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Text parsing and tokenization are crucial steps for processing unstructured textual data. Parsing involves extracting and structuring data from text, while tokenization breaks down text into meaningful elements or "tokens" for analysis. This approach is particularly useful for unstructured datasets like customer reviews, social media comments, or any free-form text where content analysis is required. By structuring the data through tokenization, a data analyst can perform further analysis, like sentiment analysis or topic modeling, to extract insights from textual data. The other options are incorrect because: • Linear Regression is a statistical technique, unsuitable for unstructured text. • Data Normalization standardizes numeric values, not text. • Data Aggregation consolidates data, but doesn't handle text processing specifically. • K-means Clustering groups data, but tokenization is first needed for textual data.
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In a right triangle, the hypotenuse is 13 cm and one leg is 5 cm. What is the radius of the circle inscribed in the triangle?
A sum increases by 40% in 4 years at simple interest. What will be the compound interest on Rs 14000 at the same rate after 3 years?
'P' sold an item at a profit of 40%. If the cost price of the item had been Rs. 150 less and its selling price had been Rs. 30 mo...
The following table shows the monthly rainfall in a city for the first six months of the year: