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大三上数据挖掘项目总结
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<h2 id="SCNU-CS-2018-DataMining"><a href="#SCNU-CS-2018-DataMining" class="headerlink" title="SCNU-CS-2018-DataMining"></a>SCNU-CS-2018-DataMining</h2><p>这一次整理的是数据挖掘课程项目，分为平时项目与期末项目。以小组为单位，课程要求使用数据挖掘方法进行数据分析和建模，最终需要提交代码、论文，期末项目还需要进行小组汇报，具体的内容请参见 <span class="exturl" data-url="aHR0cHM6Ly9naXRodWIuY29tL2NoYXJmb2xlL1NDTlUtQ1MtMjAxOC1EYXRhTWluaW5n">Github<i class="fa fa-external-link-alt"></i></span>。</p>
<h2 id="项目结构"><a href="#项目结构" class="headerlink" title="项目结构"></a>项目结构</h2><p>Project-1为平时项目，Project-2为期末项目，项目结构与具体的使用方法请参照下面的内容。<br><img src="https://charfole-blog.oss-cn-shenzhen.aliyuncs.com/image/项目结构图.png" alt="项目结构图"></p>
<a id="more"></a>
<h2 id="项目一简介"><a href="#项目一简介" class="headerlink" title="项目一简介"></a>项目一简介</h2><p><strong>客户流失管理</strong>：根据多个客户的数据，利用数据挖掘方法进行建模，使模型能够通过用户的信息来预测用户是否已经流失（二分类问题：1为流失、0为无流失）。在这个项目中，你需要使用决策树（预剪枝和后剪枝）、多层感知机、逻辑回归、决策树来进行实验，提交每个模型的混淆矩阵，并在最终的报告中呈现各个模型的性能。</p>
<h2 id="项目二简介"><a href="#项目二简介" class="headerlink" title="项目二简介"></a>项目二简介</h2><p><strong>抑郁症 HAMD 评分预测</strong>：数据集“data_depression.xlsx”包含两个数据表单：“抑郁脑电图” 和 “针刺效益、前后脑电图”。表单 “抑郁症脑电图” 字段有姓名、性别、年龄、HAMD评分、以及针刺前脑电节律波幅指数和节律指数。表单“针刺效益、前后脑电图”字段主要有姓名、针刺前脑电指标和针刺后脑电指标。<strong>其中，表单 ”针刺效益、前后脑电图“ 中患者只是表单抑郁症脑电图中患者的一部分。</strong>根据17-item HAMD抑郁症评价量表，字段HAMD评分是患者抑郁症程度评价。</p>
<p><strong>提示</strong>：在预处理中需要将同一个患者的数据拼接成一行，表单一中的数据为针刺前的脑电指数，有 HAMD 评分，可以进行有监督学习；表单二中加入了部分患者无 HAMD 评分的针刺后脑电数据，可以将脑电前后数据进行拼接，从而进行半监督学习。</p>
<h2 id="开发环境和使用方法"><a href="#开发环境和使用方法" class="headerlink" title="开发环境和使用方法"></a>开发环境和使用方法</h2><p>为了能够实时查看到数据，我和项目成员选取了 Jupyter Notebook 的方式在 <span class="exturl" data-url="aHR0cHM6Ly9jb2xhYi5yZXNlYXJjaC5nb29nbGUuY29tL25vdGVib29rcy9pbnRyby5pcHluYg==">Colab<i class="fa fa-external-link-alt"></i></span> 上进行实验。如果你此前没有配置过相关的环境，在此极力推荐在线的 Jupyter Notebook环境（<span class="exturl" data-url="aHR0cHM6Ly9jb2xhYi5yZXNlYXJjaC5nb29nbGUuY29tL25vdGVib29rcy9pbnRyby5pcHluYg==">Colab<i class="fa fa-external-link-alt"></i></span> , <span class="exturl" data-url="aHR0cHM6Ly93d3cua2FnZ2xlLmNvbS9ub3RlYm9va3M/c29ydEJ5PWRhdGVSdW4mYW1wO3RhYj1wcm9maWxl">Kaggle<i class="fa fa-external-link-alt"></i></span>）进行实验，以免花费太多不必要的精力在配置环境和安装依赖库中。</p>
<p>所有 .ipynb 文件 均可在 <span class="exturl" data-url="aHR0cHM6Ly9jb2xhYi5yZXNlYXJjaC5nb29nbGUuY29tL25vdGVib29rcy9pbnRyby5pcHluYg==">Colab<i class="fa fa-external-link-alt"></i></span> 上直接运行，但不排除会因为没有 pip 安装库等原因导致一些小 bug 出现。</p>
<h2 id="写在后面"><a href="#写在后面" class="headerlink" title="写在后面"></a>写在后面</h2><p>由于项目以小组单位进行开发，所以各位成员的代码风格有一定的差异，也不排除某些方面的问题有所忽略，如发现有错误或不足，十分欢迎 issue 或 pr。希望能帮助到学习该门课程的同学。</p>
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