This tutorial explains how to compute accuracy, precision, recall, F1‑score, and ROC‑AUC from a confusion matrix using sklearn, TensorFlow, and hand‑crafted Python code, illustrating each metric with example data and visualizations.
This article explains the difference between machine learning and deep learning, compares ML algorithms with traditional logic code, introduces the scikit‑learn library, demonstrates data preprocessing, model training with RandomForestRegressor, and shows how to build a voting regressor for disease progression prediction using Python.
This article demonstrates how to perform multiple linear regression using sklearn's LinearRegression and the statsmodels library in Python, covering both formula‑based and array‑based approaches, complete with example data, code snippets, and model evaluation details.
This guide explains how to use scikit-learn’s SimpleImputer and KNNImputer to fill missing values, covering available strategies such as mean, median, most frequent, and constant, and provides complete Python code examples with expected output.
This guide demonstrates how to perform multiple linear regression in Python using sklearn's LinearRegression and the statsmodels library, covering data preparation, model fitting, coefficient extraction, prediction, and detailed statistical diagnostics with example cement heat data.
An in‑depth guide to sklearn.svm explains SVM classification and regression, details key parameters such as C and kernel types, demonstrates how to use GridSearchCV for hyperparameter tuning, and provides complete Python code examples for iris classification and California housing price prediction.
This article demonstrates how to perform multiple linear regression using scikit‑learn's LinearRegression and the statsmodels library, covering data preparation, model fitting, code examples, and interpretation of statistical results for a cement heat‑release case study.
