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Gdelt Global News Sentiment Analysis and Visualization
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<p>Analyze global news average sentiment from GDELT Global Knowledge Public Dataset in Google Big Query</p>
<p>100,000,000 global news from 2016.1-2017.2. Attributes include website domain, news’ sentiment, countries mentioned in the news.</p>
<p>Sentiments toward other countries are calculated based on the countries mentioned in the news and the normalized average sentiment.</p>
<p>The analysis is done in both Pandas and Spark.</p>
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MNIST using LeNet-5 CNN
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<h3 style="text-align:center"> LeNet-5 CNN Structure</h3><br><p></p><br><img src="/2017/03/07/MNIST%20LeNet-5/LeNet-5-structure.png" alt="LeNet-5-structure.png" title=""><br><br><p></p><br><p></p><br><br><br><br>This is a codelab for LeNet-5 CNN. Clustering MNIST data in latent space using variational autoencoder.<br><br>Details include:<br> - Pre-process dataset<br> - Elaborate recipes<br> - Define training procedures<br> - Train and test models<br> - Observe metrics<br><br>Functionalityies of convolutional layers:<br> - Capture patterns<br> - Share parameters (otherwise too many parameters)<br>——————————————<br><br><br>### Index<br>1. <a href="#Pre-processing">Pre-processing</a><br>2. <a href="#Variational-Autoencoder-Clustering">Variational Autoencoder Clustering</a><br>3. <a href="#Recipes-Exclusively-for-Convolutional-Neural-Nets">Recipes for ConvNet</a><br> 1. <a href="#Network-Structure">Network Structure</a><br> 2. <a href="#Pooling">Pooling</a><br>4. <a href="#Define-Training-Procedure">Define Training Procedure</a><br>5. <a href="#Start-Training">Start Training</a> and <a href="#Observe-Training-Process">Observe Training Process</a><br>6. <a href="#Define-Testing-Procedure">Testing</a><br><br>
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NYC Cab Trip Record Data Flying Line Visualization
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<p>Flying line visualiazation of NYC public trip record data based on the pick and drop Geojson Information</p>
<p>The analysis is doen by D3.js and Canvas</p>
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Credit Modelling using Financial Lending Data from Lending Club
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<h2 id="Credit-modelling-using-linancial-lending-data-from-Lending-Club"><a href="#Credit-modelling-using-linancial-lending-data-from-Lending-Club" class="headerlink" title="Credit modelling using linancial lending data from Lending Club"></a>Credit modelling using linancial lending data from Lending Club</h2><p>Modeling Credit status by making predictions about whether or not a loan will be paid off on time, which is contained in the <em>loan_status</em> column of the clean dataset.</p>
<p>40000 approved loan data with loan status attributes from 2017 to 2011 are analyzed in the project.</p>
<p>Using False Positive Rate(fpr) and Accuracy as error metric. With a false positive, we predict that a loan will be paid off on time, but it actually isn’t. This costs us money. Our objective in this is to make money – we want to fund enough loans that are paid off on time to offset our losses from loans that aren’t paid.</p>
<p>Prediction Goal: Minimize the fpr results to reduce the risk; Maximize the Accuracy to maximize the potential money.</p>
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Soccer Game prediction using Deep learning
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<h2 id="Soccer-Result-Prediction"><a href="#Soccer-Result-Prediction" class="headerlink" title="Soccer Result Prediction"></a>Soccer Result Prediction</h2><p>Predict 2008-2016 soccer game results(Win/Draw/Lose) of 11 European lead league based on the 25K+ match result Kaggle datase <a href="https://www.kaggle.com/hugomathien/soccer">here</a>. The prediction is based on the bet odds and the FIFA game team attribute.</p>
<p>*+25,000 matches</p>
<p>*+10,000 players</p>
<p>*11 European Countries with their lead championship</p>
<p>*Seasons 2008 to 2016</p>
<p><em>Players and Teams’ attributes</em> sourced from EA Sports’ FIFA video game series, including the weekly updates</p>
<h2 id="Result"><a href="#Result" class="headerlink" title="Result"></a>Result</h2><p>The Deep FNN gives us 53.86% Accuracy in test set and 53.12% accuracy in validation set.<br>The Random Forest gives us 50.42% Accuracy in test set and 50.33% accuracy in validation set.</p>
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