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<!DOCTYPE html>
<html ng-app = "input_bar">
<head>
<meta http-equiv="Content-Type" content="text/html;charset=utf-8"> <!-- Specifies the Encoding of the Page -->
<title>Heart Disease Classification Results Visualization</title>
<!--<script src="min_ml_summary.js"></script>-->
<script src="ml_summary.js"></script>
<script src="https://ajax.googleapis.com/ajax/libs/angularjs/1.6.7/angular.min.js"></script>
<script src="https://d3js.org/d3.v4.min.js"></script>
<link rel="stylesheet" href="style.css" > <!-- Refers to the style sheet file located in thi-->
</head>
<body>
<script src="app.js"> </script>
<div ng-controller= "Plotter as Plotter">
<h1>D3 Plot</h1>
<div class="chart"></div>
<div class = "misc"></div>
<!-- Accepting User Input -->
<form name="user_input"
ng-controller="Inputer as Inputer"
ng-submit="Inputer.addInput(Plotter)">
<!--TODO move all this logic into the javacript using angular repeats and expressions and such-->
<label for="x_ax_in">X Axis</label>
<select ng-model="Inputer.x_data_name" id="x_ax_in" selected="mean_train_score">
<option value="mean_test_score">Test Score Mean</option>
<option value="std_test_score">Test Score Sigma</option>
<option value="mean_train_score">Train Score Mean</option>
<option value="std_train_score">Train Score Sigma</option>
<option value="sigma_low_test_score">Test Sigma-Low Score</option>
<option value="sigma_low_train_score">Train Sigma-Low Score</option>
</select>
<label for="y_ax_in">Y Axis</label>
<select ng-model="Inputer.y_data_name" id="y_ax_in" selected="mean_test_score">
<option value="mean_test_score">Test Score Mean</option>
<option value="std_test_score">Test Score Sigma</option>
<option value="mean_train_score">Train Score Mean</option>
<option value="std_train_score">Train Score Sigma</option>
<option value="sigma_low_test_score">Test Sigma-Low Score</option>
<option value="sigma_low_train_score">Train Sigma-Low Score</option>
</select>
<label for="c_ax_in">Color By</label>
<select ng-model="Inputer.c_data_name" id="c_ax_in" selected="algorithum">
<option value="algorithum">Algorithum</option>
<option value="svc_kernal">SVC Kernal</option>
<option value="gm_covariance_type">GM Covariance Type</option>
<option value="pca_n">PCA N Features</option>
</select>
<input type="submit" value="Submit" />
</form>
<div class="Description">
<h1>Description</h1>
<p>This is a demo visualization I built while teaching myself the D3 library</p>
<p>The data I'm plotting comes from an experiment I did where I applied machine learning to the problem of identifying heart disease from a series of physiological measurements. In that experiment I played with Perceptrons, Gaussian Mixtures, and Support Vector Classifiers, as well as various pre-processing techniques like PCA. The physiological data comes from a simple open dataset. </p>
<p>In total 3081 Models were trained, 25 times each for cross validation. Statistics on performance of each of these 3081 models across the 25 cross validation runs are displayed above.</p>
<p>My python code for the machine learning is available <a href =https://github.com/TychonautVII/heart_disease_dm>here</a></p>
<p>The javascript code for this visualization is available <a href=https://github.com/TychonautVII/learn_d3>here</a></p>
<p>Data Available at the UCI machine learning repository <a href =https://archive.ics.uci.edu/ml/datasets/Heart+Disease >here</a></p>
<p>Find me on LinkedIn <a href="https://www.linkedin.com/in/peter-mariani-84860063/">here</a></p>
</div>
</div>
</body>
</html>