Encoding techniques are used to convert categorical data into a numerical format that Machine Learning models can understand. Most ML algorithms cannot work directly with text or categorical values, so encoding is an essential step in data preprocessing.<\/p>\n\n\n\n
Categorical data represents information such as colors, labels, or categories. For example, a column \u201cColor\u201d may contain values like Red, Blue, and Green. Machine Learning models require numerical input, so these categories need to be converted into numbers.<\/p>\n\n\n\n
Label Encoding assigns a unique number to each category. For example, Red = 0, Blue = 1, Green = 2. This method is simple and works well for ordinal data, where the categories have a natural order.<\/p>\n\n\n\n
Advantages:<\/strong><\/p>\n\n\n\n Limitations:<\/strong><\/p>\n\n\n\n One-Hot Encoding creates a separate binary column for each category. Each row has a 1 in the column corresponding to its category and 0 in others. For example, a \u201cColor\u201d column with Red, Blue, Green will become three columns: Is_Red, Is_Blue, Is_Green.<\/p>\n\n\n\n Advantages:<\/strong><\/p>\n\n\n\n Limitations:<\/strong><\/p>\n\n\n\n Ordinal Encoding assigns numbers to categories based on a meaningful order. For example, a \u201cSize\u201d column with Small, Medium, Large can be encoded as Small = 1, Medium = 2, Large = 3.<\/p>\n\n\n\n Advantages:<\/strong><\/p>\n\n\n\n Limitations:<\/strong><\/p>\n\n\n\n Binary Encoding converts categories into binary numbers. This method is useful for columns with high cardinality (many unique categories) because it reduces the number of dimensions compared to One-Hot Encoding.<\/p>\n\n\n\n <\/p> <\/div> <\/div>\n\n\n\n Encoding techniques are essential for converting categorical data into numerical format for Machine Learning models. Choosing the right encoding method depends on the type of categorical data (nominal or ordinal) and the number of unique categories. Proper encoding ensures that your models can learn effectively from the data.<\/p>\n\n\n\n
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One-Hot Encoding<\/h2>\n\n\n\n
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Ordinal Encoding<\/h2>\n\n\n\n
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Binary Encoding<\/h2>\n\n\n\n
Conclusion<\/h2>\n\n\n\n