practical project for real-world use cases.

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📈 Predict rent prices based on user input
🧠 Built using Python, Pandas, Scikit-learn
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🎯 Project Name: Stock Price Prediction System

What it does:
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✅ Python
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✅ Matplotlib + Seaborn
✅ Scikit-learn
✅ Optionally Flask for web version

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Updategadh.com



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🍔📦 Food Delivery Time Prediction – Data Science Project 🧠
Predict delivery time based on restaurant, order, and traffic data using powerful machine learning techniques!

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Build a smart system that predicts whether a customer is likely to claim insurance using historical data and machine learning models. Perfect for those exploring data science in the insurance domain.

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🔗 Project Link:
Insurance Claim Prediction

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