The past week i was pushed into data analytics and somehow i rolled up to find the best analysis to do with B2B.
Most companies in the B2B space are using web analytics to track the basics; page views, visitors, bounce rate, and similar metrics. Today, that is the low bar. The true value that can be extracted from the way in which customers interact with a site isn’t lurking in an obscure Google Analytics report. It’s buried in the wealth of data that can be collected from customer interactions. For companies both small and large, the world of data science can be intimidating and knowing where to start can leave one’s head spinning. But ignore this at your own peril. OK, so a full discussion of data science is beyond what’s possible in a blog, but here are some key points that all in the B2B space should know:
Customer classification :Understanding behavioral tendencies of customers and grouping them with similar customers can be an effective way to focus marketing and merchandising efforts. One classification that most have likely seen is the “VIP Customer” but too often, B2B companies simply decide, using a single metric, what constitutes a VIP. One implementation I’ve seen simply classifies any customer that purchased over $100 as a VIP and placed a little badge on the customer’s online profile. That’s not really classification; it’s closer to gamification which gives the customer some sort of sense of achievement and hopefully boosts their loyalty. Real classification is based on a set of data features measured across all recent customer activity. In other words, whether someone is a real VIP customer or not, is going to change when compared to the behavior of the entire customer base. Knowing customer classification and being able to use a classification model to predict a group to which a new customer is likely to belong, can help greatly in determining which promotions to show to a customer while they’re on the site or via email. In a recent machine learning implementation we did for a B2B firm, we grouped customers into several segments by using data from a recent time period:
It’s also important to note that models aren’t static. They must be recalculated frequently. In the recent implementation, the models would recalculate every few hours to ensure that they were as current and as accurate as possible. Prescriptive Product and Content Placement :There are many reasons buying behavior may change. Seasonality is one such reason. Other reasons that may drive short term behavioral changes are weather, news, shortages, and manufacturer promotions. Using models to detect conditions that are “outside of expectations” and adjust site merchandising to respond to time sensitive anomalies are becoming more commonplace. This technique can be used to alter search results, home page item placement, category item placement, and guide users to product information when a search for a competitive product is conducted. Does a given product perform better when visible at the top of the home page? Some do, some don’t. Being able to detect optimal placement for products given a recent history of activity such as conversions and cart additions can be accomplished through machine learning. When introducing a new product, similar models can be used to predict a best-placement for optimal performance based on product attributes. Improving Personalization :Personalization on most B2C sites has a long way to go. And on B2B sites, it’s still in its infancy. While it’s possible to group shoppers into a cohort and show them similar items, machine learning makes it’s possible to make these cohorts smaller, approaching a more unique experience for your B2B customer. This can be especially impactful when there are multiple B2B buyers from a single customer. To begin moving toward a more unique experience, machine learning models can be developed starting at a high-level, then progressing to more granular levels that deliver unique insights into a customer. For example, beginning with geolocation as a factor, a B2B seller of industrial HVAC equipment should feature different products for customers in Minnesota vs. those in Florida. The buying seasons are not only different, but events in Florida such as an impending hurricane may influence buying behavior in the short term. A properly designed model can help spot these changes and alert the B2B marketer to changes that may require a change in site merchandising for a geographical region. To add on, a company could develop models that can predict the optimal sort order for search results for a customer, the ideal products and categories to feature for them, along with suggested promotions based on their recent behavior. In Summary :It’s a fascinating time to be working in commerce and with data in particular. The convergence of low-cost cloud-based computing and the abundance of data available from a wealth of sources (not the least of which is a company’s B2B site) and provide a lot of actionable intelligence with an investment orders of magnitude lower than a decade ago. That not only puts this technology within reach, it positions it to become a core part of how you relate to your customers and how you operate your business. Those that ignore the call today, may likely be tomorrow’s Borders. Hello all there !!!!
I've just want to present my new work on scrapping. This is not too late for the scrapping, the modern age mechanism to steal data. When i started to do so : I always have high personal context awareness, when stealing from websites like amazon you need to take more care on your privacy. Things i gone through while started scraping is :
They are clever, Really clever. We do wanna show us clever. There exist several proxies in the world and change IP like flying from Pakistan to America in second. ("Not even Superman can"). Sounds Great :) BREAK THE FIREWALL - AMAZON:
2. Strip "tracking" query params from the URLs to remove identifiers linking requests together
Decided to dive deeper into Python - My code for achieving this. I don't wanna explain my code here, I already gave enough definition in code. follow the steps and be careful about your privacy. This tutorial is completely for Education purpose. Scrapping is Illegal and i don't promote it. Thank you - SHREE THAANU We all know that Jack ma " The man behind the Online marketing " , Pioneer of the Online marketing and E Commerce.
Alibaba is the world's largest and most valuable retailer since April 2016, after it surpassed Walmart, with operations in over 200 countries, as well as one of the largest Internet companies. Its online sales and profits surpassed all US retailers (including Walmart, Amazon and eBay) combined since 2015. It has been expanding into the media industry, with revenues rising 3-digit percents year on year. It also orchestrated China's Singles' Day into the world's biggest online and offline shopping day, with its own sales reaching over US$25.4 billion on 11 November 2017. So, what should I do now to make something new in the market, " ARTIFICIAL INTELLIGENCE " Now i will make shopping more cool than ever, Almost its been 6 months that i started working on this huge mission, Bringing world closer with technology, now its time for artificial intelligence to conquer the M COM market. As discussed my jarvis would play the key role in this mission " A well trained AI ". PIONEERING ARTIFICIAL INTELLIGENCE IN M-COMMERCE MARKET - A moment of pride for Online Marketing My IOS Application is powered with artificial intelligence, that could detect the objects and bring it to you from our store. Its a bit surprising and i 'm not gonna leave as of that and my another mission of pattern recognition Algorithm to provide you the similar kind of clothes, and the AI acts as a fashion designer for you and you are not gonna shop alone in online. All you wanna do is to feed your interest and will provide you the related dress in future, Simply AI. The goals is :
Any the beauty is I patented it :) -SHREE THAANU 👨💻 "Will it be easy ? Nope! Worth it ? Absolutely" WWDC16 simply finished and Apple left us with new astounding imaginative APIs. This year discourse acknowledgment, proactive applications, machine learning, client expectations, and neural systems have been the most successive terms utilized amid the gathering. In this way, other than another rich variant 3 of Swift, practically every new expansion to iOS, tvOS and macOS is connected with manmade brainpower. For instance, Metal and Accelerate in iOS 10 give an execution of convolutional neural systems (CNNs) for the GPU and CPU individually. Amid the keynote, Craig Federighi (Apple's SVP Software Engineering) indicated how the Photos application on iOS sorts out our photographs as per distinctive keen criteria. He featured that Photos application utilizes profound figuring out how to give such usefulness. Additionally, Federighi indicated how Siri, now accessible to engineers, can propose what we require.
Late advances in machine learning joined with the expansion of the GPU processing power and the reduction of their expenses can help us to play out the above characterization issue (and not just that) utilizing a unique sort of calculations known as Artificial Neural Networks (or in a matter of seconds, ANNs or NNs). An ANN is a numerical apparatus we can use to take care of the grouping issue in a practically programmed way. Neural systems are not new to established researchers and industry, but rather they were practically deserted, in view of the gigantic measure of calculations they required. At the point when the expenses of the Graphics Processing Units (GPUs) began to drop, researchers and engineers reevaluated neural systems. Particularly the work done amid the previous 4 or 5 years and the presentation of another kind of ANNs, known as Convolutional Neural Network (CNNs), has upheld the advancement of new applications in light of neural systems. This opened likewise another field of Machine Learning known as Deep Learning. The figure below highlights an ANN. For the moment, think of it as a black box. Later, we will see how the ANN looks like and how to implement it in Swift. Neural Network OverviewAs you can see in the above picture, the ANN has a single input (an image of a person) and 7 outputs. The number of inputs and outputs depends on the particular classification problem we want to solve. For each input image we provide to it, the neural network will tell us who the person in the image is. Of course, an ANN cannot be 100% accurate. Indeed, a neural network provides you with an estimation of the likelihood that the input image shows the person Steve. In the previous picture, this likelihood is represented by a number between zero (likelihood = 0%) and one (likelihood = 100%). How does the ANN compute this likelihood? It learns from data through a process known as training. In this process, we provide the ANN with labelled examples. For each labelled example, we force the ANN to adjust its internal states. The process works in the following way. You present a labelled image to the ANN. Then, the ANN computes the likelihood for each of its output. Now, if you provide an image of the person Steve, you would expect that the probability of the output “Steve” would be almost one, while the probability of the remaining outputs would be almost zero. However, since the ANN is starting to learn now and it really doesn’t know who Steve is, it can provide an erroneous result. So, we compute the error between the expected result (Steve = 1, other people = 0) and the actual result. Then, using an algorithm known as back propagation, we inject this error back in the ANN through its outputs. The error is then propagated back until it reaches the ANN’s input. The back-propagation process changes the internal states of the ANN. Once this is done, we present a new photo to the ANN and wait for the neural network to compute the new results. Then, we repeat this process many times until the output error does not change anymore. At that point, we say that the neural network has converged. We stop the training here and we collect the internal states (represented by a large data set of floating point numbers called weights - see later). This data set is what then we use in the final application. If you want to make your app able to classify images, sounds, or any type of digital signal, you need a neural network and the back-propagation algorithm. The back-propagation process is automatic, but it is very computational expensive and extensive. Here is where GPUs become really helpful. They can enormously reduce the training time. So, instead of spending days to train the neural network, you can use arrays of GPUs and reduce the training time to just few hours. Besides the processing time, you need to prepare many labelled samples. There are ways to generate these labelled samples automatically, but this is not always possible. So, in some cases you need to collect the data and then, manually label them one by one. These labeled data are also known as the training set. There are some studies about how large the training set should be to train a neural network, but I am not going to cover these details here. As I mentioned above, in iOS 10, macOS 10.12, and tvOS 10, the Metal and Accelerate frameworks will provide developers with an implementation of a Convolutional Neural Network. Unfortunately, the training algorithm will not be available. So, you would need to train your neural network using third-party tools or you have to develop them by yourself. You have different alternative solutions. For example, you can use TensorFlow from Google or Caffe from Berkley Vision and Learning Center. Other commercial or open-source solutions are also available on the market. INVASIVECODE, developed the training algorithms based on Metal and Accelerate framework. Additionally, because we are expert in computer vision and pattern recognition, we can preprocess your image or audio data and prepare them for the neural network. Our convolutional neural network supports iOS 8, iOS 9 and iOS 10. Neuron, Synapsis and LayerLet’s see the main components of an artificial neural network. For sure, the most important component is the Neuron or Node. An ANN can contain hundreds or thousands of neurons organized in Layers. Each neuron is connected to other neurons through Synapses or Connections. The following image highlights an ANN with four layers (L0, L1, L2, and L3). Example of a neural networkEach ANN can contain any number of layers and each layer can contain any number of neurons. The first layer (L0 in the previous figure) is known as the input layer. The last network layer (L3 in the previous figure) is the output layer. Additional layers between the input and output layers are known as hidden layers. The ANN represented in the previous figure is also a fully-connected neural network (FCNN), because each neuron of a hidden layer is connected with every neuron of the previous and next layers. FCNNs are convenient for practical implementations, but you can also have NN were each neuron is connected only to some of the neurons of the previous or next layer. Synapses and neurons apply transformations to the input data. The data flow from the input layer to the first hidden layer; then, from the the first hidden layer to the second hidden layer, and so on, until they reach the output layer. Each synapsis input is the output of a neuron. In the same way, the synapsis output becomes the input of a neuron. A synapsis multiplies its input for a value known as weight. Each connection has its own weight. During the neural network training process, the back-propagation algorithm modifies the value of each weight of each synapsis. Synapses can be implemented in Swift in a very simple way: struct Synapsis { var input: Double var weight: Double var output: Double { return input * weight } } Neurons perform two sequential operations on its inputs (see figure below):
A NeuronIf we want to build a neuron in Swift, it is very simple too: struct Neuron { var inputs: [Double] var output: Double { let sum = inputs.reduce(0, combine: +) //1 return activate(value: sum) //2 } func activate(value v: Double) -> Double { return 1.0 / (1.0 + exp(-v)) //2 } } Line 1 computes the sum of the neuron’s inputs. Instead of using an initial value zero, it is very common to use a value different from zero, known as the bias of the neuron. Line 2 applies the activation function to the previously computed sum. There are different types of activation functions proposed by scientists. In line 2, I am using a sigmoid function (see next figure). Sigmoid functionOther activation functions proposed in the literature are the hyperbolic tangent, the rectified linear unit (ReLU), the leaky rectified linear unit (leaky ReLU), the exponential linear unit (ELU). The following code snippet shows the implementation of the most important activation functions: enum ActivationFunction { case sigmoid case tanh case reLU case leakyReLU case eLU } func activate(value v: Double, using activationFunction: ActivationFunction) -> Double { switch activationFunction { case .sigmoid: return 1.0 / (1.0 + exp(-v)) case .tanh: return tanh(v) case .reLU: return max(0.0, v) case .leakyReLU: return max(0.01 * v, v) case .eLU: return (v > 0) ? v : 0.01*(exp(v) - 1.0) } } With some little extra code you can have a fully functional neural network. ApplicationsSo, what can you do with an artificial neural network? The number of applications is enormous. If you think carefully about the classification problem I described above, you will find out that this type of issues are very frequent in many applications. For example, face recognition is a classification problem. Or, if you want to let the machine understand your mood just looking at your facial expression, this is also a classification problem. Another typical application is handwriting recognition. Navigation assistance algorithms use CNNs to decide if the object moving in front of the car is a pedestrian or another car. Neural networks are used nowadays in many fields: military, health, social, risk management, traffic, entertainment and so on. ConclusionsMachine learning and computer vision open new kind of applications for iOS, tvOS and macOS devices. Smarter, more user-friendly and convenient applications are already starting to appear on mobile devices. But we are just at the beginning. And to make it even more convenient for you, we have developed preprocessing and training algorithms that will be available for purchase very soon. Stay tuned! SOURCE : https://www.invasivecode.com -SHREETHAANU "Machine learning is a core sub-area of artificial intelligence as it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, computer programs, are enabled to learn, grow, change, and develop by themselves " This is my first post for Machine learning, I've started this in python and later preferred swift, for development.This time i've bowed my way for the future of technology 👨💻 #Artificial_intelligence. The data model i used will identify objects with different aspects and provide you with the higher probability result. This dataset Detects the dominant objects present in an image from a set of 1000 categories such as trees, animals, food, vehicles, people, and more. The app i've done is fairly simple and lets user either take a picture of something or choose a photo from their photo library. Then, the machine learning algorithm will try to predict what the object is in the picture. The result may not be perfect for will match the maximum probability. This Machine learning feature is been implemented in my previous IOS application to make it a bit spicy, the link to view my Jarvis IOS App is here shreethaanu.weebly.com/blog/jarvis-central-your-personal-assistant. Model Name: Inception v3 Description: Detects the dominant objects present in an image from a set of 1000 categories such as trees, animals, food, vehicles, people, and more. The top-5 error from the original publication is 5.6%. Tools : Xcode Language : Swift Framework : Core ML Source :developer.apple.com/machine-learning/ Sometimes when you innovate, you make mistakes. It is best to admit them quickly, and get on with improving your other innovationsWhen i used to tell people about my life failure i used to mention this " You can see the calmness on my face not the storm on my mind ", Actually thats the thing inspired me and brought me here.
Never wait for the time to reach you, because this is your life. put on your burdens and carry it with your Journey. They all comes because you should have something to say people after your being successful. Each and every successful people came through the problems, when you are ready to face it you gained a step forward. Another excuse people said to me is " GOD ", merciless god they used to mention, This going too funny i used to say, If your have that much hope on god, and not on your skill surely your are not gonna be successful. Believe in you, you are the master of yourself. Think that you have the courage to push yourself forward, not the god not the people, not me. Stop complaining time and god and start working thinking that, you are about to change the world. Your technology, your vision and you are the one whose gonna recognised by the people i future. If you cannot grab the opportunity, Well good. Move forward and create the opportunity. Never sit dump after your first failure because there are few more steps to get failed. Who came successfully without a bunch of failure. Try hard and bring the real you. When i get failed for the first time, some people laughed and for the second time a few more and for the n'th time a bunch of idiotic joined and they waited for the next failure, and i was too busy making out to be successful. They used to talk about failure only til your success. When i proved myself strong and bold, to overcome the failure and made out my first Successful product i don't had haters i had followers. " It all started with a small dream and a lot of self confidence " A must for being successful, Don't work for 8 hours for a company then go home and not work on your own goals. You are not tired , you are Un Inspired - Recently i to fitted in this quote, after a long wait i too entered in a small scale company with a job that i wished. After few months i realised a thing that i'm not living the lifestyle i wanted and not the goal in me has moved forward, So i just scheduled my self to work on what i wished. I'm not a Successful guy for saying this, But been highly experienced in failure and overcoming them. Stop making excuses and procrastinating. You want it? Go for it. Holding off for "maybe later", "tomorrow", or the famous excuse"I want to but I'm just not ready right now" No one is ever truly ready for anything. Being ready is being courageous, You have to have the courage to overcome doubt and fear of failure Only way to do that is to have confidence in yourself that you will figure it out and if you learn a few lessons on the way your that much closer to success! Don't wait until tomorrow. Do it now! #motivation - SHREE THAANU Hello guys, Its been a long time !! Again am back in coding with swift, this time worked in a new product " dressOlogy ". Hope you guys would like it " Fashion is about dreaming and making other people dream" This app will be a basic need for people like me who are lazy to fold their clothes. now you don't wanna worry about that, just load your dresses in this app. choose your outfit by just swiping the particular area. ** Additionally you can even select Belts and shoes accordingly. ** The innovation that guarantees to convey gigantic changes to the world one years from now is machine learning (ML). Machine learning is a subfield of the Artificial Intelligence look into and got the most noteworthy spotlight in business. ML speaks to another period in programming improvement where PCs, contraptions and different gadgets don`t require uncommon programming to finish errands any longer. Rather, they can gather and break down data that is expected to make suitable determinations and pick up amid program execution. Presently machines can amass past involvement keeping in mind the end goal to settle on choices as it happens among individuals. Obviously, the way toward learning requires uncommon calculations that would "instruct" machines. That is the reason, at The App Solutions, we utilize machine learning in versatile application improvement. To comprehend the size of ML industry, let`s take a general viewpoint to the Artificial Intelligence showcase. As per Bank of America Merrill Lynch, throughout the following five years, the market will reach out to $153 billion contrasted with $58 billion in 2014. Wander Scanning gives an infographic that condenses the Artificial Intelligence market and shows subsidizing of each classification. The outline demonstrates that ML applications classification is driving with over $2 billion piece of the overall industry. This is three times more than the aggregate subsidizing of the following Natural Learning Processing gathering. Venture Scanning gives an infographic that summarizes the Artificial Intelligence market and shows funding of every category. The chart shows that ML applications category is leading with over $2 billion market share. This is three times more than the total funding of the next Natural Learning Processing group. ML has started from the computer, but the emerging trend shows that machine learning mobile app development is the next big thing. The modern mobile devices show the high productive capacity level that is enough to perform appropriate tasks to the same degree as traditional computers do. Also, there are some signals from global corporations that confirm this assumption:
Application of machine learning in roboticsLooking broader at robotics, engineering includes not only mechanisms but also cognitive technologies. Today we are witnessing the emergence of the era when robots assist people on work and household, take care and entertain them. And people will manage these machines with voice commands or program tool actions with only a few taps on their smartphone's screen. All it needs is a machine learning feature for correct performance in unpredictable environment. Implementation of machine learning in data miningThe field of data mining serves to analyze big data and to discover interesting, non-obvious connections within significant set of data. It consists of the data storage, maintenance and the actual analysis. Here ML provides both a set of tools and the learning algorithm to discover all possible relationships. Further in this article we will talk about how to use this technology for predictive analytics when you need to develop a mobile app with machine learning for eCommerce. Application of machine learning in financeIn finance sphere, machine learning algorithms are widely used for predicting future trends, bubbles, and crashes. For example, custom software can analyze all types of information about borrowers such as a history of previous transactions and social media activities to determine the credit rating. Or the system can bring an outcome considering portfolio optimization and send recommendations right to the smartphone. For eCommerce machine learning also opens new opportunities for revenue and improved customer experience. Such retail giants as eBay and Amazon already proved it. But these tools are available for smaller players as well. At The App Solutions, we provide eCommerce machine learning applications for our customers. These apps can be completely custom or with usage of open-source API`s and SDKs (Amazon ML API, Google Cloud Prediction API, etc.). Enterprises can use ML algorithms to their advantage in entirely different aspects of their business. READ ALSO: Do You Need a Mobile eCommerce App for Your Business?
However, the number of opportunities goes far beyond the list. Our team also provides the following machine learning for eCommerce app solutions for our business and startup clients:1
Developing mobile application for eCommerce business and providing it with ML algorithms, you get ahead in the growing industry. Shoppers are spending more and more time online on their mobile devices and expect their shopping experience to get more and more personalized and comfy. Technologies unlock a new potential for market leadership with improving every aspect of commercial workflow. - SHREE THAANUiOS 10.3 Alternate Icons: what can you do and how it works ?In the first beta of iOS 10.3, Apple introduced the ability for an application to change its icon. You could ask when it would be useful. In fact, I see different cases:
Furthermore, you cannot change the icon unbeknownst to the user. Indeed, when you change the icon, there is an alert saying You have changed the icon for "Application Name". Here is a little demonstration of this feature in action: How it worksTo show how this works, I made a dummy supporter application. You have to say which is your favorite national sports team, and it changes the app icons with theses colors. You can find the source code here on Github. So, how is it done ? DeclareFirst: you should declare all alternate icons in the info.plist file. The problem is that you should give up assets catalog for icons. Indeed, it seems not to be compatible with this feature :/. So, we should go back to the old method with simple .png resources. Everything is declared in CFBundleAlternateIcons. In this demo, it gives: <key>CFBundleIcons</key> <dict> <key>CFBundleAlternateIcons</key> <dict> <key>de</key> <dict> <key>CFBundleIconFiles</key> <array> <string>ic_de</string> </array> <key>UIPrerenderedIcon</key> <false/> </dict> <key>fr</key> <dict> <key>CFBundleIconFiles</key> <array> <string>ic_fr</string> </array> <key>UIPrerenderedIcon</key> <false/> </dict> <key>it</key> <dict> <key>CFBundleIconFiles</key> <array> <string>ic_it</string> </array> <key>UIPrerenderedIcon</key> <false/> </dict> </dict> <key>CFBundlePrimaryIcon</key> <dict> <key>CFBundleIconFiles</key> <array> <string>ic_none</string> </array> </dict> </dict>We defined 3 alternate icons:
ChangeOnce the icons are declared, we simply have to call setAlternateIconName(_:completionHandler:) on UIApplication shared instance with one of the declared icon names. For instance, if we want iticon: UIApplication.shared.setAlternateIconName("it") { (error) in if let error = error { print("err: \(error)") } }ReadIf you want to know which icon is currently set up, just read alternateIconName on UIApplication shared instance: if UIApplication.shared.alternateIconName == "it" { print("Viva italia") } else if UIApplication.shared.alternateIconName == "fr" { print("Allez les bleus!") }FutureRight now, this feature is still beta. So it might evolve in the future. Maybe Apple will allow the user to block the icon change (right now, the user is just informed, he cannot say no). Moreover, Apple did not say what is allowed, and what is not in the customization of icons. " I don't take realities of the world for granted; I seek to break and rebuild what I don't like. I seek to outsmart the world " -ShreeThaanu Introduction : In this article you will see how you can obtain a user’s Facebook credentials without him suspecting a thing. There are going to be provided two versions of this attack, one being locally in a private network and one being public, using port forwarding for the latter. Before devoting yourself to the main body of this article, I would like to mention two things right from the very beginning. Firstly, DO NOT, UNDER NO CIRCUMSTANCE, try what you are about to see, to cause harm, assault, threat or have a leverage over a person (illegal, you get it). The purpose of this article is to show how it’s done in regard to academic purposes (pass the knowledge on to everyone) and for testing purposes. Should you be given strict consent, act accordingly. Secondly, you must have Kali Linux installed or booted from a live CD or USB Flash before doing anything. So, check this article here explaining what Kali Linux is, if you haven’t already. SET (Social-Engineer Toolkit) is an open-source tool written in Python. It’s a framework that offers a variety of tools regarding phishing, spoofing, etc. in Social Engineering environment, as the name suggests. It was created by TrustedSec and according to them, Social Engineering is one of the hardest attacks to protect against and nowadays one of the most prevalent. Site Cloner, as the name suggests, is a tool that gives you the option to clone a website, locally. This means that your localhost, 127.0.0.1 will be running the desired website, provided that you enable the Apache service. You can find many details regarding Apache and running a website locally in the DVWA article,
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Photo used under Creative Commons from nan palmero