{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"collapsed":false},"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 pandas as pd\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n# sklearn imports\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.cross_validation import cross_val_score\nfrom sklearn.linear_model import SGDClassifier\n\nmatplotlib.style.use('ggplot')\n\n%matplotlib inline\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"},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":false},"outputs":[],"source":"path = '../input/'\ntrain_file = path + 'train.csv'\ntest_file = path + 'test.csv'\nsample_file = path + 'sample_submission.csv'\n\ncol_types = {'row_id': np.dtype(np.int32),\n            'x': np.dtype(np.float32),\n            'y': np.dtype(np.float32),\n            'accuracy': np.dtype(np.int32),\n            'time': np.dtype(np.int32),\n            'place_id': np.dtype(np.int64)}\n\ntrain_data = pd.read_csv(train_file, dtype = col_types, index_col = 0,)"}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"}},"nbformat":4,"nbformat_minor":0}