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Showing posts from December, 2024

Carbohydrates || Lipids|| Proteins || Vitamins & Minerals || Fats and Oils

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Carbohydrates Carbohydrates are organic compounds composed of carbon, hydrogen, and oxygen, typically with a hydrogen-to-oxygen ratio of 2:1. They are one of the primary macronutrients and serve as a significant energy source for living organisms. They also play vital roles in the structure and function of cells. Sources of Carbohydrates Natural Sources: Plants: Grains (wheat, rice, maize), fruits (bananas, apples, mangoes), vegetables (potatoes, carrots), and legumes (lentils, beans). Dairy Products: Milk, yogurt. Sugars: Honey, sugarcane, and fruits. Processed Foods: Bread, pasta, cookies, and other carbohydrate-rich snacks. Functions of Carbohydrates Energy Source: Provide 4 kcal of energy per gram. Protein Sparing: Spare proteins from being used as energy sources. Structural Role: Form structural components like cellulose in plants. Storage: Serve as storage forms of energy (e.g., glycogen in animals and starch in plants). Sweetening Agents: Many ca...

Data Mining || Supervised vs. Unsupervised Techniques || Dimensionality Reduction || Partitioning Methods

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                          Data Mining Need for Data Mining Data mining is the process of extracting meaningful patterns, trends, and knowledge from large datasets. Its need arises from: Data Explosion : Organizations generate massive amounts of data that need to be analyzed effectively. Decision-Making : It supports informed decision-making by identifying hidden patterns and correlations. Competitive Advantage : Helps businesses optimize processes, enhance customer relationships, and forecast trends. Automation : Reduces manual data analysis efforts and increases efficiency. Problem-Solving : Detects anomalies, predicts outcomes, and aids in problem-solving across domains. Data Mining Tasks Classification : Assigning items to predefined categories (e.g., spam email detection). Clustering : Grouping similar data points together without predefined labels (e.g., customer segmentation). Regression : Predicting ...

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