{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","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 numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\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\nfrom sklearn.neighbors import KNeighborsClassifier\nimport os\nprint(os.listdir(\"../input\"))\ntrain1 = pd.read_csv(\"../input/train.csv\")\ntest = pd.read_csv(\"../input/test.csv\")\n\n\ntrain_x = train1.drop('label', axis = 1)\ntrain_y = train1['label']\n\nmodel_KNN = KNeighborsClassifier(n_neighbors=3)\nmodel_KNN.fit(train_x, train_y)\n\ntest_pred = model_KNN.predict(test)\nfinal_df = pd.DataFrame({'ImageId': list(range(1,len(test_pred)+1)), 'Label': test_pred})\nfinal_df.to_csv('final_df.csv', index = False)\n\n# Any results you write to the current directory are saved as output.","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"e8b01686f0754bea62e665d8ffdbd30b7a195c48"},"cell_type":"code","source":"#from sklearn.ensemble import RandomForestClassifier\n\n#model_rf = RandomForestClassifier(random_state=100)\n#model_rf.fit(train_x, train_y)\n\n#test_pred = model_rf.predict(test_x)\n#df_pred = pd.DataFrame({'actual': test_y, 'predicted': test_pred})\n\n#df_pred['pred_status'] = df_pred['actual'] == df_pred['predicted']\n#df_pred['pred_status'].sum()/df_pred.shape[0]*100","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"490fdf98e40538a7c545b88a96073f23b28274bc"},"cell_type":"code","source":"#from sklearn.tree import DecisionTreeClassifier\n\n#model_dt = DecisionTreeClassifier(random_state=100)\n#model_dt.fit(train_x, train_y)\n\n#test_pred = model_dt.predict(test_x)\n#df_pred = pd.DataFrame({'actual': test_y, 'predicted': test_pred})\n\n#df_pred['pred_status'] = df_pred['actual'] == df_pred['predicted']\n#df_pred['pred_status'].sum()/df_pred.shape[0]*100","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"6c77a7f7468277e8324729371810d3aa6ccd41b1"},"cell_type":"code","source":"#from sklearn.ensemble import AdaBoostClassifier\n\n#model_ab = AdaBoostClassifier(random_state=100)\n#model_ab.fit(train_x, train_y)\n\n#test_pred = model_ab.predict(test_x)\n#df_pred = pd.DataFrame({'actual': test_y, 'predicted': test_pred})\n\n#df_pred['pred_status'] = df_pred['actual'] == df_pred['predicted']\n#df_pred['pred_status'].sum()/df_pred.shape[0]*100","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.6.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":1}