{"id":31047,"date":"2020-01-10T09:42:34","date_gmt":"2020-01-10T14:42:34","guid":{"rendered":"https:\/\/ibkrcampus.com\/?p=31047"},"modified":"2022-11-21T09:44:54","modified_gmt":"2022-11-21T14:44:54","slug":"neural-network-in-python-part-vii","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/neural-network-in-python-part-vii\/","title":{"rendered":"Neural Network In Python \u2013 Part VII"},"content":{"rendered":"\n

In the previous installment<\/a>, the author discussed how to develop our own Artificial Neural Network.  <\/em><\/p>\n\n\n\n

Setting the random seed to a fixed number<\/strong><\/p>\n\n\n\n

import random\nrandom.seed(42)<\/p>\n\n\n\n

Random will be used to initialize the seed to a fixed number so that every time we run the code we start with the same seed.<\/p>\n\n\n\n

Importing the dataset<\/strong><\/p>\n\n\n\n

dataset = pd.read_csv(‘RELIANCE.NS.csv’)
\ndataset = dataset.dropna()
\ndataset = dataset[[‘Open’, ‘High’, ‘Low’, ‘Close’]]<\/p>\n\n\n\n

We then import our dataset, which is stored in the .csv file named \u2018RELIANCE.NS.csv\u2019. This is done using the pandas library, and the data is stored in a dataframe named dataset. We then drop the missing values in the dataset using the dropna() function. The csv file contains daily OHLC data for the stock of Reliance trading on NSE for the time period from 1st January 1996 to 15th January 2018. <\/p>\n\n\n\n

We choose only the OHLC data from this dataset, which would also contain the date, Adjusted Close and Volume data. We will be building our input features by using only the OHLC values.<\/p>\n\n\n\n

In the next installment, the author will demonstrate how to prepare the dataset.<\/em><\/p>\n\n\n\n

Visit https:\/\/www.quantinsti.com\/<\/a> for ready-to-use functions as applied in trading and data analysis. <\/p>\n","protected":false},"excerpt":{"rendered":"

We import our dataset, which is stored in the .csv file named \u2018RELIANCE.NS.csv\u2019. This is done using the pandas library, and the data is stored in a dataframe named dataset. We then drop the missing values in the dataset using the dropna() function<\/p>\n","protected":false},"author":251,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":true,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[339,343,349,338,350,341,344],"tags":[851,4087,4582,2105,2697,4363,4227,852,3866,1225,1224,595,5902,5482,5483],"contributors-categories":[13654],"class_list":{"0":"post-31047","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-data-science","7":"category-programing-languages","8":"category-python-development","9":"category-ibkr-quant-news","10":"category-quant-asia-pacific","11":"category-quant-development","12":"category-quant-regions","13":"tag-algo-trading","14":"tag-backpropagation","15":"tag-dataframe","16":"tag-deep-learning","17":"tag-deep-neural-networks","18":"tag-forward-propagation","19":"tag-gradient-descent","20":"tag-machine-learning","21":"tag-neuron","22":"tag-numpy","23":"tag-pandas","24":"tag-python","25":"tag-rsi-stochastic-gradient-descent","26":"tag-talib","27":"tag-williams-r","28":"contributors-categories-quantinsti"},"pp_statuses_selecting_workflow":false,"pp_workflow_action":"current","pp_status_selection":"publish","acf":[],"yoast_head":"\nNeural 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