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In  statistics , the  generalized linear model  ( GLM ) is a flexible generalization of ordinary linear regression  that allows for response variables that have other than a  normal distribution . The GLM generalizes linear regression by allowing the linear model to be related to the response variable via a  link function  and by allowing the magnitude of the variance of each measurement to be a function of its predicted value.  in many cases when the response variable must be positive and can vary over a wide scale, constant input changes lead to geometrically varying rather than constantly varying output changes

In  statistics  and, in particular, in the fitting of  linear regression  models, the  elastic net  is a regularized regression method that combines the L1 and L2 penalties of the  lasso  and  ridge  methods.

Lasso  a regularization technique that's useful for feature selection and to prevent over-fitting training data. It works by penalizing the sum of absolute value (L1 norm) of weights found by the  regression.  

Want something that is potentially human comprehensible? Use decision trees or rules. Have a situation where you have lots of memory, but have to learn incrementally and evaluate quickly? Use Nearest Neighbour. Have a clear binary decision in a continuous space? Use SVMs. Have thousands of independent attributes and lots of data? Use Naive Bayes. Have a situation where you know which attributes are correlated with which? Use Bayes nets.

ML algorithms are an evolution over normal algorithms. They make your programs "smarter", by allowing them to automatically learn from the data you provide. You take a randomly selected specimen of mangoes from the market ( training data ), make a table of all the physical characteristics of each mango, like color, size, shape, grown in which part of the country, sold by which vendor, etc ( features ), along with the sweetness, juicyness, ripeness of that mango ( output variables ). You feed this data to the machine learning algorithm ( classification/regressio n ), and it learns a model of the correlation between an average mango's physical characteristics, and its quality.  Next time you go to the market, you measure the characteristics of the mangoes on sale ( test data ), and feed it to the ML algorithm. It will use the model computed earlier to predict which mangoes are sweet, ripe and/or juicy. The algorithm may internally use rules similar to the rules y...

Credit cards with rewards generally target people who spend a lot on credit cards. That high transaction volume is what makes it viable for issuers to provide the rewards. Rewards  are drawn from the in terchange revenue that issuers get from merchants.    To sup port those high transaction volumes, rewards cards generally have a high credit limit. Lower limits do not make economic sense. These cards are generally offered to people with very good credit as a result.    People with no credit history are generally (but not always) considered to be higher risk by issuers, which translates to lower credit limits, which squeezes out rewards propositions. 

While there are generic FICO scores used for all types of lending,  there also are FICO scores for specific types of lending designed for mortgage lending and insurance purposes. Further, there are FICO scores for specific types of lenders, such as credit unions or traditional banks. Adding to that, there are FICO credit scores created for specific lenders, called custom scores. FICO is a good company, and it produces good credit scores. But it also has competitors, including Scorex, a credit scoring company owned by Experian, that also produce credit scores in much the same way. There are also lenders that produce their own credit scoring systems

The way to solve this is to save the customer’s card number, verify  that it’s legitimate, and then charge all the customers once the deal’s  critical mass has been achieved. However, storing card numbers is a pain because if the card numbers  are stolen from your servers you can face fines which can easily exceed  $millions of dollars for a large breach.  Also, to get a a merchant account with a credit card processor you will need to sign a document telling them that you are PCI compliant.  Being PCI compliant ( http://feefighters.com/b log/easy... ) is pretty easy if you don’t store card numbers, but if you do wish to store card numbers it requires security audits. You can get most of the benefits of storing card numbers locally without worrying too much about PCI by using tokenization ( http://feefighters.com/b log/cred... ). You also have 2 options with respect to verifying the card number before storing it as a token. Do a quick algorithmic check us...

American Express has always charged higher merchant interchange rates than Visa, MasterCard and Discover.  They justify these rates to merchants by claiming to have a higher end card holder base that spends more money with retailers and is, therefore, worth more to the retailer. PayPal pioneered the simple blended interchange cost structure for merchant accounts.  The theory was that merchant fees were excessively complicated and unfriendly to merchants, with different rates for different categories of goods of services or different card types, etc.   PayPal took the merchant friendly route and offered merchants a simple to understand blended fee structure.  It is a much more merchant friendly form of pricing, hands down. The problem with PayPal's simple blended average pricing for Amex was that it undercut Amex's pricing in the market.  It became cheaper to accept Amex through your PayPal account than it was to go direct to Amex.  This was obviou...

Every credit card rewards program is structured differently.   You receive a cashback bonus of 1%, 2%, or 5% depending on where you shop. These rewards are generally thought of as being funded by a component of the fees that a merchant has to pay to accept credit cards calle d interchange or merchant discount. This fee can be fixed or variable and can vary based on the merchants' negotiated agreement with an acquiring bank/credit card processor, with a nationwide average of 1.79%. Usually it is the sponsor of the program who  pays for credit card bonus points , that could be either the bank behind the credit card (in case the card is not affiliated to a brand) or the co-branding company (such as the store or airline, etc). Fraction of the revenue is deferred until the points are redeemed in the future. The expense associated with the program are under the marketing budget.

(1)  The Merchant POS is the in-st ore scanner or shop ping cart. Often they will help to do the initial collection of  payment data. Online examples include Shopify, Magento, Drupal, and Big Platform. The Brick and Mortar POS landscape is dominated by NCR, IBM, HP, and Verifone - and Square is quickly emerging. For the most part, merchants hold an 'Approved Merchant Account (Token)' however many e-commerce platforms are now providing this as a service. Fees are usually based on a monthly fixed cost, a per-transaction fee, and an overall percent of transaction fee. Generally the POS (or sometimes Gateway) charges the merchant a single fee that covers all the downstream costs. (2)  The payment gateway is a secure connection between a POS and Processor. Historically, processor connections required such high security that it was not economical for a POS or merchan t to implement themselves. Of late, these costs have come down and we now see the integration of POS, Gateway...

(1)A customer completes a transaction at the merchant POS. POS system then passes the transaction, card, and merchant bank info to the Gateway (2) The Payment Gateway collects transaction, card, and merchant data from the Gateway and passes it securely to the linked Payment Processor (3) The Payment Processor collects the Gateway data and identifies the correct Network/Association based on the card type. Processor then routes all the transaction data to the Network. (4) The Network collects the transaction information and verifies the available balance with the Card Issuer*. In addition to the balance check, the network performs a security check to ensure the card is within normal spending patterns. If the transaction is approved by Card Issuer and passes the security check, the Network passes an affirmative response upstream to the POS and downstream to Card Issuer**. During Settlement***, network collects transaction amount from Card Issuer account and passes it to the Merchant Acco...

Firstly,  Square (product)  is not a replacement but a supplement to credit card payments and therefore it is still part of the traditional credit card payment system. Square enables users with iPhone, iPod Touch or iPad to easily accept payments and removes the hassles of signing up for a merchant account, buying a point of sale terminal and credit card reader. After you sign up with Square [ www.squareup.com ] they send you a free credit card reader. You will then need to download the Square app [ http://itunes.apple.com/ us/app/s...   from the  App Store , open the app and plug the Square credit card reader in the headphone jack to accept credit card payments. Square charges 2.75% towards transaction fees. Banks and payment processing service providers that offer merchant solutions usually charge a flat fee and varying percentage (1% to 5%) of the transaction value in addition to a monthly fee. Square does not charge flat fee or monthly fees. ...

If  you are interested in marketing credit cards with your brand, big banks have programs for issuing "affinity" and "co-branded" credit cards.  Examples include cards that are branded by charities (e.g., Nature Conservancy), colleges/universities, consumer brands (e.g., MLB baseball teams).  The issuers then pay the affinity groups or brands for the use of the brand and often a fee for each new account.  Larger brands often work with banks to create reward credit cards that offer rewards connected with credit card (e.g. airline credit cards with airline miles).   Chase Paymentech is Square’s acquirer. So Square pays Chase Paymentech (subsidiary of JP Morgan, who is the de-facto Acquiring Bank) for gateway fees, processing fees, card scheme fees, and interchange fees. Square's business model is very much like Paypal's business model but probably with lesser risks because Square accepts card-present payments and one would likely see less charge...

Logistic regression   does not make many of the key assumptions of  linear regression  and  general linear models  that are based on ordinary least squares algorithms – particularly regarding linearity, normality, homoscedasticity, and measurement level. Besides that, one of the assumptions of regression is that the variance of Y is constant across values of X (homoscedasticity), which cannot be the case with a binary variable, because its variance is p(1-p). Suppose 50 percent of the people are 1s, then the variance of .25 would be its maximum value. As  we move to more extreme values, the variance decreases, for example when p =.10, the variance is .1*.9 = .09, so as p approaches 1 or 0, the variance approaches 0.   Firstly, it does not need a linear relationship between the dependent and independent variables.  Logistic regression can handle all sorts of rela...