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Data preprocessing for machine learning.

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In this video, we will understand why data preprocessing is important in machine learning.

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Identify common data issues, missing, noisy, and inconsistent.

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Describe basic preprocessing steps like cleaning, transformation, and splitting.

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What is data preprocessing?

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In machine learning, raw data cannot be used directly.

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It needs to be cleaned and prepared so the model learns correctly.

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This step is called data preprocessing.

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Without it, poor quality data leads to poor predictions,

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also known as garbage in, garbage out.

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Common data problems.

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Data often contains errors that affect accuracy.

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To build reliable models, these issues must be fixed.

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The most common problems are missing data, empty or incomplete values.

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Noisey data, incorrect or extreme values.

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In consistent data, duplicates or mixed formats.

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Pre-processing techniques.

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Pre-processing improves the quality of data so the model can learn better.

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Two main steps are involved.

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Cleaning, filling missing values, correcting errors, and removing duplicates.

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Transformation, scaling numbers to a similar range and converting categories into numbers through encoding.

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Splitting data.

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Once data is cleaned, it must be divided into parts for training and testing.

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This ensures the model is both accurate and fair.

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Training set.

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The larger portion used to teach the model.

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Testing set.

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The smaller portion used to check accuracy.

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Data preprocessing is a crucial first step in machine learning.

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It ensures that raw data is cleaned, transformed, and properly split

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so models can learn accurately and make reliable predictions.