{"cells":[{"cell_type":"markdown","metadata":{"_cell_guid":"acc932a5-f680-4730-1a90-3e89bb923d32"},"source":""},{"cell_type":"markdown","metadata":{"_cell_guid":"21973b04-b61c-4d4d-8a26-b4b9fc2c205d"},"source":""},{"cell_type":"markdown","metadata":{"_cell_guid":"b53d814d-deab-4f6e-9d09-abcd83bc3a29"},"source":""},{"cell_type":"markdown","metadata":{"_cell_guid":"700f5718-48a3-45f6-8cb1-f38e50d82cd3"},"source":""},{"cell_type":"markdown","metadata":{"_cell_guid":"78dc909a-4861-4d06-88fd-a554d55a15a5"},"source":""},{"cell_type":"markdown","metadata":{"_cell_guid":"fecdc4d6-7c67-437a-a7fe-407a05ca7213"},"source":""},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"16319c8f-cf4b-206b-4d25-54bd7cb16879"},"outputs":[],"source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load in \n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n\nfrom subprocess import check_output\nprint(check_output([\"ls\", \"../input/TrainDotted\"]).decode(\"utf8\"))\n\n# Any results you write to the current directory are saved as output."},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"db69e140-1f50-2b05-57a0-c4ac1cad0e97"},"outputs":[],"source":""},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"fd44e443-be20-0cac-be7f-ae21fab679bb"},"outputs":[],"source":""},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"6a9ed9ef-434c-48d9-99e1-8eae236274da"},"outputs":[],"source":"# This R environment comes with all of CRAN preinstalled, as well as many other helpful packages\n# The environment is defined by the kaggle/rstats docker image: https://github.com/kaggle/docker-rstats\n# For example, here's several helpful packages to load in \n\nlibrary(ggplot2) # Data visualization\nlibrary(readr) # CSV file I/O, e.g. the read_csv function\n\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n\nsystem(\"ls ../input\")\n\n# Any results you write to the current directory are saved as output."},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"64a9e8b4-ee90-4c27-97bf-5cc79bced4cd"},"outputs":[],"source":""},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"dfd971b1-1307-4d24-bac3-bf5ab30ec1d3"},"outputs":[],"source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load in \n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n\nfrom subprocess import check_output\nprint(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n\n# Any results you write to the current directory are saved as output."},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"2686e5ca-828e-40dd-a235-528247f51bef"},"outputs":[],"source":""},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"590e54e8-0add-45dd-aeb2-1014e2e0c18b"},"outputs":[],"source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load in \n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n\nfrom subprocess import check_output\nprint(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n\n# Any results you write to the current directory are saved as output."},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"488b31f6-7270-effa-1fcb-ce0a56edd5e9"},"outputs":[],"source":""},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"36b869fb-819e-40ce-9029-c377b1891f1e"},"outputs":[],"source":""},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"fd6c4972-68b0-44f4-9e7d-29cf5410c08c"},"outputs":[],"source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load in \n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n\nfrom subprocess import check_output\nprint(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n\n# Any results you write to the current directory are saved as output."},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"e1798027-df63-4b43-be1b-9749625d8db0"},"outputs":[],"source":""},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"881e30d1-9bbd-4711-b440-57e442963242"},"outputs":[],"source":""},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"fbcbf0ad-b41f-4d97-8411-2d59453bd21c"},"outputs":[],"source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load in \n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n\nfrom subprocess import check_output\nprint(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n\n# Any results you write to the current directory are saved as output."},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"167ffa4a-ba24-4990-96e1-739b5bda5aac"},"outputs":[],"source":""},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"0a6ac007-f06c-4632-a01b-96fb1f6a3815"},"outputs":[],"source":""},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"1ca4a89d-f75d-261a-7e27-aa2ea85c5d10"},"outputs":[],"source":""}],"metadata":{"_change_revision":0,"_is_fork":false,"kernelspec":{"display_name":"R","language":"R","name":"ir"},"language_info":{"codemirror_mode":"r","file_extension":".r","mimetype":"text/x-r-source","name":"R","pygments_lexer":"r","version":"3.3.3"}},"nbformat":4,"nbformat_minor":0}