{"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":"markdown","source":"### Author: [Roshan Ram](https://linkedin.com/in/roshanr11)\n---\n##### About: Hi! I'm Roshan, an undergraduate student @ Carnegie Mellon studying Information Systems, Machine Learning, and Software Engineering.\n---\n##### I blog about ML on my [LinkedIn](https://linkedin.com/in/roshanr11) and [Medium](https://medium.com/@roshan.ram0101), check it out!\n\n---\n\n##### Please feel free to give this notebook an upvote if you found it helpful, and connect with me on LinkedIn! :) \n\n---\n\n### Note: Notebook is still a work in progress! ","metadata":{}},{"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","execution":{"iopub.status.busy":"2021-11-02T22:57:00.789931Z","iopub.execute_input":"2021-11-02T22:57:00.790288Z","iopub.status.idle":"2021-11-02T22:57:00.821427Z","shell.execute_reply.started":"2021-11-02T22:57:00.790185Z","shell.execute_reply":"2021-11-02T22:57:00.820828Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train = pd.read_csv('../input/g-research-crypto-forecasting/train.csv') \ntest = pd.read_csv('../input/g-research-crypto-forecasting/example_test.csv') \nsub = pd.read_csv('../input/g-research-crypto-forecasting/example_sample_submission.csv') \nasset_details = pd.read_csv('../input/g-research-crypto-forecasting/asset_details.csv')  ","metadata":{"execution":{"iopub.status.busy":"2021-11-02T22:59:36.882196Z","iopub.execute_input":"2021-11-02T22:59:36.882492Z","iopub.status.idle":"2021-11-02T23:00:31.451557Z","shell.execute_reply.started":"2021-11-02T22:59:36.882459Z","shell.execute_reply":"2021-11-02T23:00:31.450285Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train.head()","metadata":{"execution":{"iopub.status.busy":"2021-11-02T23:04:11.147809Z","iopub.execute_input":"2021-11-02T23:04:11.148256Z","iopub.status.idle":"2021-11-02T23:04:11.164319Z","shell.execute_reply.started":"2021-11-02T23:04:11.148226Z","shell.execute_reply":"2021-11-02T23:04:11.163727Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train.describe()","metadata":{"execution":{"iopub.status.busy":"2021-11-02T23:06:23.217307Z","iopub.execute_input":"2021-11-02T23:06:23.217816Z","iopub.status.idle":"2021-11-02T23:06:31.265855Z","shell.execute_reply.started":"2021-11-02T23:06:23.217766Z","shell.execute_reply":"2021-11-02T23:06:31.264907Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train.info()","metadata":{"execution":{"iopub.status.busy":"2021-11-02T23:06:56.59233Z","iopub.execute_input":"2021-11-02T23:06:56.592624Z","iopub.status.idle":"2021-11-02T23:06:56.607985Z","shell.execute_reply.started":"2021-11-02T23:06:56.592593Z","shell.execute_reply":"2021-11-02T23:06:56.60706Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### We note that we have 10 columns. \n\n`timestamp` - A timestamp for the minute covered by the row.  \n`Asset_ID` - An ID code for the cryptoasset.  \n`Count` - The number of trades that took place this minute.  \n`Open` - The USD price at the beginning of the minute.  \n`High` - The highest USD price during the minute.  \n`Low` - The lowest USD price during the minute.  \n`Close` - The USD price at the end of the minute.  \n`Volume` - The number of cryptoasset units traded during the minute.  \n`VWAP` - The volume weighted average price for the minute.  \n`Target` - 15 minute residualized returns. See the 'Prediction and Evaluation' section of this notebook for details of how the target is calculated.  ","metadata":{}},{"cell_type":"code","source":"test.head()","metadata":{"execution":{"iopub.status.busy":"2021-11-02T23:00:31.488314Z","iopub.execute_input":"2021-11-02T23:00:31.488538Z","iopub.status.idle":"2021-11-02T23:00:31.503403Z","shell.execute_reply.started":"2021-11-02T23:00:31.488511Z","shell.execute_reply":"2021-11-02T23:00:31.502455Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Automated EDA","metadata":{}},{"cell_type":"code","source":"# import pandas_profiling as pp\n# pp.ProfileReport(train)","metadata":{"execution":{"iopub.status.busy":"2021-11-03T00:21:27.222973Z","iopub.execute_input":"2021-11-03T00:21:27.224544Z","iopub.status.idle":"2021-11-03T00:21:27.234454Z","shell.execute_reply.started":"2021-11-03T00:21:27.224486Z","shell.execute_reply":"2021-11-03T00:21:27.233263Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Work in progress...","metadata":{}},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}