I am trying to model a market with multiple goods, with multiple sellers and buyers offering a price for one or more goods. It is allowed for a seller who for example is selling 6 apples to sell 2 to a buyer offering the highest price and 4 to the buyer offering a slightly worse price. The market place also needs to support conditional offers, i.e. I want to sell 4 apples but only if I can buy 2 pears.
I have modelled this a few ways now using Google OR-Tools, but the closest way of solving it I can find is using CP-SAT, however it's not immediately obvious to me how to model the conditional offers.
What is the best way of modelling a market place like this?
You should have a look at:
https://github.com/google/or-tools/blob/stable/ortools/sat/doc/channeling.md
The main idea is that you can attach Boolean literals to some properties of variables.
Then you can use standard Boolean logic to link these literals together.
Related
I am trying to predict match winner based on the historical data set as shown below,
The data set comprises of IPL seasons and Team_Name_id vs Opponent Team are the team names in IPL. I have set the match id as Row id and created the model. When running realtime testing, the result is not as expected (shown below)
Target is set as Match_winner_id.
Am I missing any configurations? Please help
The model is working perfectly correctly. There's just two problems:
Your input data is not very good
There's no way for the model to know that only one of those two teams should win
Data Quality
A predictive model needs good quality input data on which to reverse-engineer a model that explains a given result. This input data should contain information that can be used to predict a result given a different set of input data.
For example, when predicting house prices, it would need to know the suburb (category), number of bedrooms/bathrooms/parking spaces, age of the building and selling price. It could then predict the selling price for other houses with a slightly different mix of variables.
However, based on your screenshot, you are giving the following information (and probably more) on which to make your prediction:
Teams: Not great, because you are separating Column C and Column D. The model will assume they are unrelated information. It doesn't realise that those two values could be swapped.
Match date: Useless information unless the outcome varies in proportion to time (eg a team continually gets better)
Season: As with Match Date, this is probably useless because you're always predicting the future -- you won't be predicting for a past season
Venue: Only relevant if a particular team always wins at a given venue
Toss Decision: Would this really influence the outcome? Also, it's only known once the game begins, so not great for predicting a future game.
Win Type: You won't know the win type until a game is over, so it's not suitable for predicting a future game.
Score: Again, not known until the actual game, so no good for future predictions.
Man of the Match: Not known for future games.
Umpire: How does an umpire influence the result of a game?
City: Yes, given that home teams often have an advantage.
You have provided very little information that could be used to predict a future game. There is really only the teams and the venue. Everything else is either part of the game itself or irrelevant.
Picking only one of the two teams
When the ML model looks at your data and tries to make a prediction, it will look at all the data you have provided. For example, it might notice that for a given venue and season, Team 8 has a higher propensity to win. Therefore, given that venue and season, it will favour a win by Team 8. The model has no concept that the only possible outcome is one of the two teams given in columns C and D.
You are predicting for two given teams and you are listing the teams in either Column C or Column D and this makes no sense -- the result is the same if you swapped the teams between columns, but the model has no concept of this. Also, information about Team 1 vs Team 2 is totally irrelevant for Team 3 vs Team 4.
What you should do is create one dataset per team, listing all their matches, plus a column that shows the outcome -- either a boolean (Win/Lose) or a value that represents the number of runs by which they won (where negative is a loss). You would then ask them model to predict the result for that team, given the input data, which would be win/lose or a points above/below the other team.
But at the core, I think that your input data doesn't have enough rich content to be able to make a sensible prediction. Just ask yourself: "What data would I like to know if I were to guess which team would win?" It would probably be past results, weather conditions, which players were on each team, how many matches they played in the last week, etc. None of this information is being provided as input on each line of your input data.
I have a bit of a unique problem here. I currently have two warehouses that I ship items out of for selling on Amazon, my primary warehouse and my secondary warehouse. Shipping out of the secondary warehouse takes significantly longer than shipping from the main warehouse, hence why it is referred to as the "secondary" warehouse.
Some of our inventory is split between the two warehouses. Usually this is not an issue, but we keep having a particular issue. Allow me to explain:
Let's say that I have 10 red cups in the main warehouse, and an additional 300 in the secondary warehouse. Let's also say it's Christmas time, so I have all 310 listed. However, from what I've seen, Amazon only allows one shipping time to be listed for the inventory, so the entire 310 get listed as under the primary warehouse's shipping time (2 days) and doesn't account for the secondary warehouse's ship time, rather than split the way that they should be, 10 at 2 days and 300 at 15 days.
The problem comes in when someone orders an amount that would have to be split across the two warehouses, such as if someone were to order 12 of said red cups. The first 10 would come out of the primary warehouse, and the remaining two would come out of the secondary warehouse. Due to the secondary warehouse's shipping time, the remaining two cups would have to be shipped out at a significantly different date, but Amazon marks the entire order as needing to be shipped within those two days.
For a variety of reasons, it is not practical to keep all of one product in one warehouse, nor is it practical to increase the secondary warehouse's shipping time. Changing the overall shipping date for the product to the longest ship time causes us to lose the buy box for the listing, which really defeats the purpose of us trying to sell it.
So my question is this: is there some way in MWS to indicate that the inventory is split up in terms of shipping times? If so, how?
Any assistance in this matter would be appreciated.
Short answer: No.
There is no way to specify two values for FulfillmentLatency, in the same way as there is no way to specify two values for Quantity in stock. You can only ever have one inventory with them (plus FBA stock)
Longer answer: You could.
Sign up twice with Amazon:
"MySellerName" has an inventory of 10 and a fulfillment latency of 2 days
"MySellerName Overseas Warehouse" has an inventory of 300 and a fulfillment latency of 30 days
I haven't tried by I believe Amazon will then automatically direct the customer to the best seller for them, which should be "MySellerName" for small orders and "MySellerName Overseas Warehouse" for larger quantities.
I have a dataset including 1 million customers. They are splitted into some categories like electronics customers,food and Beverage customers etc. Group names present customers' profiles.
each customer has different behaviours. For instance suppose that an electronic customer buys one electronic devices at least when he goes shopping. This transaction repeats randomly or continuously. So that I present each transaction by numerical codes.
(Value of transaction, volume of trans., transaction type, etc..) = (100,200,1)
for each transaction I have this vector above.it means every customer has a different trade behaviour.
I want to find out whether each customer has a pattern? Do we have outliers?
it is a profiling problem basically.
which analysis do you recommend?
Can you be more specific? What are you trying to get out of the analysis exactly? Buying patterns, customers that are outliers, purchases that are outliers?
If you want to determine which items are bought together, group the transactions together, just listing the items purchased at the same time and do shopping basket analysis, using the apriori algorithm or similar.
If you want to find similar customers, using k nearest neighbor or k means against a vector representing a customer's buy patterns (probably just the items bought). You can do this on individual transactions also to compare transactions.
To determine outliers, you can use a density based clustering algorithm (e.g. DBSCAN) to cluster customers together that are close to one another, and look at those customers that are not in clusters to determine outliers also.
Suppose I want to do some data mining on the database of a supermarket. What does that actually mean?
1) What will the output/results be like?
2) Will the output be different every day or change over time?
3) Before applying data mining, do I need to know what I want or will data mining give everything I want automatically?
Data Mining is a general category of techniques that can be applied to different kinds of datasets, just like programming is a general category of techniques that can be applied using different languages to do different things.
None of your questions make any sense.
A1: Data mining will give us an accurate reports about your queries of database of supermarket.
A2: Sure, because Data mining depend on analyzing during time, in this case it depend on your problems or goals that you want to reach it. if your database was very big also you built data warehouse in right way you will get the different output over time.
A3: yes you should determine what are the problems you have to mine then use tools of Data mining to get the results or indicators automatically.
To answer your first question: For the case of supermarket customer data, I could image the following questions:
how many products X are usually sold on Fridays ?
(helps you to determine how many X you should have in stock)
which customers bought product X often in the last month/year ?
Useful when when you introduce a new X-like product: send advertising material (which has a given cost) only to those customers.
given a customer buys product X (e.g. beer) what's the probability that he/she also buys product Y (e.g. chips) ?
useful for the following: make sure X and Y never are on promotional offer at the same time (X and Y are bought together often). Get the customers into the store by offering a rebate on X knowing they'll also by Y at the same time. Or: put a high price X-like product right next to Y, putting the cheaper X somewhere else.
which neighborhoods have the smallest number of customers ?
helps to find out which neighborhoods you could target with advertising to bring more customers into the store.
Often, by 'asking certain questions to the data' one discovers some features and comes up with new questions.
Data mining is a set of techniques. It refers to discovering interesting and unexpected patterns in data.
If you want to apply some data mining techniques, you need to know which one and you should know why. The answer to questions 1, 2 and 3 depends on the techniques that you choose.
For example, if i want to find associations between items sold in a supermarket, i may use association rule mining. If i want to find groups of similar customers, I might use a clustering algorithm. etc.
There is not just ONE technique in data mining.
Having implemented an algorithm to recommend products with some success, I'm now looking at ways to calculate the initial input data for this algorithm.
My objective is to calculate a score for each product that a user has some sort of history with.
The data I am currently collecting:
User order history
Product pageview history for both anonymous and registered users
All of this data is timestamped.
What I'm looking for
There are a couple of things I'm looking for suggestions on, and ideally this question should be treated more for discussion rather than aiming for a single 'right' answer.
Any additional data I can collect for a user that can directly imply an interest in a product
Algorithms/equations for turning this data into scores for each product
What I'm NOT looking for
Just to avoid this question being derailed with the wrong kind of answers, here is what I'm doing once I have this data for each user:
Generating a number of user clusters (21 at the moment) using the k-means clustering algorithm, using the pearsons coefficient for the distance score
For each user (on demand) calculating their a graph of similar users by looking for their most and least similar users within their cluster, and repeating for an arbitrary depth.
Calculating a score for each product based on the preferences of other users within the user's graph
Sorting the scores to return a list of recommendations
Basically, I'm not looking for ideas on what to do once I have the input data (I may need further help with that later, but it's not the point of this question), just for ideas on how to generate this input data in the first place
Here's a haymaker of a response:
time spent looking at a product
semantic interpretation of comments left about the product
make a discussion page about a product, brand, or product category and semantically interpret the comments
if they Shared a product page (email, del.icio.us, etc.)
browser (mobile might make them spend less time on the page vis-à-vis laptop while indicating great interest) and connection speed (affects amt. of time spent on the page)
facebook profile similarity
heatmap data (e.g. à la kissmetrics)
What kind of products are you selling? That might help us answer you better. (Since this is an old question, I am addressing both #Andrew Ingram and anyone else who has the same question and found this thread through search.)
You can allow users to explicitly state their preferences, the way netflix allows users to assign stars.
You can assign a positive numeric value for all the stuff they bought, since you say you do have their purchase history. Assign zero for stuff they didn't buy
You could do some sort of weighted value for stuff they bought, adjusted for what's popular. (if nearly everybody bought a product, it doesn't tell you much about a person that they also bought it) See "term frequency–inverse document frequency"
You could also assign some lesser numeric value for items that users looked at but did not buy.