Comprehensive Skincare Product Data Extracted from Dermstore – Ingredient List in JSON Format|护肤产品数据集|成分分析数据集

<p>This dataset contains meticulously extracted information on 100+ skincare products from Dermstore, one of the leading online skincare retailers. Designed for beauty analysts, researchers, and data scientists, this dataset provides a deep dive into ingredient compositions, product categories, and targeted skin concerns. Each entry is enriched with essential details, including full ingredient lists, enabling in-depth ingredient analysis and skincare product comparisons.</p>
<p><strong>Data Highlights:</strong></p>
<ul>
<li><strong>Ingredient Listings</strong>: Comprehensive ingredient details for each product, ideal for ingredient-based research and analysis.</li>
<li><strong>Product and Brand Details</strong>: Product name, brand, category (e.g., cleansers, serums, sunscreens), and skin concerns (such as acne, aging, or sensitivity) offer full product context.</li>
<li><strong>User Ratings and Reviews</strong> (where available): Consumer ratings add valuable insights into product performance, satisfaction, and ingredient effectiveness.</li>
</ul>
<p><strong>Dataset Scope:</strong> The data includes essential product details and ingredient information for a variety of skincare products available on Dermstore. It covers skincare categories such as moisturizers, cleansers, serums, and sunscreens, making it a versatile tool for anyone interested in the ingredients behind skincare.</p>
<p><strong>Dataset Use Cases:</strong></p>
<ol>
<li><strong>Trend Analysis</strong>: Identify ingredient trends, such as the popularity of specific actives like hyaluronic acid, niacinamide, or retinol.</li>
<li><strong>Recommendation Models</strong>: Build recommendation algorithms for skincare products based on ingredient compatibility and skin concerns.</li>
<li><strong>Product Comparison</strong>: Conduct comparative studies on products with similar or unique ingredients.</li>
<li><strong>Health and Safety Analysis</strong>: Research ingredient safety, allergens, and natural vs. synthetic composition trends.</li>
</ol>
<p><strong>Ideal For</strong>:</p>
<ul>
<li><strong>Data Scientists</strong>: For projects in predictive modeling, trend analysis, and building recommendation systems.</li>
<li><strong>Beauty and Health Researchers</strong>: For ingredient efficacy studies, consumer trend analysis, and product formulation research.</li>
<li><strong>Skincare Enthusiasts</strong>: Provides insights into product compositions and ingredient effectiveness.</li>
</ul>
<p>&nbsp;</p>

<p>本数据集收录了来自Dermstore，一家领先的在线护肤零售商，关于100多个护肤产品的精心提取信息。专为美容分析师、研究人员和数据科学家设计，该数据集深入探讨了成分组成、产品类别以及针对的皮肤问题。每个条目都包含了详尽的详细信息，包括完整的成分列表，从而实现了深入成分分析和护肤产品的比较。</p>

<p><strong>数据亮点：</strong></p>

<ul>
<li><strong>成分列表</strong>：每个产品的全面成分细节，非常适合基于成分的研究和分析。</li>
<li><strong>产品和品牌详情</strong>：产品名称、品牌、类别（例如，洁面乳、精华液、防晒霜）以及皮肤问题（如痤疮、衰老或敏感性）提供了全面的产品背景。</li>
<li><strong>用户评分和评论</strong>（如有）：消费者评分为产品性能、满意度和成分有效性提供了宝贵的见解。</li>
</ul>

<p><strong>数据集范围：</strong> 数据包括Dermstore上各种护肤产品的必要产品详情和成分信息。涵盖了护肤类别，如保湿霜、洁面乳、精华液和防晒霜，使其成为任何对护肤成分感兴趣的人的通用工具。</p>

<p><strong>数据集应用场景：</strong></p>

<ol>
<li><strong>趋势分析</strong>：识别成分趋势，例如透明质酸、烟酰胺或视黄醇等特定活性成分的流行程度。</li>
<li><strong>推荐模型</strong>：基于成分兼容性和皮肤问题构建护肤产品的推荐算法。</li>
<li><strong>产品比较</strong>：对具有相似或独特成分的产品进行对比研究。</li>
<li><strong>健康和安全分析</strong>：研究成分安全性、过敏原以及天然与合成成分组成的趋势。</li>
</ol>

<p><strong>适用对象：</strong></p>

<ul>
<li><strong>数据科学家</strong>：适用于预测建模、趋势分析和构建推荐系统等项目的项目。</li>
<li><strong>美容与健康研究人员</strong>：适用于成分功效研究、消费者趋势分析和产品配方研究。</li>
<li><strong>护肤爱好者</strong>：提供了关于产品组成和成分有效性的见解。</li>
</ul>