{"nbformat_minor":1,"nbformat":4,"cells":[{"cell_type":"code","source":"# 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.\n","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Let's get started!\n\n# data analysis and wrangling\nimport pandas as pd\nimport numpy as np\nimport random as rnd\n\n# visualization\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n%matplotlib inline\n\n# machine learning\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.svm import SVC, LinearSVC\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.linear_model import Perceptron\nfrom sklearn.linear_model import SGDClassifier\nfrom sklearn.tree import DecisionTreeClassifier","metadata":{"_cell_guid":"e81dfbdd-d48b-4b57-b871-1472ba3e95c1","collapsed":true,"_uuid":"82d5bf7d95afbbd543696b69ca0957c8f81b257f"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_df = pd.read_csv('../input/stage1_train.csv')\ntest_df = pd.read_csv('../input/stage1_test.csv')\ncombine = [train_df, test_df]","metadata":{},"execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","name":"python3","language":"python"},"language_info":{"mimetype":"text/x-python","name":"python","pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"version":3,"name":"ipython"}}}}