Category: Machine Learning
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Linear regression is a statistical technique for modeling relationships between a dependent variable and independent variables to make predictions. It includes simple and multiple linear regression, relies on assumptions like homoscedasticity, and uses metrics like Mean Squared Error to evaluate performance. It’s widely applied in fields such as finance and biology.
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In machine learning, data is divided into training and testing sets to evaluate model performance. A common split is 70-30% or 80-20%. Using Scikit-learn’s train_test_split allows random distribution while maintaining class proportions. The Iris dataset is used in the example, demonstrating the process and verifying class distributions.
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Data processing is essential in AI and ML, transforming messy raw data into usable formats for algorithm effectiveness. Critical steps include data cleansing, integration, transformation, and feature engineering. Techniques for handling missing values, like using mean or median, and feature scaling, such as StandardScaler, improve model performance and prevent overfitting.
PRADOSH 