I am looking for solutions where I can automatically approve or disapprove different supplier invoices based on historical data.
Let's say, I got an invoice from an HP laptop supplier and based on the previous data, I have to approve or reject that invoice.
Basically, I want to make a decision or prediction based on the data already available based on the history with artificial intelligence, machine learning or any other cloud service
This isn't a direct question though but you can start by looking into various methods of classifications. There is a huge amount of material available online. Try reading about K-Nearest Neighbors, Naive Bayes, K-means, etc. to get an idea about how algorithms in Machine Learning domain work. Once you start understanding what is written in the documentation then start implementing them. You will face a lot of problems which you can search online and I'm sure you will find most of them answered here in this portal.
Friends,
We are trying work on a problem where we have a dump of only reviews but there is no rating in a .csv file. Each row in .csv is one review given by customer of a particular product, lets a TV.
Here, I wanted to do classification of that text into below pre-defined category given by the domain expert of that products:
Quality
Customer
Support
Positive Feedback
Price
Technology
Some reviews are as below:
Bought this product recently, feeling a great product in the market.
Was waiting for this product since long, but disappointed
The built quality is not that great
LED screen is picture perfect. Love this product
Damm! bought this TV 2 months ago, guess what, screen showing a straight line, poor quality LED screen
This has very complicated options, documentation of this TV is not so user-friendly
I cannot use my smart device to connect to this TV. Simply does not work
Customer support is very poor. I don't recommend this
Works great. Great product
Now, with above 10 reviews by 10 different customers, how do I categorize them into the given buckets (you can call multilabel classification or Named Entity Recognition or Information extraction with sentiment analysis or be it anything)
I tried all NLP word frequency counting related stuff (in R) and referred StanfordNLP (https://nlp.stanford.edu/software/CRF-NER.shtml) and many more. But could not get a concrete solution.
Can anybody please guide me how can we tackle this problem? Thanks !!!
Most NLP frameworks will handle multi-class classification. Word count by itself in R will not likely be very accurate. A python library you can explore is Spacy. Commercial APIs like Google, AWS, Microsoft can also be used. You will need quite a few examples per category for training. Feel free to post your code and the problem or performance gap you see for further help.
I was diving more into anomaly detection algorithms and found many applications in various domains including seismic acitivty, IDS etc. However I could not find a single paper on Google Scholar nor on Semantic Scholar on the application to stock markets, but there are endless on the prediction of stock markets.
I just found these two sites, which briefly discuss it: SliceMatrix and Intro to AD
How come? Is it not as interesting as predicting the stock market price? Or is there an additional complication of stock markets, I am not aware of?
I would be very glad, if anyone could guide me to some resources on this topic.
Kind Regards
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My friend asked me if I could write a program capable of identifying relevant keywords from job adverts knowing 3 variables: Industry, job title and the job posting text (example below).
The problem we are trying to address, from a job seeker's point of view, evolves around having the correct keywords in your resume for each job application hereby increasing your chances of getting shortlisted for an interview. This is especially important when the first stage screening is done by bots scanning for keywords.
Initially I was considering a relational database containing all industries, all job titles and their related keywords. This however is an enormous task and the data in progressive fields like information and bio technology would quickly become stale.
It seems machine learning and natural language processing is unavoidable.
Consider below job advert for a bank seeking a teller:
Are you an experienced Bank Teller seeking that perfect work life
balance? If you’re looking for Casual Hours and have an absolute
passion for customer service then this is the role for you!
Our client services Queensland Public Servants (particularly
Queensland Police); and is currently seeking a Bank Teller to join
their Brisbane CBD team to start ASAP.
The successful candidate will be required to work from 9:30am to
2:30pm, Monday to Friday therefore 25 hours per week. Based on
experience the successful candidate will be paid (approximately) $25 -
$27 + superannuation per hour.
This position is casual/temporary with the potential to for a
permanent placement (based on performance/length of assignment etc.).
DUTIES & RESPONSIBILITIES:
As a Bank Teller your will be required to:
Attend to customers in a exceptional professional and efficient
manner; Processing basic transactions such as deposits and
withdrawals; Complete complex transactions such as loans and
mortgages; Pass referrals onto sales team (NO SALES); Large amounts of
cash handling; and Ensuring high attention to detail is at the top of
your list! SKILLS & EXPERIENCED:
The successful candidate will have the following:
Previous teller experience (within last 5 years) IDEAL; Previous
customer service experience (within finance) IDEAL; Ability to work in
a fast paced and time pressured environment; Excellent presentation
and attitude; Exceptional attention to detail; Ability to quickly
‘master’ multiple software packages; and Strong time management skills
and ability to work autonomously. If you boast to have fantastic
customer service skills, a professional manner, and preferrably teller
experience we would LOVE to hear from you!
If I was the hiring manager (or a bot) I would probably look for these keywords in the resume:
teller, transactions, deposits, withdrawals, loans, mortgages, customer
service, time management
How would you attack this problem?
If you have access to lots of advertisements, group them by job title and then run a topic modelling algorithm such as Latent Dirichlet Allocation (LDA) on each group. This will produce the keywords.
For more information see Relink who does exactly what you are trying to do. They provide an outline of the process here:
The Science Behind Relink - Organizing Job Postings
Here is a paper that may help: Modeling Career Path Trajectories.
For a technical paper on just LDA see Latent Dirichlet Allocation.
For an article with sample Python code using the gensim library see Experiments on the English Wikipedia. This is an interesting article as it deals with a huge corpus, a dump of the entire Wikipedia database, and talks about ways of improving execution times using distributed LDA on a cluster of computers. The sample code also shows how to apply Latent Semantic Analysis and compares the results with LDA.
The following article and sample code by Jordan Barber, Latent Dirichlet Allocation (LDA) with Python, uses NLTK to create a corpus and gensim for LDA. This code is more adaptable to other applications than the Wikipedia code.
Given a set of data very similar to the Motley Fool CAPS system, where individual users enter BUY and SELL recommendations on various equities. What I would like to do is show each recommendation and I guess some how rate (1-5) as to whether it was good predictor<5> (ie. correlation coefficient = 1) of the future stock price (or eps or whatever) or a horrible predictor (ie. correlation coefficient = -1) or somewhere in between.
Each recommendation is tagged to a particular user, so that can be tracked over time. I can also track market direction (bullish / bearish) based off of something like sp500 price. The components I think that would make sense in the model would be:
user
direction (long/short)
market direction
sector of stock
The thought is that some users are better in bull markets than bear (and vice versa), and some are better at shorts than longs- and then a combination the above. I can automatically tag the market direction and sector (based off the market at the time and the equity being recommended).
The thought is that I could present a series of screens and allow me to rank each individual recommendation by displaying available data absolute, market and sector out performance for a specific time period out. I would follow a detailed list for ranking the stocks so that the ranking is as objective as possible. My assumption is that a single user is right no more than 57% of the time - but who knows.
I could load the system and say "Lets rank the recommendation as a predictor of stock value 90 days forward"; and that would represent a very explicit set of rankings.
NOW here is the crux - I want to create some sort of machine learning algorithm that can identify patterns over a series of time so that as recommendations stream into the application we maintain a ranking of that stock (ie. similar to correlation coefficient) as to the likelihood of that recommendation (in addition to the past series of recommendations ) will affect the price.
Now here is the super crux. I have never taken an AI class / read an AI book / never mind specific to machine learning. So I cam looking for guidance - sample or description of a similar system I could adapt. Place to look for info or any general help. Or even push me in the right direction to get started...
My hope is to implement this with F# and be able to impress my friends with a new skill set in F# with an implementation of machine learning and potentially something (application / source) I can include in a tech portfolio or blog space;
Thank you for any advice in advance.
I have an MBA, and teach data mining at a top grad school.
The term project this year was to predict stock price movements automatically from news reports. One team had 70% accuracy, on a reasonably small sample, which ain't bad.
Regarding your question, a lot of companies have made a lot of money on pair trading (find a pair of assets that normally correlate, and buy/sell pair when they diverge). See the writings of Ed Thorpe, of Beat the Dealer. He's accessible and kinda funny, if not curmudgeonly. He ran a good hedge fund for a long time.
There is probably some room in using data mining to predict companies that will default (be unable to make debt payments) and shorting† them, and use the proceeds to buy shares in companies less likely to default. Look into survival analysis. Search Google Scholar for "predict distress" etc in finance journals.
Also, predicting companies that will lose value after an IPO (and shorting them. edit: Facebook!). There are known biases, in academic literature, that can be exploited.
Also, look into capital structure arbitrage. This is when the value of the stocks in a company suggest one valuation, but the value of the bonds or options suggest another value. Buy the cheap asset, short the expensive one.
Techniques include survival analysis, sequence analysis (Hidden Markov Models, Conditional Random Fields, Sequential Association Rules), and classification/regression.
And for the love of God, please read Fooled By Randomness by Taleb.
† shorting a stock usually involves calling your broker (that you have a good relationship with) and borrowing some shares of a company. Then you sell them to some poor bastard. Wait a while, hopefully the price has gone down, you buy some more of the shares and give them back to your broker.
My Advice to You:
There are several Machine Learning/Artificial Intelligence (ML/AI) branches out there:
http://www-formal.stanford.edu/jmc/whatisai/node2.html
I have only tried genetic programming, but in the "learning from experience" branch you will find neural nets. GP/GA and neural nets seem to be the most commonly explored methodologies for the purpose of stock market predictions, but if you do some data mining on Predict Wall Street, you might be able to utilize a Naive Bayes classifier to do what you're interested in doing.
Spend some time learning about the various ML/AI techniques, get a small data set and try to implement some of those algorithms. Each one will have its strengths and weaknesses, so I would recommend that you try to combine them using Naive Bays classifier (or something similar).
My Experience:
I'm working on the problem for my Masters Thesis so I'll pitch my results using Genetic Programming: www.twitter.com/darwins_finches
I started live trading with real money in 09/09/09.. yes, it was a magical day! I post the GP's predictions before the market opens (i.e. the timestamps on twitter) and I also place the orders before the market opens. The profit for this period has been around 25%, we've consistently beat the Buy & Hold strategy and we're also outperforming the S&P 500 with stocks that are under-performing it.
Some Resources:
Here are some resources that you might want to look into:
Max Dama's blog: http://www.maxdama.com/search/label/Artificial%20Intelligence
My blog: http://mlai-lirik.blogspot.com/
AI Stock Market Forum: http://www.ai-stockmarketforum.com/
Weka is a data mining tool with a collection of ML/AI algorithms: http://www.cs.waikato.ac.nz/ml/weka/
The Chatter:
The general consensus amongst "financial people" is that Artificial Intelligence is a voodoo science, you can't make a computer predict stock prices and you're sure to loose your money if you try doing it. None-the-less, the same people will tell you that just about the only way to make money on the stock market is to build and improve on your own trading strategy and follow it closely.
The idea of AI algorithms is not to build Chip and let him trade for you, but to automate the process of creating strategies.
Fun Facts:
RE: monkeys can pick better than most experts
Apparently rats are pretty good too!
I understand monkeys can pick better than most experts, so why not an AI? Just make it random and call it an "advanced simian Mersenne twister AI" or something.
Much more money is made by the sellers of "money-making" systems then by the users of those systems.
Instead of trying to predict the performance of companies over which you have no control, form a company yourself and fill some need by offering a product or service (yes, your product might be a stock-predicting program, but something a little less theoretical is probably a better idea). Work hard, and your company's own value will rise much quicker than any gambling you'd do on stocks. You'll also have plenty of opportunities to apply programming skills to the myriad of internal requirements your own company will have.
If you want to go down this long, dark, lonesome road of trying to pick stocks you may want to look into data mining techniques using advanced data mining software such as SPSS or SAS or one of the dozen others.
You'll probably want to use a combination or technical indicators and fundamental data. The data will more than likely be highly correlated so a feature reduction technique such as PCA will be needed to reduce the number of features.
Also keep in mind your data will constantly have to be updated, trimmed, shuffled around because market conditions will constantly be changing.
I've done research with this for a grad level class and basically I was somewhat successful at picking whether a stock would go up or down the next day but the number of stocks in my data set was fairly small (200) and it was over a very short time frame with consistent market conditions.
What I'm trying to say is what you want to code has been done in very advanced ways in software that already exists. You should be able to input your data into one of these programs and using either regression, or decision trees or clustering be able to do what you want to do.
I have been thinking of this for a few months.
I am thinking about Random Matrix Theory/Wigner's distribution.
I am also thinking of Kohonen self-learning maps.
These comments on speculation and past performance apply to you as well.
I recently completed my masters thesis on deep learning and stock price forecasting. Basically, the current approach seems to be LSTM and other deep learning models. There are also 10-12 technical indicators (TIs) based on moving average that have been shown to be highly predictive for stock prices, especially indexes such as SP500, NASDAQ, DJI, etc. In fact, there are libraries such as pandas_ta for computing various TIs.
I represent a group of academics that are trying to predict stocks in a general form that can also be applied to anything, even the rating of content.
Our algorithm, which we describe as truth seeking, works as follows.
Basically each participant has their own credence rating. This means that the higher your credence or credibility, then the more their vote counts. Credence is worked out by how close to the weighted credence each vote is. It's like you get a better credence value the closer you get to the average vote that has already been adjusted for credence.
For example, let's say that everyone is predicting that a stock's value will be at value X in 30 day's time (a future's option). People who predict on the average get a better credence. The key here is that the individual doesn't know what the average is, only the system. The system is tweaked further by weighting the guesses so that the target spot that generates the best credence is those votes that are already endowed with more credence. So the smartest people (historically accurate) project the sweet spot that will be used for further defining who gets more credence.
The system can be improved too to adjust over time. For example, when you find out the actual value, those people who guessed it can be rewarded with a higher credence. In cases where you can't know the future outcome, you can still account if the average weighted credence changes in the future. People can be rewarded even more if they spotted the trend early. The point is we don't need to even know the outcome in the future, just the fact that the weighted rating changed in the future is enough to reward people who betted early on the sweet spot.
Such a system can be used to rate anything from stock prices, currency exchange rates or even content itself.
One such implementation asks people to vote with two parameters. One is their actual vote and the other is an assurity percentage, which basically means how much a particular participant is assured or confident of their vote. In this way, a person with a high credence does not need to risk downgrading their credence when they are not sure of their bet, but at the same time, the bet can be incorporated, it just won't sway the sweet spot as much if a low assurity is used. In the same vein, if the guess is directly on the sweet spot, with a low assurity, they won't gain the benefits as they would have if they had used a high assurity.