{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"5c45aa02-dd9d-3f77-7235-db5066f88587"},"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":"e8e86dc9-ab23-b813-8dc3-12d330b4ba4a"},"outputs":[],"source":"import pandas as pd\nfrom sklearn import svm\nclassifier = svm.SVC(gamma=0.001)\ndataset = pd.read_csv(\"../input/train.csv\")\ntrain_y = dataset.iloc[:, 0]\ntrain_x = dataset.iloc[:, 1:]\n\ntest_x = pd.read_csv(\"../input/test.csv\")\n\n\n"}],"metadata":{"_change_revision":0,"_is_fork":false,"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.6.0"}},"nbformat":4,"nbformat_minor":0}