{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"collapsed":false},"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.\npd.set_option('display.max_columns', None)\ntrain_data = pd.read_csv('../input/train.csv')"},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":false},"outputs":[],"source":"# Data fields\n\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)\ntrain_data.head()"},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":false},"outputs":[],"source":""}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"}},"nbformat":4,"nbformat_minor":0}