{"cells":[{"metadata":{"_uuid":"c46a642c779ed2c14c2b1c51800303647cb1b6e1"},"cell_type":"markdown","source":"**ELO worlds data **\n\nIn this kernel ,I bulid a randomforest regreesor  model for train.csv,historical_transactions.csv,and merchants.csv\n"},{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","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 \nimport seaborn as sns\nimport os\nprint(os.listdir(\"../input\"))\n\n# Any results you write to the current directory are saved as output.","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"aee62b477202a1bf9710103356395b980c07b204"},"cell_type":"markdown","source":"First, we load the new_merchant_transactions.csv and historical_transactions.csv. In practice, these two files contain the same variables and the difference between the two tables only concern the position with respect to a reference date."},{"metadata":{"trusted":true,"_uuid":"d05fd121726d816c2252e878621c64c14091deac"},"cell_type":"code","source":"train=pd.read_csv(\"../input/train.csv\",parse_dates=[\"first_active_month\"])\ntest=pd.read_csv(\"../input/test.csv\",parse_dates=[\"first_active_month\"])","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"3110c2ffa6bbf44c951c968664d6ec2125bd0146"},"cell_type":"markdown","source":"* train.csv - the training set\n* test.csv - the test set"},{"metadata":{"trusted":true,"_uuid":"d4ad2d2a06bc0ef08ebe27ff754973d07734301d"},"cell_type":"code","source":"print(\"columns and rows in train dataset:\",train.shape)\nprint(\"columns and rows in test dataset:\",test.shape)\n","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"a71ce41793e8ec7ac1f9a638d59af526fe4df703"},"cell_type":"markdown","source":"we have to  show the  train dataset columns "},{"metadata":{"trusted":true,"_uuid":"2e27cc8bd58ccb2cecbb73b2db04944fffaf5f6b"},"cell_type":"code","source":"train.head()","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"2fceea357c730c09a0cc75875260b490b58d7f53"},"cell_type":"markdown","source":"**EDA******\nnow we will explore the target varible"},{"metadata":{"trusted":true,"_uuid":"6a7496b238f03b65dc0832d4df166ca6578d1f4b"},"cell_type":"code","source":"train.target.plot.hist()","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"93527f7380d5a7e2497b71607352e518c7719f77"},"cell_type":"markdown","source":"we above target varible 1/2 values  are  negative and  1/2 values are positive . "},{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","trusted":true},"cell_type":"code","source":"hist=pd.read_csv(\"../input/historical_transactions.csv\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"42ff4fa8954d54fee4592996a7738d292b64dda7"},"cell_type":"code","source":"hist.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"c9b18a006b5574e10e75fd70d3ecbb16201b6b12"},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.6.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":1}