Description:
Breast cancer is the most prevalent cancer among women globally, accounting for 25% of all cancer cases. In 2015 alone, it impacted over 2.1 million individuals. The disease begins when cells in the breast grow uncontrollably, forming tumors that can be detected via X-ray or felt as lumps.

The primary challenge in its detection is classifying tumors as malignant (cancerous) or benign (non-cancerous). We invite you to analyze and classify these tumors using machine learning techniques, specifically Support Vector Machines (SVMs), with the Breast Cancer Wisconsin (Diagnostic) Dataset.

Acknowledgements:
This dataset is sourced from Kaggle.

Objective:

• Understand and clean the dataset if necessary.
• Build classification models to predict if the cancer is malignant or benign.
• Fine-tune hyperparameters and compare the performance of various classification algorithms.