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Predict-Amazon-Stock-Price-tomorrow

Predict-Amazon-Stock-Price-tomorrow

active ARFF CC0: Public Domain Visibility: public Uploaded 24-03-2022 by Dustin Carrion
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I have created this dataset to showcase the use of predictive modeling using the stock market as a case study. This dataset is designed to help and predict tomorrow's Amazon stock price. If you want to get the most updated dataset you will need to pull them in real time. I have shared my code to pull data using Yahoo Finance API and preprocess it in Data Analytics for Fun Github Repository The uploaded dataset is for Jan 11, 2021. What are the columns? yes_changeP: Yesterday Amazon's stock price change lastweek_changeP: Last week Amazon's stock price change dow_yes_changeP: Yesterday Dow Jones change dow_lastweek_changeP: Last Week Dow Jones change nasdaq_yes_changeP: Yesterday NASDAQ 100 change nasdaq_lastweek_changeP: Last Week NASDAQ 100 change today_changeP: Today Amazon's stock price change To learn more about the dataset and see a very simple prediction model applied to the dataset you may watch this YouTube Video where I have explained the dataset and also prediction: A Taste for Prediction: Predict Tomorrow's Amazon Stock Price

8 features

tstring350 unique values
0 missing
yes_changePnumeric350 unique values
0 missing
lastweek_changePnumeric350 unique values
0 missing
dow_yes_changePnumeric350 unique values
0 missing
dow_lastweek_changePnumeric350 unique values
0 missing
nasdaq_yes_changePnumeric350 unique values
0 missing
nasdaq_lastweek_changePnumeric350 unique values
0 missing
today_changePnumeric349 unique values
1 missing

19 properties

350
Number of instances (rows) of the dataset.
8
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
1
Number of missing values in the dataset.
1
Number of instances with at least one value missing.
7
Number of numeric attributes.
0
Number of nominal attributes.
0
Percentage of binary attributes.
0.29
Percentage of instances having missing values.
Average class difference between consecutive instances.
0.04
Percentage of missing values.
0.02
Number of attributes divided by the number of instances.
87.5
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
0
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.

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