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This post provides an in-depth overview of the most important evaluation metrics for classification models, including accuracy, precision, recall, F1-score, and AUC-ROC, and explains how each metric can be used to assess model performance in various machine learning tasks.
This post introduces the fundamental concepts of classification in machine learning, exploring how models can categorize data into predefined labels. It covers basic algorithms such as logistic regression, decision trees, and support vector machines, providing a foundation for understanding supervised learning tasks.
This post explores various regression techniques in machine learning, from linear regression to more advanced methods like decision trees and random forests. It provides insights into how these models predict continuous outcomes, along with use cases and evaluation metrics such as RMSE, MAE, and R-squared.
The report analyzes monthly sales performance data from January 2022 to December 2024, focusing on key metrics such as total orders, gross sales, discounts, returns, net sales, shipping costs, and total sales to provide insights into the company's revenue trends and operational efficiency, utilizing Python in RStudio for inline code with Quarto