I need to find the handset model using IMEI number... There are some websites which provide this information for one or two IMEI in a day but I want some DB or webservice or API through which I can know the model of the phone using IMEI in bulk.. I have around 65000 IMEI number for which I want to know the model information and have to insert them in my DB.. Thanks in advance for your help..
What you really need is a Type Allocation Code (TAC) reference. The TAC is the first eight digits of the IMEI number and uniquely identifies the device. The other digits are the serial number (and version and check digit info).
However, there is not a publicly available database of TAC codes. New TAC codes come out almost daily, as new devices are registered and as new codes are issued for existing devices (for example, there are over 100 TAC codes for an especially popular device). You can only get a complete list if you are affiliated with a wireless carrier or a manufacturer. The codes are considered proprietary.
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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'm attempting to set up an OpenCart store for a client.
I'm getting the following error on the shipping page.
"Warning: No Shipping options are available. Please contact us for assistance!"
Research suggests that this error happens when there is a mismatch between the weight-class for the store and for the plugin, or something similar.
I've tried every combination of configuration settings that I can think of without result.
I'm not familiar enough with OpenCart to debug this issue. Where do I need to start looking?
Firstly you have to enable the shipping status and the values from admin panel shipping tab.After that you can get it in the front end.
My troubleshooting procedure:
The store weight UOM had been set to ounces.
The Fedex plugin doesn't support ounces as a weight UOM.
Nothing works.
The store weight UOM was changed to LBS.
Nothing works.
The package size was set to FedEx 10 KG Box
The Fedex plugin can't convert from Lbs to KG on the fly.
The package size was changed to "Fedex Box", without a weight class
Some products now working, all shipping estimates are WAY high.
When changing the default UOM for the store, no existing weights are converted in to the new units, although any weights stored without a unit are now read as being in the new unit.
This meant that the fedex system was trying to pull prices for items that "weighed" hundred of ounces (which it should have been able to do, even though those weights were incorrect.)
I updated the weights on all products to be in line with their unit of measure
At this point, the plugin was working for most, but not all, products, with reasonable accuracy.
I changed the plugin settings from List Rate to Account rate.
Now everything works.
To simplify - The fedex shipping plugin in opencart 1.5.6 will only work if:
All the products in the system have their weights stored in the same UOM.
That UOM is either pounds or kilograms (not ounces!)
A geozone is set, and a zip code is supplied(zip codes are important!)
The package size matches the unit of measure for the weight (no mixing kilograms and pounds!)
The product weights are actually correct
The account in question has a rate for a package of the indicated size
Hopefully someone else will find this helpful.
Ive been through this problem as well and I haven't yet fixed it completely.
But in my particular problem, I had a syntax error in the XML retrieved by the Fedex server after the cURL request.
Printing the $response variable I could find some good hints about some of the problems, for example, commas (,) instead of dots (.) to refer to decimal numbers and decimal numbers where it was expecting an integer.
So var_dump($response) could help some people find their specific issues.
I am making an application to secure barcodes so the patients at hospitals cant read them. But i want to hear your perspective on it.
First of all im making this program in C++.
My idea is to make a barcode and secure it for patients at hospital for a application which i am developing (secret sorry guys).
The way i am going to secure it is to get the following information about the patients.
Their unique ID nr (its possible in Denmark to do that)
Their room nr which they are staying at
Their Patient nr (the patients have a specific patient number)
The unique ID nr is containing 10 chars. The room nr contains 2 digits and the Patient nr contain 10 digits.
If I am predefining each chars and digits to random numbers and letters, and if I for example take 1 char from the ID nr and generate it to 5 chars in my secured code (or maybe after I have generated the new code I will secure it afterwards with a AES code or something), would that be a good way to secure the barcode, so the patient cant read the BarCode and get information of it?
The problem is that the patient must not scan the other patients barcode and retrieve information out of it, because that will be a big problem.
Please don't do what you are suggesting!
Instead, use a surrogate key (barcode) which is held securely in a database (and by that, I mean carefully check all relevant laws in your jurisdiction).
Don't try and be clever and 'encrypt' any info into the key. It will come back to bite you.
I am designing the primary key for storing product. I look around to find some insight how to design the ID as using auto increment is too boring. Do any one know that the code 'KB46279860I' on the below banknote meaning?
100 USD picture
I think that code is not just using auto-increment but some algorithm like check digit,etc.
Could any one give me some hints , Thanks!!
If you're not planning on showing the user your ID then auto-increment could save you processing time as it is handled by your database directly.
If you are planning on showing the ID to the user without showing the one in the database, you could consider using Hashids, or GUID or generating your own unique random value with a check digit. You can use Luhn or Damm's algorithm for check digit.
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.