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+<div class="sc-cmRAlD dkqmWS"><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-jgvlka jFuPjz"><div class="sc-gzqKSP ktvwwo"><div style="min-height: 80px;"><div class="sc-etVRix jqYJaa sc-bMmLMY ZURWJ"><h4><strong>Description</strong></h4>
+<p>This dataset provides a collection of patient health records, including key medical conditions, lifestyle habits, and biometric indicators related to stroke occurrence. It includes details on <strong>age, gender, BMI, average glucose levels, hypertension, heart disease, diabetes status, smoking status, and socioeconomic status (SES)</strong>.  </p>
+<p>This dataset can be used for <strong>predictive modeling, healthcare analytics, and medical research</strong> to identify patterns in stroke risk factors. It is particularly useful for <strong>machine learning models</strong> focused on stroke prediction and cardiovascular health analysis.  </p>
+<h4><strong>Potential Use Cases</strong></h4>
+<ul>
+<li>Machine Learning models for <strong>stroke prediction</strong>  </li>
+<li><strong>Exploratory Data Analysis (EDA)</strong> on cardiovascular health metrics  </li>
+<li>Identifying <strong>risk factors</strong> contributing to stroke occurrence  </li>
+<li><strong>Correlation analysis</strong> between lifestyle, biometric data, and stroke  </li>
+<li>Building <strong>classification models</strong> for healthcare applications  </li>
+</ul></div></div></div></div></div>
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