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  "source_url": "https://www.kaggle.com/competitions/ctc-34-concrete-compressive-strength-prediction/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": "Esses são os dados da competição:\n- training.csv: conjunto para treinamento da evolução gramatical\n- testing.csv: conjunto de teste\n- sample.csv: exemplo de como deve ser formatada uma submissão (essa submissão contém apenas a mediana de valores a serem previstos)",
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      "content": "**Esse conjunto de dados está no repositório UCI (University of California Irvine)**\n\nAbstract: Concrete is the most important material in civil engineering. The \nconcrete compressive strength is a highly nonlinear function of age and \ningredients. These ingredients include cement, blast furnace slag, fly ash, \nwater, superplasticizer, coarse aggregate, and fine aggregate.\n\n---------------------------------\n\nSources: \n\n  Original Owner and Donor\n  Prof. I-Cheng Yeh\n  Department of Information Management \n  Chung-Hua University, \n  Hsin Chu, Taiwan 30067, R.O.C.\n  e-mail:icyeh@chu.edu.tw\n  TEL:886-3-5186511\n\n  Date Donated: August 3, 2007\n \n---------------------------------\n\nData Characteristics:\n    \nThe actual concrete compressive strength (MPa) for a given mixture under a \nspecific age (days) was determined from laboratory. Data is in raw form (not scaled). \n\nSummary Statistics: \n\nNumber of Attributes: 9\nAttribute breakdown: 8 quantitative input variables, and 1 quantitative output variable\n\n---------------------------------\n\nVariable Information:\n\nGiven is the variable name, variable type, the measurement unit and a brief description. \nThe concrete compressive strength is the regression problem. The order of this listing \ncorresponds to the order of numerals along the rows of the database. \n\nName -- Data Type -- Measurement -- Description\n\nCement (component 1) -- quantitative -- kg in a m3 mixture -- Input Variable\nBlast Furnace Slag (component 2) -- quantitative -- kg in a m3 mixture -- Input Variable\nFly Ash (component 3) -- quantitative -- kg in a m3 mixture -- Input Variable\nWater (component 4) -- quantitative -- kg in a m3 mixture -- Input Variable\nSuperplasticizer (component 5) -- quantitative -- kg in a m3 mixture -- Input Variable\nCoarse Aggregate (component 6) -- quantitative -- kg in a m3 mixture -- Input Variable\nFine Aggregate (component 7) -- quantitative -- kg in a m3 mixture -- Input Variable\nAge -- quantitative -- Day (1~365) -- Input Variable\nConcrete compressive strength -- quantitative -- MPa -- Output Variable \n---------------------------------\n\nPast Usage: \n\nMain\n1. I-Cheng Yeh, \"Modeling of strength of high performance concrete using artificial \nneural networks,\" Cement and Concrete Research, Vol. 28, No. 12, pp. 1797-1808 (1998).\n\nOthers\n2. I-Cheng Yeh, \"Modeling Concrete Strength with Augment-Neuron Networks,\" J. of \nMaterials in Civil Engineering, ASCE, Vol. 10, No. 4, pp. 263-268 (1998).\n\n3. I-Cheng Yeh, \"Design of High Performance Concrete Mixture Using Neural Networks,\"  \nJ. of Computing in Civil Engineering, ASCE, Vol. 13, No. 1, pp. 36-42 (1999).\n\n4. I-Cheng Yeh, \"Prediction of Strength of Fly Ash and Slag Concrete By The Use of \nArtificial Neural Networks,\" Journal of the Chinese Institute of Civil and Hydraulic \nEngineering, Vol. 15, No. 4, pp. 659-663 (2003).\n\n5. I-Cheng Yeh, \"A mix Proportioning Methodology for Fly Ash and Slag Concrete Using \nArtificial Neural Networks,\" Chung Hua Journal of Science and Engineering, Vol. 1, No. \n1, pp. 77-84 (2003).\n\n6. Yeh, I-Cheng, \"Analysis of strength of concrete using design of experiments and \nneural networks,\": Journal of Materials in Civil Engineering, ASCE, Vol.18, No.4, \npp.597-604 ?2006?.\n\n---------------------------------\n\nAcknowledgements, Copyright Information, and Availability:\n\nNOTE: Reuse of this database is unlimited with retention of copyright notice for \nProf. I-Cheng Yeh and the following published paper:\n\nI-Cheng Yeh, \"Modeling of strength of high performance concrete using artificial \nneural networks,\" Cement and Concrete Research, Vol. 28, No. 12, pp. 1797-1808 (1998)\n\n\n\n",
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      "content": "A medida de avaliação é a raiz do erro quadrático médio entre as previsões e a solução desejada.",
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