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‎README.md‎

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‎aboutus.html‎

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@@ -165,7 +165,7 @@ <h1 id="about-us">About Us</h1>
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<img src="./assets/imgs/JesusRC_c.jpg" width="250">
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<div class="profile-info">
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<h2>Dr. Jesús Requena Carrión</h2>
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<p class='box'><b>Reader in Data Science and Engineering</b><br>School of Electronic Engineering and Computer Science, Queen Mary University of London, UK<br>
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<p class='box'><b>Reader in Data Science and Engineering and Executive Vice-Dean</b><br>School of Electronic Engineering and Computer Science, Queen Mary University of London, UK<br>
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Email: <a href="mailto:[email protected]">[email protected]</a><br>
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</p>
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</div>
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<!-- <p class="name-title">Dr Jesús Requena Carrión</p> -->
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<p class="profile-box">
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<a href="https://uk.linkedin.com/in/jesus-requena-carrion" target="_blank"><h2>Dr. Jesús Requena Carrión</h2></a>
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<b>Reader in Data Science and Engineering</b><br />School of Electronic Engineering and Computer Science, Queen Mary University of London, UK<br />
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<b>Reader in Data Science and Engineering and Executive Vice-Dean</b><br />School of Electronic Engineering and Computer Science, Queen Mary University of London, UK<br />
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Email: <a href="mailto:[email protected]">[email protected]</a><br />
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</p>
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<!-- </div> -->
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<br />
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<br />
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<h4> PhD students supporting to MLEnd Datasets</h4>
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<h4> Teaching Fellows and Students supporting to MLEnd Datasets are: </h4>
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<hr />
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<!-- <table style="background-color:white; background-color: transparent;">
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<div class="card-body">
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<center>
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<img src="./assets/imgs/Jiayu_c.png" width="200" />
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<font color="black"><b>Jiayu Men</b></font>
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<font color="black"><b>Yiayu Men</b></font>
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</center>
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</div>
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‎gallery/index.html‎

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</div>
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</div> -->
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<p><br /></p>
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<hr />
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<h3 id="mlend-documentation">MLEnd Documentation</h3>
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<p>For mlend documentation use <code>help(fun)</code> in python terminal or Jupyter-notebook. Alternately, check out</p>
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<h4><a href="https://mlend.readthedocs.io/" target="_blank">MLEnd Documentation</a></h4>
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<p><br /></p>
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<!-- </article> -->
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</div>
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</div>

‎happiness/index.html‎

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<center>
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<img src="/assets/imgs/square_r_4.png" width="100" align="left" />
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<h1> MLEnd Happiness </h1>
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<h3>...</h3>
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<h3> Happiness with demographic</h3>
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</center>
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<div class="divider-10"></div>
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<hr />
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<div class="alert alert-primary" role="alert">
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<!-- <div class="alert alert-primary" role=alert>
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<h3>Will be updated soon ...</h3>
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</div>
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</div> -->
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<p><img src="/assets/imgs/happiness_cover.png" width="30%" align="right" /></p>
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<div class="section" id="projects-list">
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<div class="container">
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<h3 style="font-weight:600; font-family: sans-serif;"> About Dataset <div style="float:right"><span></span></div>
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</h3> <br style="line-height:200%;" />
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</div>
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</div>
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<h3 style="font-weight:600; font-family: sans-serif;"> Sample of data <div style="float:right"><span></span></div>
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<article class="markdown-body entry-content" itemprop="text">
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<div style="font-size:70%;margin-left:-100px;">
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<!-- <font size=2> -->
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<table border="1">
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<thead>
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<tr style="text-align: center;">
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<th>Age</th>
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<th>Height</th>
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<th>Weight</th>
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<th>EducationLevel</th>
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<th>NumberOfLanguages</th>
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<th>LikeVollyball</th>
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<th>LikeTableTennis</th>
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<th>FavouriteMovie</th>
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<th>Province</th>
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<th>HappinessLevel</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td>48.0</td>
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<td>162.0</td>
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<td>61.0</td>
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<td>Undergraduate Degree (UG)</td>
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<td>3</td>
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<td>False</td>
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<td>True</td>
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<td>The Lord of the Rings</td>
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<td>Henan</td>
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<td>9.0</td>
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</tr>
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<tr>
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<td>48.0</td>
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<td>172.0</td>
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<td>65.0</td>
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<td>Postgraduate/ Master's Degree (PG)</td>
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<td>3</td>
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<td>True</td>
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<td>True</td>
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<td>Wolf Warriors</td>
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<td>Henan</td>
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<td>10.0</td>
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</tr>
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<tr>
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<td>19.0</td>
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<td>169.0</td>
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<td>61.0</td>
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<td>Undergraduate Degree (UG)</td>
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<td>2</td>
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<td>True</td>
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<td>False</td>
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<td>Guardians of the Galaxy</td>
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<td>Henan</td>
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<td>9.0</td>
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</tr>
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<tr>
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<td>18.0</td>
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<td>176.0</td>
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<td>95.0</td>
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<td>Undergraduate Degree (UG)</td>
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<td>2</td>
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<td>True</td>
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<td>True</td>
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<td>Fast &amp; Furious</td>
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<td>Liaoning</td>
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<td>8.0</td>
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</tr>
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<tr>
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<td>18.0</td>
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<td>168.0</td>
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<td>52.0</td>
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<td>Undergraduate Degree (UG)</td>
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<td>2</td>
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<td>False</td>
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<td>False</td>
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<td>The Wandering Earth</td>
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<td>Tianjin</td>
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<td>8.0</td>
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</tr>
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</tbody>
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</table>
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<!-- </font> -->
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</div>
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</article>
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<hr />
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<h3 style="font-weight:600; font-family: sans-serif;"> Download Data</h3>
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<h4 style="font-weight:600; font-family: sans-serif;"> Install mlend (&gt;1.0.0.2)</h4>
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To download the Yummy dataset, first step is to install <a href="https://pypi.org/project/mlend" target="_blank"><code>mlend</code></a> library. Use pip to install library.
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<br />
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<br />
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<pre><code>pip install mlend</code></pre>
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<br />
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<br />
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<h4 style="font-weight:600; font-family: sans-serif;"> Download dataset</h4>
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<br />
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To download happiness dataset make sure to updgrade <code>mlend</code> library to <code>version&gt;1.0.0.2</code>
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<br />
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<pre><code>pip install mlend --upgrade</code></pre>
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#### To download dataset, use following piece of code
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<pre><code class="python">import mlend
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from mlend import download_happiness, download_load_happiness, happiness_load
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datadir = download_happiness(save_to = '../MLEnd')
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</code></pre>
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It will download a CSV file in folder <code>../MLEnd/happiness</code> directory
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<br />
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<br />
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<h4 style="font-weight:600; font-family: sans-serif;"> Read dataset</h4>
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After downloading, to load dataset use following piece of code
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<pre><code class="python">import mlend
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from mlend import download_happiness, download_load_happiness, happiness_load
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datadir = download_happiness(save_to = '../MLEnd')
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D = happiness_load(datadir)
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</code></pre>
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<h4 style="font-weight:600; font-family: sans-serif;"> Download and read dataset</h4>
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<br />
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Alternately, use following piece of code to download and load data with one line
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<pre><code class="python">import mlend
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from mlend import download_load_happiness
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D = download_load_happiness()
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D.head()
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</code></pre>
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<br />
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<hr />
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### MLEnd Documentation
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For mlend documentation use <code>help(fun)</code> in python terminal or Jupyter-notebook. Alternately, check out
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<h4><a href="https://mlend.readthedocs.io/" target="_blank">MLEnd Documentation</a></h4>
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<h4>Todo</h4>
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<br />
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</h3>
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‎hums_whistles/index.html‎

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<div class="section" id="projects-list">
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<h3 style="font-weight:600; font-family: sans-serif;"> About the dataset <div style="float:right"><span></span></div>
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<h3 style="font-weight:600; font-family: sans-serif;"> About Dataset <div style="float:right"><span></span></div>
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</h3> <br style="line-height:200%;" />
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The MLEnd Hums and Whistles dataset will give you an opportunity to explore the non-trivial problem of
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recognizing music from extreme interpretations, in our case, hums and whistles produced by people like
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you and me. This dataset comes with additional demographic information about our participants, so that
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you can explore how people with different backgrounds interpret music. The MLEnd datasets have been
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created by students at the School of Electronic Engineering and Computer Science, Queen Mary University of London.
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</font>
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The MLEnd Hums and Whistles dataset will give you an opportunity to explore the non-trivial problem of recognizing music from extreme interpretations, in our case, hums and whistles produced by people like you and me. This dataset comes with additional demographic information about our participants, so that you can explore how people with different backgrounds interpret music. A small version of this dataset can be found here.
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</font>
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<br />
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The MLEnd datasets have been created by students at the School of Electronic Engineering and Computer Science, Queen Mary University of London. Other datasets include the MLEnd Spoken Numerals dataset, also available on Kaggle. Do not hesitate to reach out if you want to know more about how we did it.
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</font><font size="4">
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<br />
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<br />
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Enjoy!
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</code></pre>
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<p><br /></p>
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<hr />
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<h3 id="mlend-documentation">MLEnd Documentation</h3>
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<p>For mlend documentation use <code>help(fun)</code> in python terminal or Jupyter-notebook. Alternately, check out</p>
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<h4><a href="https://mlend.readthedocs.io/" target="_blank">MLEnd Documentation</a></h4>
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<p><br /></p>
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‎index.html‎

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<h3><b>Welcome to MLEnd Datasets!</b></h4>
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<p >The MLEnd Datasets is a collection of datasets acquired during the modules <i>Principles of Machine Learning</i> and <i>Introduction to Data Science Programming</i>
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led by the Data Science and AI Teaching Group at Queen Mary Univeristy of London.
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Following collaborative crowdsourcing approaches, students participate in the processes of data collection and curation. Then, they formulate and solve problems using the dataset that they have co-created.
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This unique learning experience builds upon a deployment-first perspective of machine learning and constructivist principles and allows our students to
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gain a deeper understanding of the significance of data collection and curation in machine learning.
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This unique learning experience builds upon a deployment-first perspective of machine learning and constructivism and allows our students to gain first-hand a deeper understanding of the significance of data collection and curation.
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</p>
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<h4><b>Welcome to MLEnd Datasets!</b></h4>
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The MLEnd Datasets are a collection of datasets acquired during the modules <i>Principles of Machine Learning</i> and <i>Introduction to Data Science Programming</i>,
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led by the Data Science and AI Teaching Group at Queen Mary Univeristy of London.
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<br>
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<br>
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Following collaborative crowdsourcing approaches, students participate in the processes of data collection and curation.
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Then, they formulate and solve problems using the new dataset that they have co-created.
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This unique learning experience builds upon constructivism and a deployment-first perspective of machine learning,
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and allows our students to gain a deeper understanding of the significance of data collection and curation.
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<h3><b>Welcome to MLEnd Datasets!</b></h3>
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The MLEnd Datasets are a collection of datasets acquired during the modules <i>Principles of Machine Learning</i> and <i>Introduction to Data Science Programming</i>,
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led by the Data Science and AI Teaching Group at Queen Mary Univeristy of London.
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<br>
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<br>
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Following collaborative crowdsourcing approaches, students participate in the processes of data collection and curation.
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Then, they formulate and solve problems using the new dataset that they have co-created.
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This unique learning experience builds upon constructivism and a deployment-first perspective of machine learning,
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and allows our students to gain a deeper understanding of the significance of data collection and curation.
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<!-- </p> -->
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<div class="card-body"> <img src="./assets/imgs/yummy_cover.png">
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<b>Enriched food images</b>
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<ul class="list-unstyled mt-3 mb-4">
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<li>• 3K images </li>
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<li>• 200 participants </li>
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<li>• 3K images</li>
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<li>• 200 participats</li>
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<li>• Participants' preferences and assessment</li>
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</ul>
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‎london_sounds/index.html‎

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<center>
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<img src="/assets/imgs/square_o_3.png" width="100" align="left" />
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<h1> MLEnd London Sounds </h1>
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<h3>A dataset for acoustic scence recognition</h3>
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<h3>A dataset for acoustic scence</h3>
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<h3 style="font-weight:600; font-family: sans-serif;"> About Dataset <div style="float:right"><span></span></div>
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The MLEnd datasets have been created by students at the School of Electronic Engineering and Computer Science, Queen Mary University of London.
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The MLEnd datasets have been created by students at the School of Electronic Engineering and Computer Science, Queen Mary University of London. Other datasets include the MLEnd Spoken Numerals and the MLEnd Hums and Whistles datasets, also available on Kaggle. Do not hesitate to reach out if you want to know more about how we did it.
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<p><br /></p>
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<h3 id="mlend-documentation">MLEnd Documentation</h3>
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<p>For mlend documentation use <code>help(fun)</code> in python terminal or Jupyter-notebook. Alternately, check out</p>
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<h4><a href="https://mlend.readthedocs.io/" target="_blank">MLEnd Documentation</a></h4>
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<p><br /></p>
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<!-- </article> -->
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