11- Complete Guide to Data Preparation for ML: Handling Missing data, Outliers and More !
🚀 Welcome to My Machine Learning Data Preparation Tutorial! 🚀
In this video, I’ll guide you through the essential steps of data preparation for machine learning using both Python and R. Whether you’re just starting out or looking to sharpen your skills, this hands-on tutorial will walk you through every important step of data preparation, ensuring your datasets are clean and ready for building machine learning models! 🔥
🔍 What You Will Learn:
1. Data Preparation Importance – Why is it crucial to prepare your data before modeling? 🧠
2. Handling Missing Data – Learn about different imputation techniques:
Mean, Mode, Median Imputation ✏️
KNN Imputation – When and how to use them! 🛠️
Python Code Practice 🐍
3. Duplicates and Near Duplicates Detection – Keep your data clean and free of duplicates! 🧹
Python Code Practice 🖥️
4. Handling Outliers – Techniques to detect and manage outliers in your data. 🚨
5. Data Normalization – Methods like:
Z-Score, Min-Max, and Log Normalization 📊
Python & R Code Practice 🐍📘
6. Data Sampling – Simple random sampling & stratified sampling explained. 🎲
Python & R Code Practice 🔄
7. Dimensionality Reduction – Feature extraction and feature selection techniques. ✂️
💻 Exercise Files: Download all the exercise files and follow along with the code examples!
🔗 Access them here: GitHub - Data Preparation Files
https://github.com/ibtissam09/Hands-o...
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🎯 Who Is This For?
This tutorial is perfect for:
Data science enthusiasts looking to learn data cleaning techniques.
Machine learning beginners wanting hands-on practice in Python and R.
Anyone preparing for machine learning projects and model building!
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Check out the following articles:
✅ / complete-guide-to-data-preparation-for-ml-...
✅ https://medium.com/@ibtissam.makdoun/...
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Happy learning, and see you in the video! 🌟
#MachineLearning #DataPreparation #Python #R #DataCleaning #DataScience #FeatureEngineering