{"cells":[{"metadata":{"trusted":true},"cell_type":"code","source":"import numpy as np \nimport matplotlib.pyplot as plt \nimport pandas as pd \nfrom sklearn.ensemble import RandomForestClassifier\n\ntrain = pd.read_csv('../input/train.csv')\ntest  = pd.read_csv('../input/test.csv')\ntarget = train[\"label\"]\ntrain = train.drop(\"label\",1)\n\nclf = RandomForestClassifier(n_estimators = 100, n_jobs=1, criterion=\"gini\")\nclf.fit(train, target)\nresults=clf.predict(test)\n\n# prepare submit file\n\nnp.savetxt('results.csv', \n           np.c_[range(1,len(test)+1),results], \n           delimiter=',', \n           header = 'ImageId,Label', \n           comments = '', \n           fmt='%d')\n","execution_count":83,"outputs":[{"output_type":"error","ename":"AttributeError","evalue":"'numpy.ndarray' object has no attribute 'csv'","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)","\u001b[0;32m<ipython-input-83-338e807bd819>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     21\u001b[0m            \u001b[0mcomments\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m''\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m            fmt='%d')\n\u001b[0;32m---> 23\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcsv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'csv'"]}]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.6.4","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":1}