Hey DEV Community!
I recently wrote a beginner-friendly blog that breaks down one of the most important (yet often overlooked) steps in Machine Learning: Data Preprocessing.
We often jump straight into model building, but did you know that 80% of a successful ML project depends on how well the data is preprocessed? Only 20% depends on the algorithm you choose. So if your data isn’t clean, integrated, and well-prepared, even the best algorithm won’t help.
In this blog, I explain:
What is Data Preprocessing?
Why is it important in ML?
Five essential techniques with real-life examples:
Data Cleaning: Removing noise, handling missing values
Data Integration: Combining data from multiple sources (like triangulation and crowdsourcing)
Data Transformation: Scaling, normalization, generalization, aggregation
Data Reduction: Making big data more manageable (using techniques like dimensional reduction, numeric encoding)
Data Discretization: Converting continuous data into categories or groups
I’ve included analogies like organizing a kitchen or planning a birthday party to help explain complex ideas in a simple and relatable way.
Read the full blog here:
Medium Post — Understanding Data Preprocessing in Machine Learning for Beginners
Whether you're a beginner or refreshing your fundamentals, I’d love for you to give it a read and share your feedback!
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Thanks for reading!
Let’s connect and grow together.
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