{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"8ba267b8-1a80-1cd7-56be-5310857d2ed5"},"outputs":[],"source":"import numpy as np\nimport pandas as pd\nimport math\nfrom IPython.display import display\n\ntraining = pd.read_csv('../input/train.csv', nrows = 500000)\ntesting = pd.read_csv('../input/test.csv', nrows = 500000)\nprint(\"Training dataset\")\nprint(training.head());    print(training.tail());   print(str(training.shape));    print()\n\nprint(training.describe());   print()\n\nprint(\"Testing dataset\")\nprint(testing.head());   print(str(testing.shape));    print()"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"52569e4b-2b42-54f0-b12c-73e5da54690f"},"outputs":[],"source":"forecast_col = 'Demanda_uni_equil'\nforecast_out = -2\ntraining.fillna(-99999, inplace=True)\n\ntraining[forecast_col] = training[forecast_col].shift(forecast_out)\n# testing[forecast_col] = testing[forecast_col].shift(forecast_out) test data doesn't have it\nprint(\"Training dataset\")\nprint(training.head());    print(training.tail());    print()\n\ntraining.dropna(inplace=True)\nprint(training.head());    print(training.tail());    print()"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"5f381798-ad3e-de44-3170-24bdae848148"},"outputs":[],"source":"from sklearn.linear_model import LinearRegression\nfrom sklearn import cross_validation, svm, preprocessing\n\nX = training.drop([forecast_col], 1)\n#X_train = training[['Ruta_SAK',  'Cliente_ID',  'Producto_ID']]\ny = training[forecast_col]\nprint(\"X_train values are\")\nprint(X.head());   print()\n\nprint(\"y values are\")\nprint(y.head());   print()\n\nX_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.3)\n\nclf = svm.SVR()\nclf.fit(X_train, y_train)\n\naccuracy = clf.score(X_test, y_test)\nprint(accuracy)"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"0403ede3-fc03-f4e3-6e0b-bd739d9dfa95"},"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":"3.5.2"}},"nbformat":4,"nbformat_minor":0}