{"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":"# 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\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 read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\ntr=pd.read_csv('train.csv')\nts=pd.read_csv('test.csv')\nx_train=tr.iloc[:,2:6]\ny_train=tr.iloc[:,6:]\nx_test=ts.iloc[:,1:5]\ny_test=ts.iloc[:,5:]\nlabel=pd.DataFrame(tr.iloc[:,1:2])\nx_train=pd.DataFrame(x_train)\ny_train=pd.DataFrame(y_train)\nx_test=pd.DataFrame(x_test)\ny_test=pd.DataFrame(y_test)\nx_train[\"Sex\"].replace({\"male\": 0, \"female\": 1}, inplace=True)\nx_train[\"company\"]=y_train[\"SibSp\"]+y_train[\"Parch\"]\nx_test[\"Sex\"].replace({\"male\": 0, \"female\": 1}, inplace=True)\nx_test[\"company\"]=y_test[\"SibSp\"]+y_test[\"Parch\"]\nx_test.drop('Name',axis='columns', inplace=True)\nx_train.drop('Name',axis='columns', inplace=True)","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"x=x_train.iloc[0:423,:]\ny=x_train.iloc[423:,:]\ns=x.median()\na=y.median()\nd=y.median()-x.median()\nz=s-(1.5*d)\nw=a+(1.5*d)\nx_train=x_train.iloc[26:861,:]\nlabel=label.iloc[26:861,0:]","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"x_train[\"Age\"]=x_train[\"Age\"].fillna(26)\nx_test[\"Age\"]=x_test[\"Age\"].fillna(34)","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"x_test=np.array(x_test)\nx_train=np.array(x_train)\nx_train_min=x_train.min()\nx_train_max=x_train.max()\nts_min=x_test.min()\nts_max=x_test.max()\nx_train=(x_train-x_train_min)/(x_train_max-x_train_min)\nx_test=(x_test-ts_min)/(ts_max-ts_min) \nlabel=np.array(label)","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn import svm\ntarget=[]\nclassifier=svm.SVC(kernel='linear',gamma='auto',C=10)\nclassifier.fit(x_train,label)\ntarget=classifier.predict(x_test)","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"ta=pd.read_csv('gender_submission.csv')\nta['Survived']=target\nta.to_csv('submission.csv',encoding='utf-8',index=False)   \n!kaggle comn petitions submit -c bmi-linear-regression-competition -f submission.csv -s \"test\"","metadata":{},"execution_count":null,"outputs":[]}]}