{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"23f40715-2c13-272f-b3e4-b8467865b64d"},"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":"markdown","metadata":{"_cell_guid":"f8e3ecbd-bd13-9694-07cd-4728f6b3de29"},"source":"### Description  \n**train.csv** — the training set  \n**test.csv** — the test set  \n**sample_submission.csv** — a sample submission file in the correct format  \n**cliente_tabla.csv** — client names (can be joined with train/test on Cliente_ID)  \n**producto_tabla.csv** — product names (can be joined with train/test on Producto_ID)  \n**town_state.csv** — town and state (can be joined with train/test on Agencia_ID)  \n\n### Data fields  \n**Semana** — Week number (From Thursday to Wednesday)  \n**Agencia_ID** — Sales Depot ID  \n**Canal_ID** — Sales Channel ID  \n**Ruta_SAK** — Route ID (Several routes = Sales Depot)  \n**Cliente_ID** — Client ID  \n**NombreCliente** — Client name  \n**Producto_ID** — Product ID  \n**NombreProducto** — Product Name  \n**Venta_uni_hoy** — Sales unit this week (integer)  \n**Venta_hoy** — Sales this week (unit: pesos)  \n**Dev_uni_proxima** — Returns unit next week (integer)  \n**Dev_proxima** — Returns next week (unit: pesos)  \n**Demanda_uni_equil** — Adjusted Demand (integer) (This is the target you will predict)"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"4b220261-ec93-7ce6-ce95-903008470af6"},"outputs":[],"source":"# Imports\n\n# pandas\nimport pandas as pd\nfrom pandas import Series,DataFrame\n\n# numpy, matplotlib, seaborn\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nsns.set_style('whitegrid')\n%matplotlib inline\n\n# machine learning\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.svm import SVC, LinearSVC\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.naive_bayes import GaussianNB"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"56949eee-9efb-d041-2ce1-1344fb992546"},"outputs":[],"source":"# get titanic & test csv files as a DataFrame\ntrainingSet = pd.read_csv(\"../input/train.csv\")\ntestSet = pd.read_csv(\"../input/test.csv\")\n\n# preview the data\ntrainingSet.head()"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"44c5cba2-eb7f-268c-e60c-3f9c89d3b466"},"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}