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I am searching for some datasets in form of 0 and 1 . i cant find dataset. i have find some 10 to 12 records but want at least 100 records and 8 different records.
This is one i have searched this link but this is very low data http://searchbusinessanalytics.techtarget.com/feature/Simple-data-mining-examples-and-datasets
Check out public data sets from Google. Maybe they'll have what you want.
Have a look at the UCI machine learning repository. There you can filter how many attributes/features or prediction classes you want and choose a dataset.
The Credit Approval dataset might suit your search criteria:
http://archive.ics.uci.edu/ml/datasets/Credit+Approval
Also there is a dataset called Mushroom dataset which should also fit to what you are looking for.
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I have a dataset that has, users, items and views,which is the interaction between user and item.
The only difference in this dataset from the other recommendation datasets is that, the items have strong timeliness i.e. the items expires after a certain time period and won't be considered anymore.(Items life span can range from 1 week - 4 months)
You can use Bayesian Personalized Ranking for implicit feedback. I wrote about my experience of building such recommendation systems using Tensorflow.
Regarding timeliness, you should use only active items to find recommendations.
For an example of the workflow can look like:
you need to recommend 5 items;
you ask the system to give you 30 recommendations using only active items;
then exclude items that will expire in the next 2 days;
then randomly select 5 from those who stayed;
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I am looking for the machine learning correct approach for predicting the lottery numbers, not the most accurate answer but at least we have some predicted output. I am implementing the regression based and neural network models for this. Is their any specific approach which follows this?
It is impossible. The lottery numbers are random - actually to be more specific, the system is chaotic. You would require the initial configuration (positions etc) to insane (possibly infinite) precision to be able to make any predictions. Basically, don't even try it.
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I would like to know if it is possible to read several lines from a file in parallel with my GPU in Keras. Imagine that I have a file with 50 features (i.e 50 columns) and 10k lines that I would like to process in parallel, what's the most efficient way to do it with Keras? Moreover, I don't want to train my model, since I'm already in the deployment phase of the project.
Read the entire file, create a batch with shape (10k, 50) and give it to the model:
result = model.predict(inputData)
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Hi i want to predict health level(High,medium,low) in leaf using image processing and data mining.So far i thought using extract colors from leaf using Bayes algorithm to predict healthy of leaf. and data mining part have completed now.but i need extra features for prediction.we only used orchid leaf.So i can't use vain structure.Can anyone help me to what are the other features can be extracted from leaf for identify health level of leaf.Any idea or comments help me to improve my project. Thanks
There are many possible approaches to a problem like this. One common method is the bag-of-features model. Take a look at this example using the Computer Vision System Toolbox in MATLAB.
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i am new to data mining,i just want to know which feature selection is easy and best for time series data.
as my project is share market prediction...these are the following parameters available and i have to select best 5 features for constructing my model.can anyone help me which one to use and how to do it..
Features available are
symbol,series,date,prev close,open price,high price,low price,last price,close price,average price,total traded quantity,turnover in lacs, deliverable qty,% deliv qty to to traded qty
you can see those features in this link
click on it to see the data in nse website to know how data represents
Here I will not do your home work to select the features(attributes) but I can help you so that you can also select features:-
You can use Minimum-redundancy-maximum-relevance.
Do by choosing that features which are less repeating and giving giving high co-relation with output.You must google for more information about mRMR algorithm.