{"cells":[{"cell_type":"markdown","metadata":{"_cell_guid":"e4214ff1-8b32-82c3-3d82-84fa0d3f8e73"},"source":"Import sklearn.ensemble.RandomForestClassifier"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"0142942c-c129-0821-3f7a-6ee0565214d8"},"outputs":[],"source":"import numpy as np\nimport pandas as pd\nfrom pandas import DataFrame\nfrom sklearn.ensemble import RandomForestClassifier\n\ndf = pd.read_csv('../input/train.csv')\nY = df['label']\nX = df.drop('label', axis=1)\nx_test = pd.read_csv('../input/test.csv')\n\nmodel = RandomForestClassifier()\n\nmodel.fit(X, Y)\n\npredicted = model.predict(x_test)\n\nsubmission = DataFrame(predicted, columns=['Label'], \n                       index=np.arange(1, 28001))\nsubmission.index.names = ['ImageId']"},{"cell_type":"markdown","metadata":{"_cell_guid":"0cee834e-88ca-474c-8849-daee21cfc5c9"},"source":"Got the result"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"de486cbe-8c61-9e41-2c6a-2519fb693455"},"outputs":[],"source":"submission"}],"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":"3.5.2"}},"nbformat":4,"nbformat_minor":0}