Simple Typo Analysis

<p>🔍<strong> Simple Typo Analysis</strong></p><p>Easily spot and explore variations of a name in your dataset — from subtle misspellings to obvious typos. Perfect for cleaning <strong>customer</strong>, <strong>product</strong>, or <strong>location</strong> data.</p><p>⚙️ How it works</p><p>We combine two powerful checks:</p><ul><li><p><strong>Levenshtein distance</strong> 📝 — counts the number of edits (insertions, deletions, replacements) needed to match two names.</p></li><li><p><strong>Positional matching</strong> 🎯 — counts how many characters are in the exact same position.</p></li></ul><p>By blending these measures, we create a <strong>single similarity score</strong> that makes it easy to find and review potential typos while avoiding false matches.</p><p>📄 Dataset Overview</p><p>This dataset contains <strong>100 customer names</strong> for testing fuzzy string matching and typo detection workflows in KNIME.</p><p><strong>✅ 20 Original Names</strong><br><em>Thilo, Maria, Jonathan, Elena, Sebastian,<br>Alexander, Charlotte, Matthias, Clara, Michael,<br>Sophia, Daniel, Julian, Isabella, Maximilian,<br>Anna, Lukas, Leon, Laura, Benjamin</em></p><p><strong>✏️ 80 Variations</strong><br>Generated by introducing random typos such as <strong>letter swaps</strong>, <strong>deletions</strong>, <strong>insertions</strong>, or <strong>replacements</strong>.<br>These simulate common human data entry mistakes in real-world datasets.</p>