{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"from sklearn.ensemble import RandomForestClassifier\nfrom numpy import genfromtxt, savetxt\n\nCPU = 1\n\ndef main():\n    print(\"Reading training set\")\n    dataset = genfromtxt(open('../input/digit-recognizer/train.csv', 'r'), delimiter=',', dtype='int64')[1:]\n    target = [x[0] for x in dataset]\n    train = [x[1:] for x in dataset]\n    print(\"Reading test set\")\n    test = genfromtxt(open('../input/digit-recognizer/test.csv', 'r'), delimiter=',', dtype='int64')[1:]\n\n    #create and train the random forest\n    rf = RandomForestClassifier(n_estimators=1000, n_jobs=CPU)\n    print(\"Fitting RF classifier\")\n    rf.fit(train, target)\n\n    print(\"Predicting test set\")\n    savetxt('submission-version-1.csv', rf.predict(test), delimiter=',', fmt='%d')\n\n    filepath = r\"submission-version-1.csv\"\n    f = open(\"submission.csv\", \"w\")\n    i = 1\n    with open(filepath, \"r\") as file:\n        f.write(\"ImageId,Label\" + \"\\n\")\n        for row in file:\n            f.write(str(i) + \",\" + str(row))\n            i += 1\n    f.close()\n\n\nif __name__ == \"__main__\":\n    main()","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2022-12-15T16:49:05.594312Z","iopub.execute_input":"2022-12-15T16:49:05.595112Z","iopub.status.idle":"2022-12-15T16:55:05.365831Z","shell.execute_reply.started":"2022-12-15T16:49:05.595020Z","shell.execute_reply":"2022-12-15T16:55:05.363937Z"},"trusted":true},"execution_count":null,"outputs":[]}]}