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.