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  "source_url": "https://www.kaggle.com/competitions/dat31-shuttle/data",
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      "content": "This is a page where you can include rules that participants must accept before joining. You may wish to include rules like:\n\n - Don't cheat!\n - Apply yourself!\n - Have fun!",
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      "content": "The data originated from NASA and concern the position of radiators within the Space Shuttle.\n\n## File descriptions\n<ul>\n<li><strong>train.csv</strong> - the training set</li>\n<li><strong>test.csv</strong> - the test set</li>\n<li><strong>sample_submission.csv</strong> - a sample submission file in the correct format</li>\n\n</ul>\n\n<h2>Data fields</h2>\nThe shuttle dataset contains 9 attributes all of which are numerical. \nApproximately 80% of the data belongs to class 1\n\nSource:\nDonor: Jason Catlett Basser Department of Computer Science, University of Sydney, N.S.W., Australia\n\nData Set Information:\nApproximately 80% of the data belongs to class 1. Therefore the default accuracy is about 80%. The aim here is to obtain an accuracy of 99 - 99.9%. The examples in the original dataset were in time order, and this time order could presumably be relevant in classification. However, this was not deemed relevant for StatLog purposes, so the order of the examples in the original dataset was randomised, and a portion of the original dataset removed for validation purposes.\n\nAttribute Information:\nThe shuttle dataset contains 9 attributes all of which are numerical. The first one being time. The last column is the class which has been coded as follows : 1 Rad Flow 2 Fpv Close 3 Fpv Open 4 High 5 Bypass 6 Bpv Close 7 Bpv Open\n\nRelevant Papers:\nN/A\n\nPapers That Cite This Data Set1:\nIra Cohen and Fabio Gagliardi Cozman and Nicu Sebe and Marcelo Cesar Cirelo and Thomas S. Huang. Semisupervised Learning of Classifiers: Theory, Algorithms, and Their Application to Human-Computer Interaction. IEEE Trans. Pattern Anal. Mach. Intell, 26. 2004.\n\nGrigorios Tsoumakas and Ioannis P. Vlahavas. Effective Stacking of Distributed Classifiers. ECAI. 2002.\n\nJun Wang and Bin Yu and Les Gasser. Concept Tree Based Clustering Visualization with Shaded Similarity Matrices. ICDM. 2002.\n\nRichard Nock. Inducing Interpretable Voting Classifiers without Trading Accuracy for Simplicity: Theoretical Results, Approximation Algorithms, and Experiments. J. Artif. Intell. Res. JAIR, 17. 2002. \n\nStephen D. Bay. Multivariate Discretization for Set Mining. Knowl. Inf. Syst, 3. 2001.\n\nJochen Garcke and Michael Griebel and Michael Thess. Data Mining with Sparse Grids. Computing, 67. 2001.\n\nHaixun Wang and Carlo Zaniolo. CMP: A Fast Decision Tree Classifier Using Multivariate Predictions. ICDE. 2000.\n\nKhaled A. Alsabti and Sanjay Ranka and Vineet Singh. CLOUDS: A Decision Tree Classifier for Large Datasets. KDD. 1998.\n\nPedro Domingos. Linear-Time Rule Induction. KDD. 1996.\n\nNir Friedman and Moisés Goldszmidt. Discretizing Continuous Attributes While Learning Bayesian Networks. ICML. 1996.\n\nRon Kohavi. Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid. KDD. 1996.\n\nRon Kohavi. A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection. IJCAI. 1995.\n\nRon Kohavi. The Power of Decision Tables. ECML. 1995.\n\nKrzysztof Grabczewski and Wl/odzisl/aw Duch. THE SEPARABILITY OF SPLIT VALUE CRITERION. Department of Computer Methods, Nicolaus Copernicus University.\n\nMohammed Waleed Kadous and Claude Sammut. The University of New South Wales School of Computer Science and Engineering Temporal Classification: Extending the Classification Paradigm to Multivariate Time Series.\n\nAdil M. Bagirov and Julien Ugon. An algorithm for computation of piecewise linear function separating two sets. CIAO, School of Information Technology and Mathematical Sciences, The University of Ballarat.\n\nRon Kohavi and George H. John. Automatic Parameter Selection by Minimizing Estimated Error. Computer Science Dept. Stanford University.\n\nWl odzisl/aw Duch and Rudy Setiono and Jacek M. Zurada. Computational intelligence methods for rule-based data understanding.\n\nChris Giannella and Bassem Sayrafi. An Information Theoretic Histogram for Single Dimensional Selectivity Estimation. Department of Computer Science, Indiana University Bloomington.\n\nChristophe Giraud and Tony Martinez. ADYNAMIC INCREMENTAL NETWORK THAT LEARNS BY DISCRIMINATION. AA.\nWl odzisl and Rafal Adamczak and Krzysztof Grabczewski. Optimization of Logical Rules Derived by Neural Procedures. Department of Computer Methods, Nicholas Copernicus University.\n\nChih-Wei Hsu and Cheng-Ru Lin. A Comparison of Methods for Multi-class Support Vector Machines. Department of Computer Science and Information Engineering National Taiwan University.\n\nJeffrey P. Bradford and Clayton Kunz and Ron Kohavi and Clifford Brunk and Carla Brodley. Appears in ECML-98 as a research note Pruning Decision Trees with Misclassification Costs. School of Electrical Engineering.\n\nJun Wang. Classification Visualization with Shaded Similarity Matrix. Bei Yu Les Gasser Graduate School of Library and Information Science University of Illinois at Urbana-Champaign.\n\n### Citation Request:\nThanks to NASA for allowing us to use the shuttle datasets.[1] Papers were automatically harvested and associated with this data set, in collaborationwith Rexa.info\n\n*Source*: [http://archive.ics.uci.edu/ml/datasets/Statlog+(Shuttle)](http://archive.ics.uci.edu/ml/datasets/Statlog+(Shuttle))",
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      "content": "<p>Welcome to General Assembly DC's 31st Part Time Data Science Competition. Tonight, you'll be trying to hack away at Shuttle Radiator data trying to classify potential anomalies in the radiator position onboard Space Shuttles. </p>\n\n### NUMBER OF EXAMPLES\n\ttraining set     43500\n\ttest set         14500\n\n### NUMBER OF ATTRIBUTES\n\t9\n\nThe shuttle dataset contains 9 attributes all of which are numerical.\nThe first one being time.  The last column is the class which has been \ncoded as follows :\n        1       Rad Flow\n        2       Fpv Close\n        3       Fpv Open\n        4       High\n        5       Bypass\n        6       Bpv Close\n        7       Bpv Open\n        \nApproximately 80% of the data belongs to class 1. Therefore the default accuracy is about 80%. The aim here is to obtain an accuracy of \n99 - 99.9%.   Our evaluation metric is accuracy for this competition.   \n\n<h2>Acknowledgements</h2>\n<p>We thank NASA and UCI for providing this dataset.</p>",
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