1. Machine Learning Algorithms Require Numeric Input Most ML algorithms (e.g., linear regression, neural networks, SVM) work with numerical data. Since raw data often contains categorical features (e.g., colors, countries, gender), encoding converts them into numbers.2. Preserves Meaningful Relationships in Data Proper encoding ensures that categorical data is represented in a way that maintains its significance:Ordinal Encoding for ordered categories (e.g., "low," "medium," "high" → 0, 1, 2).One-Hot Encoding for nominal (unordered) categories (e.g., "red," "blue," "green" → binary columns).3. Avoids Misleading Numerical Assignments Simply assigning arbitrary numbers (e.g., "dog"=1, "cat"=2) can mislead algorithms into assuming ordinal relationships (e.g., "cat" > "dog"). Encoding methods like one-hot eliminate this issue.4. Improves Model Performance The right encoding technique can enhance model accuracy:One-Hot Encoding prevents bias in nominal data.Target Encoding (mean encoding) can capture relationships between categories and the target variable.Embeddings (for high-cardinality data) reduce dimensionality while preserving information.5. Handles Text and High-Cardinality Data Techniques like:Count/Frequency Encoding for rare categories.Hashing for large categorical spaces.Word Embeddings (e.g., Word2Vec) for NLP tasks.6. Normalizes Scales for Distance-Based Algorithms Methods like Binary Encoding or Label Encoding help algorithms like KNN or clustering, where distance calculations are sensitive to numeric scales.Common Encoding Techniques: Label Encoding: Assigns integers to categories (suitable for tree-based models).One-Hot Encoding: Creates binary columns for each category (good for linear models).Target/Mean Encoding: Replaces categories with the mean of the target variable (careful with overfitting).Hashing/Binning: For high-cardinality features.

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computation easy, understanding easy, numerical data represented on graph easily

we do feature encoding so the model can work easily on our data. Computation and processing become easier and faster because the CPU works with numerical values (binary numbers). Feature encoding makes the data simple to understand.

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Very Important Lecture. Because without learning these techniques, we are unable to train our model. Or we can say that model will ignore our data, model will say you are poor and I am rich so go out from here and wear clean clothes, then meet me.

I learned in this lecture about (Algorithm Compatibility, Efficiency and Performance, Feature Representation, Support Unseen categories, Less Memory Usage, One Hot Encoding).

I learned from this video about (1- Algorithm Compatibility, 2- Efficiency and Performance, 3- Feature Representation, 4- Support Unseen category, 5- Less Memory Usage, and 6-One Hot Encoding)

AOA, I also learned how encoding is necessary. There are a few reasons why encoding is important; let's look at them. 1- Algorithm Compatibility ( work at numeric data ) 2- Efficiency and Performance ( less computation power ) 3- Feature Representation ( remove bias) 4- Support Unseen category 5- Less Memory Usage 6-One Hot Encoding ALLAH KAREEM ap ko dono jahan ki bhalian ata kray AAMEEN.