{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"9b8ce086-71bf-3c35-53cc-17607bb71eef"},"outputs":[],"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\nfrom subprocess import check_output\nprint(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n\n# Any results you write to the current directory are saved as output."},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"081330cc-6433-3300-56ad-cbb639cd5ad8"},"outputs":[],"source":"import numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport matplotlib.pyplot as plt, matplotlib.image as mpimg\nfrom sklearn.model_selection import train_test_split\nfrom sklearn import svm\n%matplotlib inline"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"0450e151-7a9e-5496-9704-764b9c33d403"},"outputs":[],"source":"images = pd.read_csv('../input/train.csv')"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"2de798e0-fd4a-9602-bd30-1552b877aa46"},"outputs":[],"source":"feature = images.iloc[:5000,1:]\nlabel = images.iloc[:5000,:1]\ntrain_feature,test_feature,train_label,test_label = train_test_split(feature,label,train_size=0.8)"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"7b18459b-d5e5-ba93-c3f0-a31110416b4c"},"outputs":[],"source":"i=7\nimg = train_feature.iloc[i].as_matrix()\nimg = img.reshape(28,28)\nplt.imshow(img,cmap='gray')\nplt.title(train_label.iloc[i,0])\n#plt.show()"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"9cf1e49b-c1fb-2485-167e-5a2ac08b7b7a"},"outputs":[],"source":"train_feature[train_feature > 100] = 255\ntest_feature[train_feature > 100] = 255\ntrain_feature[train_feature < 101] = 0\ntest_feature[train_feature < 101] = 0"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"3dccc269-4fe5-93ed-3e82-0823fd189b24"},"outputs":[],"source":"train_feature[train_feature>100] = 255\ntest_feature[test_feature>100] = 255\ntrain_feature[train_feature<101] = 0\ntest_feature[test_feature<101] = 0"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"8d7e0d42-6644-518b-39f6-68f6b55abab9"},"outputs":[],"source":"img = train_feature.iloc[9].as_matrix().reshape(28,28)\nplt.imshow(img,cmap='binary')\nplt.title(train_label.iloc[9,0])"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"f7b3a6c2-32bf-87b3-f4b7-f73068dbd49d"},"outputs":[],"source":"clf = svm.SVC()\nclf.fit(train_feature,train_label.values.ravel())\nclf.score(test_feature,test_label)"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"2e3a94f0-04ae-20f9-aceb-3f179220844e"},"outputs":[],"source":""}],"metadata":{"_change_revision":0,"_is_fork":false,"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.6.0"}},"nbformat":4,"nbformat_minor":0}