Machine Learning in Python#
Classification#
This notebook demonstrates a complete machine learning workflow using the breast cancer dataset from scikit-learn. It guides you through loading and exploring the data, cleaning and preparing it, performing exploratory data analysis, and splitting the dataset into training and testing sets. The features are then scaled to improve model performance before training a Random Forest classifier to distinguish between malignant and benign tumors. Finally, the model’s performance is evaluated using a confusion matrix and classification report, providing insights into its accuracy and predictive capabilities. This tutorial covers essential steps to build, evaluate, and understand a classification model end-to-end.
Regression#
This notebook demonstrates a complete machine learning workflow for a regression task using the California Housing dataset. It guides you through loading and exploring the data, handling missing values, and preprocessing features with scaling and encoding. The dataset is split into training and testing sets before training a Linear Regression model to predict median house values. Finally, the model’s performance is evaluated using standard regression metrics like R-squared, Mean Absolute Error, and Mean Squared Error, providing insights into prediction accuracy. This tutorial covers all essential steps to build, evaluate, and understand a regression model end-to-end.