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Search documentation... Support Dashboard Card Payments Quickstart Securely collect card information from your customers and create a card payment. Supported cards Users in the United States can accept Visa Mastercard American Express Discover JCB Diners Club credit and debit cards. Stripe also supports a range of additional payment methods, depending on the country of your Stripe account. Accepting a card payment using Stripe is a two-step process, with a client-side and a server-side action: From your website running in the customer’s browser, Stripe securely collects your customer’s payment information and returns a representative token. This, along with any other form data, is then submitted by the browser to your server. Using the token, your server-side code makes an API request to create a charge and complete the payment. Tokenization ensures that no sensitive card data ever needs to touch your server so your integration can operate in a PCI compliant way. Step 1: Securely collecting payment information Checkout reference Complete information about available options and parameters is provided in the Checkout reference. The simplest way for you to securely collect and tokenize card information is with Checkout. It combines HTML, JavaScript, and CSS to create an embedded payment form. When your customer enters their payment information, the card details are validated and tokenized for your server-side code to use. To see Checkout in action, click the button below, filling in the resulting form with: Any random, syntactically valid email address (the more random, the better) One of Stripe’s test card numbers, such as 4242 4242 4242 4242 Any three-digit CVC code Any expiration date in the future To get started, add the following code to your payment page, making sure that the form submits to your own server-side code: <form action="your-server-side-code" method="POST"> <script src="https://checkout.stripe.com/checkout.js" class="stripe-button" data-key="pk_test_2DtHIU1N9li5GpmJjyxkQMHh" data-amount="999" data-name="Demo Site" data-description="Example charge" data-image="https://stripe.com/img/documentation/checkout/marketplace.png" data-locale="auto"> </script> </form> We’ve pre-filled the data-key attribute with your test publishable API key—only you can see this value. When you’re ready to go live with your payment form, you must replace the test key with your live key. Learn more about how the keys play into test and live modes. Although optional, we highly recommend also having Checkout collect the user’s ZIP code, as address and ZIP code verifications help reduce fraud. Add data-zip-code="true" to the above and enable declines on verification failures in your account settings. You can also set Checkout to collect the user’s full billing and shipping addresses (using the corresponding parameters). Requiring more than the minimum information lowers the possibility of a payment being declined or disputed in the future. Any fraudulent payments that you process are ultimately your responsibility, so requiring a little more than the minimum amount of information is an effective way to combat fraud. Radar, our modern suite of fraud protection tools, is only available to users who have implemented client-side tokenization. By doing so, it ensures that you can pass the necessary data required for our machine-learning fraud prevention models to make more accurate predictions. The amount provided in the Checkout form code is only shown to the user. It does not set the amount that the customer will be charged—you must also specify an amount when making a charge request. As you build your integration, make sure that your payment form and server-side code use the same amount to avoid confusion. An alternative to the blue button demonstrated above is to implement a custom Checkout integration. The custom approach allows you to use any HTML element or JavaScript event to open Checkout, as well as be able to specify dynamic arguments, such as custom amounts. Stripe.js and Elements If you’d prefer to have complete control over the look and fel of your payment form, you can make use of Stripe.js and Elements, our pre-built UI components. Refer to our Elements quickstart to learn more. Mobile SDKs Using our native mobile libraries for iOS and Android, Stripe can collect your customer’s payment information from within your mobile app and create a token for your server-side code to use. Step 2: Creating a charge to complete the payment Once a token is created, your server-side code makes an API request to create a one-time charge. This request contains the token, currency, amount to charge, and any additional information you may want to pass (e.g., metadata). curl Ruby Python PHP Java Node Go .NET curl https://api.stripe.com/v1/charges \ -u sk_test_fyzWf8eDyljIob76fMVSwIsi: \ -d amount=999 \ -d currency=usd \ -d description="Example charge" \ -d source=tok_6Pk6W3hFiGB7dyNavdvyrFkM These requests expect the ID of the Token (e.g., tok_KPte7942xySKBKyrBu11yEpf) to be provided as the value of the source parameter. Tokens can only be used once, and within a few minutes of creation. Using this approach, your customers need to re-enter their payment details each time they make a purchase. You can also save card details with Stripe for later use. Using this method, returning customers can quickly make a payment without providing their card details again. Next steps Congrats! You can now accept card payments with Stripe using Checkout. You may now want to check out these resources: Creating charges Getting paid Managing your Stripe account Supported payment methods Saving cards Questions? We're always happy to help with code or other questions you might have! Search our documentation, contact support, or connect with our sales team. You can also chat live with other developers in #stripe on freenode. Was this page helpful? Yes No

In developmental robotics, robot learning algorithms generate their own sequences of learning experiences, also known as a curriculum, to cumulatively acquire new skills through self-guided exploration and social interaction with humans. These robots use guidance mechanisms such as active learning, maturation, motor synergies and imitation. Association rules Main article: Association rule learning See also: Inductive logic programming Association rule learning is a rule-based machine learning method for discovering relationships between variables in large databases. It is intended to identify strong rules discovered in databases using some measure of "interestingness".[60] Rule-based machine learning is a general term for any machine learning method that identifies, learns, or evolves "rules" to store, manipulate or apply knowledge. The defining characteristic of a rule-based machine learning algorithm is the identification and utilization of a set of relational rules that collectively represent the knowledge captured by the system. This is in contrast to other machine learning algorithms that commonly identify a singular model that can be universally applied to any instance in order to make a prediction.[61] Rule-based machine learning approaches include learning classifier systems, association rule learning, and artificial immune systems. Based on the concept of strong rules, Rakesh Agrawal, Tomasz Imieliński and Arun Swami introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (POS) systems in supermarkets.[62] For example, the rule {\displaystyle \{\mathrm {onions,potatoes} \}\Rightarrow \{\mathrm {burger} \}}\{{\mathrm {onions,potatoes}}\}\Rightarrow \{{\mathrm {burger}}\} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. Such information can be used as the basis for decisions about marketing activities such as promotional pricing or product placements. In addition to market basket analysis, association rules are employed today in application areas including Web usage mining, intrusion detection, continuous production, and bioinformatics. In contrast with sequence mining, association rule learning typically does not consider the order of items either within a transaction or across transactions. Learning classifier systems (LCS) are a family of rule-based machine learning algorithms that combine a discovery component, typically a genetic algorithm, with a learning component, performing either supervised learning, reinforcement learning, or unsupervised learning. They seek to identify a set of context-dependent rules that collectively store and apply knowledge in a piecewise manner in order to make predictions.[63] Inductive logic programming (ILP) is an approach to rule-learning using logic programming as a uniform representation for input examples, background knowledge, and hypotheses. Given an encoding of the known background knowledge and a set of examples represented as a logical database of facts, an ILP system will derive a hypothesized logic program that entails all positive and no negative examples. Inductive programming is a related field that considers any kind of programming language for representing hypotheses (and not only logic programming), such as functional programs. Inductive logic programming is particularly useful in bioinformatics and natural language processing. Gordon Plotkin and Ehud Shapiro laid the initial theoretical foundation for inductive machine learning in a logical setting.[64][65][66] Shapiro built their first implementation (Model Inference System) in 1981: a Prolog program that inductively inferred logic programs from positive and negative examples.[67] The term inductive here refers to philosophical induction, suggesting a theory to explain observed facts, rather than mathematical induction, proving a property for all members of a well-ordered set. Models Performing machine learning involves creating a model, which is trained on some training data and then can process additional data to make predictions. Various types of models have been used and researched for machine learning systems. Artificial neural networks Main article: Artificial neural network See also: Deep learning An artificial neural network is an interconnected group of nodes, akin to the vast network of neurons in a brain. Here, each circular node represents an artificial neuron and an arrow represents a connection from the output of one artificial neuron to the input of another. Artificial neural networks (ANNs), or connectionist systems, are computing systems vaguely inspired by the biological neural networks that constitute animal brains. Such systems "learn" to perform tasks by considering examples, generally without being programmed with any task-specific rules. An ANN is a model based on a collection of connected units or nodes called "artificial neurons", which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called "edges". Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer) to the last layer (the output layer), possibly after traversing the layers multiple times. The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis. Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition.[68]

This is the Facial Expression Recognition Model built with TensorFlow , Keras , OpenCV in Python 3. The IDE used was Juypter Notebook hence the codes will be in the ipnb (i python note book) format . Uses Anaconda with Tensorflow enviorment as the backend. Used 8 layered Convolutional Neural Network (CNN)

Sentiment analysis is the process of understanding, extracting and processing data textual automatically to get the sentiment information contained in a sentence of opinion expressed in the form and group them into two groups: positive opinions and negative opinions. The data is used from previous project with lexicon, based on positive and negative sentiments. Several processes in this project are divided data into training and test data with ratio 90:10, calculated the word weight with Term Frequency-Inverse Document Frequency (TF-IDF), use Support Vector Machine on training data to produce the classification model then tested on test data.