{"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\"))\n\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","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","trusted":true},"cell_type":"code","source":"train_loc=\"../input/train.csv\"\n\ntrain_loc2=\"../input/test.csv\"\ntrain_loc3=\"../input/sample_submission.csv\"\n\n\ndf=pd.read_csv(train_loc)\n\ndf2=pd.read_csv(train_loc2)\n\ndf3=pd.read_csv(train_loc3)\ndf.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X=df.iloc[:,1:]\ny=df.iloc[:,0]\nX.shape\n#disp=np.reshape([X],(28,28))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.model_selection import train_test_split\nX_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.1)\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(X_train.shape)\nprint(X_test.shape)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#DECISION TREE CLASSIFIER\nfrom sklearn.tree import DecisionTreeClassifier","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"classifier=DecisionTreeClassifier()\nclassifier.fit(X,y)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"y_new=classifier.predict(X_test)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"y_new","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.metrics import confusion_matrix\ncm=confusion_matrix(y_test,y_new)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"cm","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df3","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"temp=classifier.predict(df2.iloc[:,:])\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"temp.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"temp2=np.array([])\nfor i in range(28000):\n    temp2=np.append(temp2,i+1).astype(int)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"new_df=pd.DataFrame()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"new_df[\"ImageId\"]=temp2\nnew_df[\"Label\"]=temp","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"new_df","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"new_df.to_csv('submissions_new_2.csv', index = False)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"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}