https://gb.yeadoc.com/tags/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/ Recent content in 机器学习 on Clarity Hugo -- gohugo.io zh Copyright © 2008–2018, Steve Francia and the Hugo Authors; all rights reserved. Sat, 11 Apr 2026 19:27:54 +0000 https://gb.yeadoc.com/clustering-algorithms/ Sat, 11 Apr 2026 19:27:54 +0000 https://gb.yeadoc.com/clustering-algorithms/ <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-python" data-lang="python"><span class="line"><span class="ln">1</span><span class="cl"><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span> </span></span></code></pre></div><div class="highlight"><pre tabindex="0" class="chroma"><code class="language-python" data-lang="python"><span class="line"><span class="ln">1</span><span class="cl"><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">make_blobs</span> <span class="c1">#足球数据</span> </span></span></code></pre></div><div class="highlight"><pre tabindex="0" class="chroma"><code class="language-python" data-lang="python"><span class="line"><span class="ln">1</span><span class="cl"> <span class="c1"># 数据量 列数 类别 标准差 随机种子</span> </span></span><span class="line"><span class="ln">2</span><span class="cl"><span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">make_blobs</span><span class="p">(</span><span class="n">n_samples</span><span class="o">=</span><span class="mi">150</span><span class="p">,</span> <span class="n">n_features</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">centers</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span> </span></span></code></pre></div><div class="highlight"><pre tabindex="0" class="chroma"><code class="language-python" data-lang="python"><span class="line"><span class="ln">1</span><span class="cl"><span class="n">data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">X</span><span class="p">)</span> </span></span><span class="line"><span class="ln">2</span><span class="cl"><span class="n">data</span><span class="p">[</span><span class="mi">4</span><span class="p">]</span> <span class="o">=</span><span class="n">y</span> </span></span></code></pre></div><div class="highlight"><pre tabindex="0" class="chroma"><code class="language-python" data-lang="python"><span class="line"><span class="ln">1</span><span class="cl"><span class="n">data</span> </span></span></code></pre></div><pre><code> 0 1 2 3 4 </code></pre> <p>0 -8.317893 0.698439 -10.349959 4.291286 0 1 -5.663915 7.060548 0.330762 4.576624 1 2 -9.276754 1.625037 -9.837227 3.494463 0 3 -4.981855 2.015353 -9.270422 4.598429 0 4 -3.461616 8.064244 4.563894 1.690651 1 .. ... ... ... ... .. 145 -5.561548 6.836946 1.549655 3.830984 1 146 -6.841547 -2.699957 -8.790234 -0.604204 2 147 -5.903813 7.357668 2.510227 3.125459 1 148 -8.607808 0.461901 -9.125664 4.522260 0 149 -8.503622 2.326839 -8.911981 3.532953 0</p> https://gb.yeadoc.com/datamining-overview/ Sat, 11 Apr 2026 19:27:54 +0000 https://gb.yeadoc.com/datamining-overview/ <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-python" data-lang="python"><span class="line"><span class="ln">1</span><span class="cl"><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span> </span></span><span class="line"><span class="ln">2</span><span class="cl"><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> </span></span><span class="line"><span class="ln">3</span><span class="cl"><span class="kn">import</span> <span class="nn">warnings</span> </span></span><span class="line"><span class="ln">4</span><span class="cl"><span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s1">&#39;ignore&#39;</span><span class="p">)</span> </span></span></code></pre></div><p>1.读取除表头外的前五行\读取数据前五行</p> <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-python" data-lang="python"><span class="line"><span class="ln">1</span><span class="cl"><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">&#39;./titanic_trains.csv&#39;</span><span class="p">,</span><span class="n">encoding</span><span class="o">=</span><span class="s1">&#39;utf8&#39;</span><span class="p">)</span> </span></span><span class="line"><span class="ln">2</span><span class="cl"><span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span> </span></span><span class="line"><span class="ln">3</span><span class="cl"><span class="n">df</span><span class="o">.</span><span class="n">shape</span> </span></span></code></pre></div><p>(891, 12)</p> <p>2.缺失值处理(没有要求就遵循80%原则)</p> <p>2.1.查看缺失值情况，输出缺失值比例、列名、缺失值数量</p> <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-python" data-lang="python"><span class="line"><span class="ln">1</span><span class="cl"><span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">df</span><span class="o">.</span><span class="n">columns</span><span class="p">:</span> </span></span><span class="line"><span class="ln">2</span><span class="cl"> <span class="n">zb</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">x</span><span class="p">]</span><span class="o">.</span><span class="n">isnull</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">/</span><span class="n">df</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">*</span><span class="mi">100</span> </span></span><span class="line"><span class="ln">3</span><span class="cl"> <span class="n">sl</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">x</span><span class="p">]</span><span class="o">.</span><span class="n">isnull</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> </span></span><span class="line"><span class="ln">4</span><span class="cl"> <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;缺失值占比为:&#39;</span><span class="p">,</span><span class="nb">round</span><span class="p">(</span><span class="n">zb</span><span class="p">,</span><span class="mi">2</span><span class="p">),</span><span class="s2">&#34;%,列名:&#34;</span><span class="p">,</span><span class="n">x</span><span class="p">,</span><span class="s1">&#39;,缺失值数量:&#39;</span><span class="p">,</span><span class="n">sl</span><span class="p">)</span> </span></span></code></pre></div><p>缺失值占比为: 0.0 %,列名: PassengerId ,缺失值数量: 0 缺失值占比为: 0.0 %,列名: Survived ,缺失值数量: 0 缺失值占比为: 0.0 %,列名: Pclass ,缺失值数量: 0 缺失值占比为: 0.0 %,列名: Name ,缺失值数量: 0 缺失值占比为: 0.0 %,列名: Sex ,缺失值数量: 0 缺失值占比为: 19.87 %,列名: Age ,缺失值数量: 177 缺失值占比为: 0.0 %,列名: SibSp ,缺失值数量: 0 缺失值占比为: 0.0 %,列名: Parch ,缺失值数量: 0 缺失值占比为: 0.0 %,列名: Ticket ,缺失值数量: 0 缺失值占比为: 0.0 %,列名: Fare ,缺失值数量: 0 缺失值占比为: 77.1 %,列名: Cabin ,缺失值数量: 687 缺失值占比为: 0.22 %,列名: Embarked ,缺失值数量: 2</p>