<div class="sc-jegwdG lhLRCf"><div class="sc-UEtKG dGqiYy sc-flttKd cguEtd"><div class="sc-fqwslf gsqkEc"><div class="sc-cBQMlg kAHhUk"><h2 class="sc-dcKlJK sc-cVttbi gqEuPW ksnHgj">About Dataset</h2></div></div></div><div class="sc-davvxH eCVTlP"><div class="sc-jCNfQM ikRdXB"><div style="min-height: 80px;"><div class="sc-etVRix jqYJaa sc-gVIFzB gQKGyV"><p><strong>Description:</strong><br>

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.</p>

<p>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.</p>

<p><strong>Acknowledgements:</strong><br>

This dataset is sourced from Kaggle.</p>

<p><strong>Objective:</strong></p>

<ul>

<li>Understand and clean the dataset if necessary.</li>

<li>Build classification models to predict if the cancer is malignant or benign.</li>

<li>Fine-tune hyperparameters and compare the performance of various classification algorithms.</li>

</ul></div></div></div></div></div>