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Changes made at Student Intervention according to review.
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‎projects/student_intervention/.ipynb_checkpoints/student_intervention-checkpoint.ipynb‎

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@@ -47,7 +47,7 @@
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},
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{
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"cell_type": "code",
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"[5 rows x 31 columns]"
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]
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}
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{
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"cell_type": "code",
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"output_type": "stream",
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"text": [
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"Total number of students: 395\n",
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"Number of features: 31\n",
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"Number of features: 30\n",
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"Number of students who passed: 265\n",
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"Number of students who failed: 130\n",
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"Graduation rate of the class: 67.09%\n"
@@ -332,7 +332,7 @@
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"n_students = student_data.shape[0]\n",
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"\n",
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"# TODO: Calculate number of features\n",
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"n_features = student_data.shape[1]\n",
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"n_features = student_data.shape[1] - 1\n",
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"\n",
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"# TODO: Calculate passing students\n",
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"n_passed = student_data[\"passed\"].value_counts()[\"yes\"]\n",
@@ -580,7 +580,7 @@
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"\n",
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"** 2. Support Vector Machine :** \n",
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"\n",
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"Support vector machines classify data by finding the maximum margin hyperplane that seperates class labels, it's also a very popular model like the other two, decision trees and K-nearest neighbors and used in industry for classification and regression tasks.\n",
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"Support vector machines classify data by finding the maximum margin hyperplane that seperates class labels, it's also a very popular model like the other two, decision trees and K-nearest neighbors and used in industry for classification and regression tasks. Support Vector Machines have been successfully used on high dimensional data such as genetic data(protein structure prediction), music(song genre classification, music retrival), image classification(histogram based), image retrieval etc.\n",
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"\n",
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"* Strengths :\n",
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" 1. As Support Vector Machine tries to find the seperator hyperplane that has the maximum distance between the seperate classes, it's not prone to overfitting.\n",

‎projects/student_intervention/student_intervention.html‎

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@@ -226,7 +226,7 @@ <h2 id="Exploring-the-Data">Exploring the Data<a class="anchor-link" href="#Expl
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<div class="cell border-box-sizing code_cell rendered">
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<div class="prompt input_prompt">In&nbsp;[1]:</div>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Import libraries</span>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="n">student_data</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># TODO: Calculate number of students</span>
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<span class="n">n_students</span> <span class="o">=</span> <span class="n">student_data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
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<span class="c1"># TODO: Calculate number of features</span>
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<span class="n">n_features</span> <span class="o">=</span> <span class="n">student_data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
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<span class="n">n_features</span> <span class="o">=</span> <span class="n">student_data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="mi">1</span>
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<span class="c1"># TODO: Calculate passing students</span>
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<span class="n">n_passed</span> <span class="o">=</span> <span class="n">student_data</span><span class="p">[</span><span class="s2">&quot;passed&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()[</span><span class="s2">&quot;yes&quot;</span><span class="p">]</span>
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<pre>Total number of students: 395
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Number of features: 31
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Number of features: 30
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Number of students who passed: 265
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Number of students who failed: 130
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Graduation rate of the class: 67.09%
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</ul>
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<p><strong> 2. Support Vector Machine :</strong></p>
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<p>Support vector machines classify data by finding the maximum margin hyperplane that seperates class labels, it's also a very popular model like the other two, decision trees and K-nearest neighbors and used in industry for classification and regression tasks.</p>
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<p>Support vector machines classify data by finding the maximum margin hyperplane that seperates class labels, it's also a very popular model like the other two, decision trees and K-nearest neighbors and used in industry for classification and regression tasks. Support Vector Machines have been successfully used on high dimensional data such as genetic data(protein structure prediction), music(song genre classification, music retrival), image classification(histogram based), image retrieval etc.</p>
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<ul>
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<li><p>Strengths :</p>
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<ol>

‎projects/student_intervention/student_intervention.ipynb‎

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{
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"[5 rows x 31 columns]"
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"output_type": "stream",
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"text": [
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"Total number of students: 395\n",
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"Number of features: 31\n",
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"Number of features: 30\n",
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"Number of students who passed: 265\n",
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"Number of students who failed: 130\n",
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"Graduation rate of the class: 67.09%\n"
@@ -332,7 +332,7 @@
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"n_students = student_data.shape[0]\n",
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"\n",
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"# TODO: Calculate number of features\n",
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"n_features = student_data.shape[1]\n",
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"n_features = student_data.shape[1] - 1\n",
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"\n",
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"# TODO: Calculate passing students\n",
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"n_passed = student_data[\"passed\"].value_counts()[\"yes\"]\n",
@@ -580,7 +580,7 @@
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"\n",
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"** 2. Support Vector Machine :** \n",
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"\n",
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"Support vector machines classify data by finding the maximum margin hyperplane that seperates class labels, it's also a very popular model like the other two, decision trees and K-nearest neighbors and used in industry for classification and regression tasks.\n",
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"Support vector machines classify data by finding the maximum margin hyperplane that seperates class labels, it's also a very popular model like the other two, decision trees and K-nearest neighbors and used in industry for classification and regression tasks. Support Vector Machines have been successfully used on high dimensional data such as genetic data(protein structure prediction), music(song genre classification, music retrival), image classification(histogram based), image retrieval etc.\n",
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"\n",
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"* Strengths :\n",
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" 1. As Support Vector Machine tries to find the seperator hyperplane that has the maximum distance between the seperate classes, it's not prone to overfitting.\n",

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