{"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\n\nimport os\nprint(os.listdir(\"../input\"))\ntrain=pd.read_csv(\"../input/train.csv\")\ntest=pd.read_csv(\"../input/test.csv\")\ntrain_y=train['label'] #output column\ntrain_x=train.drop('label',axis=1)\nfrom sklearn.neighbors import KNeighborsClassifier\nmodel = KNeighborsClassifier(n_neighbors=3)\nmodel.fit(train_x,train_y)\npred=model.predict(test)\nfinal = pd.DataFrame({'ImageId': list(range(1, len(pred)+1)), 'Label': pred})\nfinal.to_csv('final.csv', index=False)\n# p=pd.DataFrame(model_rf.predict(test))\n#p.head()\n#Any results you write to the current directory are saved as output.","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","collapsed":true,"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","trusted":false},"cell_type":"code","source":"# #decision tree\n# hr=pd.read_csv('train.csv')\n# train,test=train_test_split(hr,test_size=0.3,random_state=100)\n# train_y=train['label'] #output column\n# test_y=test['label']\n# train_x=train.drop('label',axis=1)# i/p - all columns other than attrition(rowwise operation)\n# test_x=test.drop('label',axis=1)\n# model=DecisionTreeClassifier(random_state=100)#maxdepth is used to control the depth in the tree(only one division is done)\n# model.fit(train_x,train_y)\n# test_pre=model.predict(test_x)\n# df_predictions = pd.DataFrame({'actual':test_y,'predicted':test_pre})\n# df_predictions['pred_status']=df_predictions['actual']==df_predictions['predicted']\n# df_predictions['pred_status'].value_counts()\n# df_predictions['pred_status'].sum()/df_predictions.shape[0]*100","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"06a248e24a39e276a0387c8bb13a52f49e682901"},"cell_type":"code","source":"##ada boost\n\n# hr=pd.read_csv('train.csv')\n# from sklearn.ensemble import AdaBoostClassifier\n# train,test=train_test_split(hr,test_size=0.3,random_state=100)\n# train_y=train['label'] #output column\n# test_y=test['label']\n# train_x=train.drop('label',axis=1)# i/p - all columns other than attrition(rowwise operation)\n# test_x=test.drop('label',axis=1)\n# model=AdaBoostClassifier(random_state=100)\n# model.fit(train_x,train_y)\n# test_pred = model.predict(test_x)\n# df_pred=pd.DataFrame({'actual':test_y,'predicted':test_pred})\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":"59ae515dfbd49db01ab7e70668c819318d5e3a19"},"cell_type":"code","source":"## random forest\n\n# hr=pd.read_csv('train.csv')\n# train,test=train_test_split(hr,test_size=0.3,random_state=100)\n# train_y=train['label'] #output column\n# test_y=test['label']\n# train_x=train.drop('label',axis=1)# i/p - all columns other than attrition(rowwise operation)\n# test_x=test.drop('label',axis=1)\n# from sklearn.ensemble import RandomForestClassifier\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# df_pred['pred_status'] = df_pred['actual']==df_pred['predicted']\n# df_pred['pred_status'].sum()/df_pred.shape[0]*100\n","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}