Learn how to process, classify, cluster, summarize, understand syntax, semantics and sentiment of text data with the power of Python! This repository contains code and datasets used in my book, "Text Analytics with Python" published by Apress/Springer.

Open in app Get started ITNEXT Published in ITNEXT You have 2 free member-only stories left this month. Sign up for Medium and get an extra one Kush Kush Follow Apr 15, 2021 · 7 min read · Listen Save How you can Control your Android Device with Python Photo by Caspar Camille Rubin on Unsplash Photo by Caspar Camille Rubin on Unsplash Introduction A while back I was thinking of ways in which I could annoy my friends by spamming them with messages for a few minutes, and while doing some research I came across the Android Debug Bridge. In this quick guide I will show you how you can interface with it using Python and how to create 2 quick scripts. The ADB (Android Debug Bridge) is a command line tool (CLI) which can be used to control and communicate with an Android device. You can do many things such as install apps, debug apps, find hidden features and use a shell to interface with the device directly. To enable the ADB, your device must firstly have Developer Options unlocked and USB debugging enabled. To unlock developer options, you can go to your devices settings and scroll down to the about section and find the build number of the current software which is on the device. Click the build number 7 times and Developer Options will be enabled. Then you can go to the Developer Options panel in the settings and enable USB debugging from there. Now the only other thing you need is a USB cable to connect your device to your computer. Here is what todays journey will look like: Installing the requirements Getting started The basics of writing scripts Creating a selfie timer Creating a definition searcher Installing the requirements The first of the 2 things we need to install, is the ADB tool on our computer. This comes automatically bundled with Android Studio, so if you already have that then do not worry. Otherwise, you can head over to the official docs and at the top of the page there should be instructions on how to install it. Once you have installed the ADB tool, you need to get the python library which we will use to interface with the ADB and our device. You can install the pure-python-adb library using pip install pure-python-adb. Optional: To make things easier for us while developing our scripts, we can install an open-source program called scrcpy which allows us to display and control our android device with our computer using a mouse and keyboard. To install it, you can head over to the Github repo and download the correct version for your operating system (Windows, macOS or Linux). If you are on Windows, then extract the zip file into a directory and add this directory to your path. This is so we can access the program from anywhere on our system just by typing in scrcpy into our terminal window. Getting started Now that all the dependencies are installed, we can start up our ADB and connect our device. Firstly, connect your device to your PC with the USB cable, if USB debugging is enabled then a message should pop up asking if it is okay for your PC to control the device, simply answer yes. Then on your PC, open up a terminal window and start the ADB server by typing in adb start-server. This should print out the following messages: * daemon not running; starting now at tcp:5037 * daemon started successfully If you also installed scrcpy, then you can start that by just typing scrcpy into the terminal. However, this will only work if you added it to your path, otherwise you can open the executable by changing your terminal directory to the directory of where you installed scrcpy and typing scrcpy.exe. Hopefully if everything works out, you should be able to see your device on your PC and be able to control it using your mouse and keyboard. Now we can create a new python file and check if we can find our connected device using the library: Here we import the AdbClient class and create a client object using it. Then we can get a list of devices connected. Lastly, we get the first device out of our list (it is generally the only one there if there is only one device connected). The basics of writing scripts The main way we are going to interface with our device is using the shell, through this we can send commands to simulate a touch at a specific location or to swipe from A to B. To simulate screen touches (taps) we first need to work out how the screen coordinates work. To help with these we can activate the pointer location setting in the developer options. Once activated, wherever you touch on the screen, you can see that the coordinates for that point appear at the top. The coordinate system works like this: A diagram to show how the coordinate system works A diagram to show how the coordinate system works The top left corner of the display has the x and y coordinates (0, 0) respectively, and the bottom right corners’ coordinates are the largest possible values of x and y. Now that we know how the coordinate system works, we need to check out the different commands we can run. I have made a list of commands and how to use them below for quick reference: Input tap x y Input text “hello world!” Input keyevent eventID Here is a list of some common eventID’s: 3: home button 4: back button 5: call 6: end call 24: volume up 25: volume down 26: turn device on or off 27: open camera 64: open browser 66: enter 67: backspace 207: contacts 220: brightness down 221: brightness up 277: cut 278: copy 279: paste If you wanted to find more, here is a long list of them here. Creating a selfie timer Now we know what we can do, let’s start doing it. In this first example I will show you how to create a quick selfie timer. To get started we need to import our libraries and create a connect function to connect to our device: You can see that the connect function is identical to the previous example of how to connect to your device, except here we return the device and client objects for later use. In our main code, we can call the connect function to retrieve the device and client objects. From there we can open up the camera app, wait 5 seconds and take a photo. It’s really that simple! As I said before, this is simply replicating what you would usually do, so thinking about how to do things is best if you do them yourself manually first and write down the steps. Creating a definition searcher We can do something a bit more complex now, and that is to ask the browser to find the definition of a particular word and take a screenshot to save it on our computer. The basic flow of this program will be as such: 1. Open the browser 2. Click the search bar 3. Enter the search query 4. Wait a few seconds 5. Take a screenshot and save it But, before we get started, you need to find the coordinates of your search bar in your default browser, you can use the method I suggested earlier to find them easily. For me they were (440, 200). To start, we will have to import the same libraries as before, and we will also have our same connect method. In our main function we can call the connect function, as well as assign a variable to the x and y coordinates of our search bar. Notice how this is a string and not a list or tuple, this is so we can easily incorporate the coordinates into our shell command. We can also take an input from the user to see what word they want to get the definition for: We will add that query to a full sentence which will then be searched, this is so that we can always get the definition. After that we can open the browser and input our search query into the search bar as such: Here we use the eventID 66 to simulate the press of the enter key to execute our search. If you wanted to, you could change the wait timings per your needs. Lastly, we will take a screenshot using the screencap method on our device object, and we can save that as a .png file: Here we must open the file in the write bytes mode because the screencap method returns bytes representing the image. If all went according to plan, you should have a quick script which searches for a specific word. Here it is working on my phone: A GIF to show how the definition searcher example works on my phone A GIF to show how the definition searcher example works on my phone Final thoughts Hopefully you have learned something new today, personally I never even knew this was a thing before I did some research into it. The cool thing is, that you can do anything you normal would be able to do, and more since it just simulates your own touches and actions! I hope you enjoyed the article and thank you for reading! 💖 468 9 468 9 More from ITNEXT Follow ITNEXT is a platform for IT developers & software engineers to share knowledge, connect, collaborate, learn and experience next-gen technologies. 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Built for //BUILD 2017; this repo contains 15 minutes code challenges across data platform and analytics. Inclusive of; SQL Server on Linux, Azure SQL Database, Azure DocumentDB, Azure Search, HDInsight, MySQL as a Service, PostgreSQL as a Service, Bot Framework, Python Tools for Visual Studio, R Tools for Visual Studio. Get challenged, learn new stuff, fork the repo, contribute to the repo, and get a sneak peak the exciting world of Microsoft products and services.

scikit-learn_bench benchmarks various implementations of machine learning algorithms across data analytics frameworks. It currently support the scikit-learn, DAAL4PY, cuML, and XGBoost frameworks for commonly used machine learning algorithms.

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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] Decision trees Main article: Decision tree learning Decision tree learning uses a decision tree as a predictive model to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modeling approaches used in statistics, data mining, and machine learning. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree describes data, but the resulting classification tree can be an input for decision making. Support vector machines Main article: Support vector machines Support vector machines (SVMs), also known as support vector networks, are a set of related supervised learning methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other.[69] An SVM training algorithm is a non-probabilistic, binary, linear classifier, although methods such as Platt scaling exist to use SVM in a probabilistic classification setting. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces. Illustration of linear regression on a data set. Regression analysis Main article: Regression analysis Regression analysis encompasses a large variety of statistical methods to estimate the relationship between input variables and their associated features. Its most common form is linear regression, where a single line is drawn to best fit the given data according to a mathematical criterion such as ordinary least squares. The latter is often extended by regularization (mathematics) methods to mitigate overfitting and bias, as in ridge regression. When dealing with non-linear problems, go-to models include polynomial regression (for example, used for trendline fitting in Microsoft Excel[70]), logistic regression (often used in statistical classification) or even kernel regression, which introduces non-linearity by taking advantage of the kernel trick to implicitly map input variables to higher-dimensional space. Bayesian networks Main article: Bayesian network A simple Bayesian network. Rain influences whether the sprinkler is activated, and both rain and the sprinkler influence whether the grass is wet. A Bayesian network, belief network, or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independence with a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms exist that perform inference and learning. Bayesian networks that model sequences of variables, like speech signals or protein sequences, are called dynamic Bayesian networks. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams. Genetic algorithms Main article: Genetic algorithm A genetic algorithm (GA) is a search algorithm and heuristic technique that mimics the process of natural selection, using methods such as mutation and crossover to generate new genotypes in the hope of finding good solutions to a given problem. In machine learning, genetic algorithms were used in the 1980s and 1990s.[71][72] Conversely, machine learning techniques have been used to improve the performance of genetic and evolutionary algorithms.[73] Training models Usually, machine learning models require a lot of data in order for them to perform well. Usually, when training a machine learning model, one needs to collect a large, representative sample of data from a training set. Data from the training set can be as varied as a corpus of text, a collection of images, and data collected from individual users of a service. Overfitting is something to watch out for when training a machine learning model. Federated learning Main article: Federated learning Federated learning is an adapted form of distributed artificial intelligence to training machine learning models that decentralizes the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralized server. This also increases efficiency by decentralizing the training process to many devices. For example, Gboard uses federated machine learning to train search query prediction models on users' mobile phones without having to send individual searches back to Google.[74] Applications There are many applications for machine learning, including: Agriculture Anatomy Adaptive websites Affective computing Banking Bioinformatics Brain–machine interfaces Cheminformatics Citizen science Computer networks Computer vision Credit-card fraud detection Data quality DNA sequence classification Economics Financial market analysis[75] General game playing Handwriting recognition Information retrieval Insurance Internet fraud detection Linguistics Machine learning control Machine perception Machine translation Marketing Medical diagnosis Natural language processing Natural language understanding Online advertising Optimization Recommender systems Robot locomotion Search engines Sentiment analysis Sequence mining Software engineering Speech recognition Structural health monitoring Syntactic pattern recognition Telecommunication Theorem proving Time series forecasting User behavior analytics In 2006, the media-services provider Netflix held the first "Netflix Prize" competition to find a program to better predict user preferences and improve the accuracy of its existing Cinematch movie recommendation algorithm by at least 10%. A joint team made up of researchers from AT&T Labs-Research in collaboration with the teams Big Chaos and Pragmatic Theory built an ensemble model to win the Grand Prize in 2009 for $1 million.[76] Shortly after the prize was awarded, Netflix realized that viewers' ratings were not the best indicators of their viewing patterns ("everything is a recommendation") and they changed their recommendation engine accordingly.[77] In 2010 The Wall Street Journal wrote about the firm Rebellion Research and their use of machine learning to predict the financial crisis.[78] In 2012, co-founder of Sun Microsystems, Vinod Khosla, predicted that 80% of medical doctors' jobs would be lost in the next two decades to automated machine learning medical diagnostic software.[79] In 2014, it was reported that a machine learning algorithm had been applied in the field of art history to study fine art paintings and that it may have revealed previously unrecognized influences among artists.[80] In 2019 Springer Nature published the first research book created using machine learning.[81] Limitations Although machine learning has been transformative in some fields, machine-learning programs often fail to deliver expected results.[82][83][84] Reasons for this are numerous: lack of (suitable) data, lack of access to the data, data bias, privacy problems, badly chosen tasks and algorithms, wrong tools and people, lack of resources, and evaluation problems.[85] In 2018, a self-driving car from Uber failed to detect a pedestrian, who was killed after a collision.[86] Attempts to use machine learning in healthcare with the IBM Watson system failed to deliver even after years of time and billions of dollars invested.[87][88] Bias Main article: Algorithmic bias Machine learning approaches in particular can suffer from different data biases. A machine learning system trained on current customers only may not be able to predict the needs of new customer groups that are not represented in the training data. When trained on man-made data, machine learning is likely to pick up the same constitutional and unconscious biases already present in society.[89] Language models learned from data have been shown to contain human-like biases.[90][91] Machine learning systems used for criminal risk assessment have been found to be biased against black people.[92][93] In 2015, Google photos would often tag black people as gorillas,[94] and in 2018 this still was not well resolved, but Google reportedly was still using the workaround to remove all gorillas from the training data, and thus was not able to recognize real gorillas at all.[95] Similar issues with recognizing non-white people have been found in many other systems.[96] In 2016, Microsoft tested a chatbot that learned from Twitter, and it quickly picked up racist and sexist language.[97] Because of such challenges, the effective use of machine learning may take longer to be adopted in other domains.[98] Concern for fairness in machine learning, that is, reducing bias in machine learning and propelling its use for human good is increasingly expressed by artificial intelligence scientists, including Fei-Fei Li, who reminds engineers that "There’s nothing artificial about AI...It’s inspired by people, it’s created by people, and—most importantly—it impacts people. It is a powerful tool we are only just beginning to understand, and that is a profound responsibility.”[99] Model assessments Classification of machine learning models can be validated by accuracy estimation techniques like the holdout method, which splits the data in a training and test set (conventionally 2/3 training set and 1/3 test set designation) and evaluates the performance of the training model on the test set. In comparison, the K-fold-cross-validation method randomly partitions the data into K subsets and then K experiments are performed each respectively considering 1 subset for evaluation and the remaining K-1 subsets for training the model. In addition to the holdout and cross-validation methods, bootstrap, which samples n instances with replacement from the dataset, can be used to assess model accuracy.[100] In addition to overall accuracy, investigators frequently report sensitivity and specificity meaning True Positive Rate (TPR) and True Negative Rate (TNR) respectively. Similarly, investigators sometimes report the false positive rate (FPR) as well as the false negative rate (FNR). However, these rates are ratios that fail to reveal their numerators and denominators. The total operating characteristic (TOC) is an effective method to express a model's diagnostic ability. TOC shows the numerators and denominators of the previously mentioned rates, thus TOC provides more information than the commonly used receiver operating characteristic (ROC) and ROC's associated area under the curve (AUC).[101] Ethics Machine learning poses a host of ethical questions. Systems which are trained on datasets collected with biases may exhibit these biases upon use (algorithmic bias), thus digitizing cultural prejudices.[102] For example, using job hiring data from a firm with racist hiring policies may lead to a machine learning system duplicating the bias by scoring job applicants against similarity to previous successful applicants.[103][104] Responsible collection of data and documentation of algorithmic rules used by a system thus is a critical part of machine learning. Because human languages contain biases, machines trained on language corpora will necessarily also learn these biases.[105][106] Other forms of ethical challenges, not related to personal biases, are more seen in health care. There are concerns among health care professionals that these systems might not be designed in the public's interest but as income-generating machines. This is especially true in the United States where there is a long-standing ethical dilemma of improving health care, but also increasing profits. For example, the algorithms could be designed to provide patients with unnecessary tests or medication in which the algorithm's proprietary owners hold stakes. There is huge potential for machine learning in health care to provide professionals a great tool to diagnose, medicate, and even plan recovery paths for patients, but this will not happen until the personal biases mentioned previously, and these "greed" biases are addressed.[107] Hardware Since the 2010s, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks (a particular narrow subdomain of machine learning) that contain many layers of non-linear hidden units.[108] By 2019, graphic processing units (GPUs), often with AI-specific enhancements, had displaced CPUs as the dominant method of training large-scale commercial cloud AI.[109] OpenAI estimated the hardware compute used in the largest deep learning projects from AlexNet (2012) to AlphaZero (2017), and found a 300,000-fold increase in the amount of compute required, with a doubling-time trendline of 3.4 months.[110][111] Software Software suites containing a variety of machine learning algorithms include the following: Free and open-source so

# big-o-performance A simple html app to demonstrate performance costs of data structures. - Clone the project - Navigate to the root of the project in a termina or command prompt - Run 'npm install' - Run 'npm start' - Go to the URL specified in the terminal or command prompt to try out the app. # This app was created from the Create React App NPM. Below are instructions from that project. Below you will find some information on how to perform common tasks. You can find the most recent version of this guide [here](https://github.com/facebookincubator/create-react-app/blob/master/template/README.md). ## Table of Contents - [Updating to New Releases](#updating-to-new-releases) - [Sending Feedback](#sending-feedback) - [Folder Structure](#folder-structure) - [Available Scripts](#available-scripts) - [npm start](#npm-start) - [npm run build](#npm-run-build) - [npm run eject](#npm-run-eject) - [Displaying Lint Output in the Editor](#displaying-lint-output-in-the-editor) - [Installing a Dependency](#installing-a-dependency) - [Importing a Component](#importing-a-component) - [Adding a Stylesheet](#adding-a-stylesheet) - [Post-Processing CSS](#post-processing-css) - [Adding Images and Fonts](#adding-images-and-fonts) - [Adding Bootstrap](#adding-bootstrap) - [Adding Flow](#adding-flow) - [Adding Custom Environment Variables](#adding-custom-environment-variables) - [Integrating with a Node Backend](#integrating-with-a-node-backend) - [Proxying API Requests in Development](#proxying-api-requests-in-development) - [Deployment](#deployment) - [Now](#now) - [Heroku](#heroku) - [Surge](#surge) - [GitHub Pages](#github-pages) - [Something Missing?](#something-missing) ## Updating to New Releases Create React App is divided into two packages: * `create-react-app` is a global command-line utility that you use to create new projects. * `react-scripts` is a development dependency in the generated projects (including this one). You almost never need to update `create-react-app` itself: it’s delegates all the setup to `react-scripts`. When you run `create-react-app`, it always creates the project with the latest version of `react-scripts` so you’ll get all the new features and improvements in newly created apps automatically. To update an existing project to a new version of `react-scripts`, [open the changelog](https://github.com/facebookincubator/create-react-app/blob/master/CHANGELOG.md), find the version you’re currently on (check `package.json` in this folder if you’re not sure), and apply the migration instructions for the newer versions. In most cases bumping the `react-scripts` version in `package.json` and running `npm install` in this folder should be enough, but it’s good to consult the [changelog](https://github.com/facebookincubator/create-react-app/blob/master/CHANGELOG.md) for potential breaking changes. We commit to keeping the breaking changes minimal so you can upgrade `react-scripts` painlessly. ## Sending Feedback We are always open to [your feedback](https://github.com/facebookincubator/create-react-app/issues). ## Folder Structure After creation, your project should look like this: ``` my-app/ README.md index.html favicon.ico node_modules/ package.json src/ App.css App.js index.css index.js logo.svg ``` For the project to build, **these files must exist with exact filenames**: * `index.html` is the page template; * `favicon.ico` is the icon you see in the browser tab; * `src/index.js` is the JavaScript entry point. You can delete or rename the other files. You may create subdirectories inside `src`. For faster rebuilds, only files inside `src` are processed by Webpack. You need to **put any JS and CSS files inside `src`**, or Webpack won’t see them. You can, however, create more top-level directories. They will not be included in the production build so you can use them for things like documentation. ## Available Scripts In the project directory, you can run: ### `npm start` Runs the app in the development mode.<br> Open [http://localhost:3000](http://localhost:3000) to view it in the browser. The page will reload if you make edits.<br> You will also see any lint errors in the console. ### `npm run build` Builds the app for production to the `build` folder.<br> It correctly bundles React in production mode and optimizes the build for the best performance. The build is minified and the filenames include the hashes.<br> Your app is ready to be deployed! ### `npm run eject` **Note: this is a one-way operation. Once you `eject`, you can’t go back!** If you aren’t satisfied with the build tool and configuration choices, you can `eject` at any time. This command will remove the single build dependency from your project. Instead, it will copy all the configuration files and the transitive dependencies (Webpack, Babel, ESLint, etc) right into your project so you have full control over them. All of the commands except `eject` will still work, but they will point to the copied scripts so you can tweak them. At this point you’re on your own. You don’t have to ever use `eject`. The curated feature set is suitable for small and middle deployments, and you shouldn’t feel obligated to use this feature. However we understand that this tool wouldn’t be useful if you couldn’t customize it when you are ready for it. ## Displaying Lint Output in the Editor >Note: this feature is available with `react-scripts@0.2.0` and higher. Some editors, including Sublime Text, Atom, and Visual Studio Code, provide plugins for ESLint. They are not required for linting. You should see the linter output right in your terminal as well as the browser console. However, if you prefer the lint results to appear right in your editor, there are some extra steps you can do. You would need to install an ESLint plugin for your editor first. >**A note for Atom `linter-eslint` users** >If you are using the Atom `linter-eslint` plugin, make sure that **Use global ESLint installation** option is checked: ><img src="http://i.imgur.com/yVNNHJM.png" width="300"> Then make sure `package.json` of your project ends with this block: ```js { // ... "eslintConfig": { "extends": "./node_modules/react-scripts/config/eslint.js" } } ``` Projects generated with `react-scripts@0.2.0` and higher should already have it. If you don’t need ESLint integration with your editor, you can safely delete those three lines from your `package.json`. Finally, you will need to install some packages *globally*: ```sh npm install -g eslint babel-eslint eslint-plugin-react eslint-plugin-import eslint-plugin-jsx-a11y eslint-plugin-flowtype ``` We recognize that this is suboptimal, but it is currently required due to the way we hide the ESLint dependency. The ESLint team is already [working on a solution to this](https://github.com/eslint/eslint/issues/3458) so this may become unnecessary in a couple of months. ## Installing a Dependency The generated project includes React and ReactDOM as dependencies. It also includes a set of scripts used by Create React App as a development dependency. You may install other dependencies (for example, React Router) with `npm`: ``` npm install --save <library-name> ``` ## Importing a Component This project setup supports ES6 modules thanks to Babel. While you can still use `require()` and `module.exports`, we encourage you to use [`import` and `export`](http://exploringjs.com/es6/ch_modules.html) instead. For example: ### `Button.js` ```js import React, { Component } from 'react'; class Button extends Component { render() { // ... } } export default Button; // Don’t forget to use export default! ``` ### `DangerButton.js` ```js import React, { Component } from 'react'; import Button from './Button'; // Import a component from another file class DangerButton extends Component { render() { return <Button color="red" />; } } export default DangerButton; ``` Be aware of the [difference between default and named exports](http://stackoverflow.com/questions/36795819/react-native-es-6-when-should-i-use-curly-braces-for-import/36796281#36796281). It is a common source of mistakes. We suggest that you stick to using default imports and exports when a module only exports a single thing (for example, a component). That’s what you get when you use `export default Button` and `import Button from './Button'`. Named exports are useful for utility modules that export several functions. A module may have at most one default export and as many named exports as you like. Learn more about ES6 modules: * [When to use the curly braces?](http://stackoverflow.com/questions/36795819/react-native-es-6-when-should-i-use-curly-braces-for-import/36796281#36796281) * [Exploring ES6: Modules](http://exploringjs.com/es6/ch_modules.html) * [Understanding ES6: Modules](https://leanpub.com/understandinges6/read#leanpub-auto-encapsulating-code-with-modules) ## Adding a Stylesheet This project setup uses [Webpack](https://webpack.github.io/) for handling all assets. Webpack offers a custom way of “extending” the concept of `import` beyond JavaScript. To express that a JavaScript file depends on a CSS file, you need to **import the CSS from the JavaScript file**: ### `Button.css` ```css .Button { padding: 20px; } ``` ### `Button.js` ```js import React, { Component } from 'react'; import './Button.css'; // Tell Webpack that Button.js uses these styles class Button extends Component { render() { // You can use them as regular CSS styles return <div className="Button" />; } } ``` **This is not required for React** but many people find this feature convenient. You can read about the benefits of this approach [here](https://medium.com/seek-ui-engineering/block-element-modifying-your-javascript-components-d7f99fcab52b). However you should be aware that this makes your code less portable to other build tools and environments than Webpack. In development, expressing dependencies this way allows your styles to be reloaded on the fly as you edit them. In production, all CSS files will be concatenated into a single minified `.css` file in the build output. If you are concerned about using Webpack-specific semantics, you can put all your CSS right into `src/index.css`. It would still be imported from `src/index.js`, but you could always remove that import if you later migrate to a different build tool. ## Post-Processing CSS This project setup minifies your CSS and adds vendor prefixes to it automatically through [Autoprefixer](https://github.com/postcss/autoprefixer) so you don’t need to worry about it. For example, this: ```css .App { display: flex; flex-direction: row; align-items: center; } ``` becomes this: ```css .App { display: -webkit-box; display: -ms-flexbox; display: flex; -webkit-box-orient: horizontal; -webkit-box-direction: normal; -ms-flex-direction: row; flex-direction: row; -webkit-box-align: center; -ms-flex-align: center; align-items: center; } ``` There is currently no support for preprocessors such as Less, or for sharing variables across CSS files. ## Adding Images and Fonts With Webpack, using static assets like images and fonts works similarly to CSS. You can **`import` an image right in a JavaScript module**. This tells Webpack to include that image in the bundle. Unlike CSS imports, importing an image or a font gives you a string value. This value is the final image path you can reference in your code. Here is an example: ```js import React from 'react'; import logo from './logo.png'; // Tell Webpack this JS file uses this image console.log(logo); // /logo.84287d09.png function Header() { // Import result is the URL of your image return <img src={logo} alt="Logo" />; } export default function Header; ``` This works in CSS too: ```css .Logo { background-image: url(./logo.png); } ``` Webpack finds all relative module references in CSS (they start with `./`) and replaces them with the final paths from the compiled bundle. If you make a typo or accidentally delete an important file, you will see a compilation error, just like when you import a non-existent JavaScript module. The final filenames in the compiled bundle are generated by Webpack from content hashes. If the file content changes in the future, Webpack will give it a different name in production so you don’t need to worry about long-term caching of assets. Please be advised that this is also a custom feature of Webpack. **It is not required for React** but many people enjoy it (and React Native uses a similar mechanism for images). However it may not be portable to some other environments, such as Node.js and Browserify. If you prefer to reference static assets in a more traditional way outside the module system, please let us know [in this issue](https://github.com/facebookincubator/create-react-app/issues/28), and we will consider support for this. ## Adding Bootstrap You don’t have to use [React Bootstrap](https://react-bootstrap.github.io) together with React but it is a popular library for integrating Bootstrap with React apps. If you need it, you can integrate it with Create React App by following these steps: Install React Bootstrap and Bootstrap from NPM. React Bootstrap does not include Bootstrap CSS so this needs to be installed as well: ``` npm install react-bootstrap --save npm install bootstrap@3 --save ``` Import Bootstrap CSS and optionally Bootstrap theme CSS in the ```src/index.js``` file: ```js import 'bootstrap/dist/css/bootstrap.css'; import 'bootstrap/dist/css/bootstrap-theme.css'; ``` Import required React Bootstrap components within ```src/App.js``` file or your custom component files: ```js import { Navbar, Jumbotron, Button } from 'react-bootstrap'; ``` Now you are ready to use the imported React Bootstrap components within your component hierarchy defined in the render method. Here is an example [`App.js`](https://gist.githubusercontent.com/gaearon/85d8c067f6af1e56277c82d19fd4da7b/raw/6158dd991b67284e9fc8d70b9d973efe87659d72/App.js) redone using React Bootstrap. ## Adding Flow Flow typing is currently [not supported out of the box](https://github.com/facebookincubator/create-react-app/issues/72) with the default `.flowconfig` generated by Flow. If you run it, you might get errors like this: ```js node_modules/fbjs/lib/Deferred.js.flow:60 60: Promise.prototype.done.apply(this._promise, arguments); ^^^^ property `done`. Property not found in 495: declare class Promise<+R> { ^ Promise. See lib: /private/tmp/flow/flowlib_34952d31/core.js:495 node_modules/fbjs/lib/shallowEqual.js.flow:29 29: return x !== 0 || 1 / (x: $FlowIssue) === 1 / (y: $FlowIssue); ^^^^^^^^^^ identifier `$FlowIssue`. Could not resolve name src/App.js:3 3: import logo from './logo.svg'; ^^^^^^^^^^^^ ./logo.svg. Required module not found src/App.js:4 4: import './App.css'; ^^^^^^^^^^^ ./App.css. Required module not found src/index.js:5 5: import './index.css'; ^^^^^^^^^^^^^ ./index.css. Required module not found ``` To fix this, change your `.flowconfig` to look like this: ```ini [libs] ./node_modules/fbjs/flow/lib [options] esproposal.class_static_fields=enable esproposal.class_instance_fields=enable module.name_mapper='^\(.*\)\.css$' -> 'react-scripts/config/flow/css' module.name_mapper='^\(.*\)\.\(jpg\|png\|gif\|eot\|otf\|webp\|svg\|ttf\|woff\|woff2\|mp4\|webm\)$' -> 'react-scripts/config/flow/file' suppress_type=$FlowIssue suppress_type=$FlowFixMe ``` Re-run flow, and you shouldn’t get any extra issues. If you later `eject`, you’ll need to replace `react-scripts` references with the `<PROJECT_ROOT>` placeholder, for example: ```ini module.name_mapper='^\(.*\)\.css$' -> '<PROJECT_ROOT>/config/flow/css' module.name_mapper='^\(.*\)\.\(jpg\|png\|gif\|eot\|otf\|webp\|svg\|ttf\|woff\|woff2\|mp4\|webm\)$' -> '<PROJECT_ROOT>/config/flow/file' ``` We will consider integrating more tightly with Flow in the future so that you don’t have to do this. ## Adding Custom Environment Variables >Note: this feature is available with `react-scripts@0.2.3` and higher. Your project can consume variables declared in your environment as if they were declared locally in your JS files. By default you will have `NODE_ENV` defined for you, and any other environment variables starting with `REACT_APP_`. These environment variables will be defined for you on `process.env`. For example, having an environment variable named `REACT_APP_SECRET_CODE` will be exposed in your JS as `process.env.REACT_APP_SECRET_CODE`, in addition to `process.env.NODE_ENV`. These environment variables can be useful for displaying information conditionally based on where the project is deployed or consuming sensitive data that lives outside of version control. First, you need to have environment variables defined, which can vary between OSes. For example, let's say you wanted to consume a secret defined in the environment inside a `<form>`: ```jsx render() { return ( <div> <small>You are running this application in <b>{process.env.NODE_ENV}</b> mode.</small> <form> <input type="hidden" defaultValue={process.env.REACT_APP_SECRET_CODE} /> </form> </div> ); } ``` The above form is looking for a variable called `REACT_APP_SECRET_CODE` from the environment. In order to consume this value, we need to have it defined in the environment: ### Windows (cmd.exe) ```cmd set REACT_APP_SECRET_CODE=abcdef&&npm start ``` (Note: the lack of whitespace is intentional.) ### Linux, OS X (Bash) ```bash REACT_APP_SECRET_CODE=abcdef npm start ``` > Note: Defining environment variables in this manner is temporary for the life of the shell session. Setting permanent environment variables is outside the scope of these docs. With our environment variable defined, we start the app and consume the values. Remember that the `NODE_ENV` variable will be set for you automatically. When you load the app in the browser and inspect the `<input>`, you will see its value set to `abcdef`, and the bold text will show the environment provided when using `npm start`: ```html <div> <small>You are running this application in <b>development</b> mode.</small> <form> <input type="hidden" value="abcdef" /> </form> </div> ``` Having access to the `NODE_ENV` is also useful for performing actions conditionally: ```js if (process.env.NODE_ENV !== 'production') { analytics.disable(); } ``` ## Integrating with a Node Backend Check out [this tutorial](https://www.fullstackreact.com/articles/using-create-react-app-with-a-server/) for instructions on integrating an app with a Node backend running on another port, and using `fetch()` to access it. You can find the companion GitHub repository [here](https://github.com/fullstackreact/food-lookup-demo). ## Proxying API Requests in Development >Note: this feature is available with `react-scripts@0.2.3` and higher. People often serve the front-end React app from the same host and port as their backend implementation. For example, a production setup might look like this after the app is deployed: ``` / - static server returns index.html with React app /todos - static server returns index.html with React app /api/todos - server handles any /api/* requests using the backend implementation ``` Such setup is **not** required. However, if you **do** have a setup like this, it is convenient to write requests like `fetch('/api/todos')` without worrying about redirecting them to another host or port during development. To tell the development server to proxy any unknown requests to your API server in development, add a `proxy` field to your `package.json`, for example: ```js "proxy": "http://localhost:4000", ``` This way, when you `fetch('/api/todos')` in development, the development server will recognize that it’s not a static asset, and will proxy your request to `http://localhost:4000/api/todos` as a fallback. Conveniently, this avoids [CORS issues](http://stackoverflow.com/questions/21854516/understanding-ajax-cors-and-security-considerations) and error messages like this in development: ``` Fetch API cannot load http://localhost:4000/api/todos. No 'Access-Control-Allow-Origin' header is present on the requested resource. Origin 'http://localhost:3000' is therefore not allowed access. If an opaque response serves your needs, set the request's mode to 'no-cors' to fetch the resource with CORS disabled. ``` Keep in mind that `proxy` only has effect in development (with `npm start`), and it is up to you to ensure that URLs like `/api/todos` point to the right thing in production. You don’t have to use the `/api` prefix. Any unrecognized request will be redirected to the specified `proxy`. Currently the `proxy` option only handles HTTP requests, and it won’t proxy WebSocket connections. If the `proxy` option is **not** flexible enough for you, alternatively you can: * Enable CORS on your server ([here’s how to do it for Express](http://enable-cors.org/server_expressjs.html)). * Use [environment variables](#adding-custom-environment-variables) to inject the right server host and port into your app. ## Deployment By default, Create React App produces a build assuming your app is hosted at the server root. To override this, specify the `homepage` in your `package.json`, for example: ```js "homepage": "http://mywebsite.com/relativepath", ``` This will let Create React App correctly infer the root path to use in the generated HTML file. ### Now See [this example](https://github.com/xkawi/create-react-app-now) for a zero-configuration single-command deployment with [now](https://zeit.co/now). ### Heroku Use the [Heroku Buildpack for Create React App](https://github.com/mars/create-react-app-buildpack). You can find instructions in [Deploying React with Zero Configuration](https://blog.heroku.com/deploying-react-with-zero-configuration). ### Surge Install the Surge CLI if you haven't already by running `npm install -g surge`. Run the `surge` command and log in you or create a new account. You just need to specify the *build* folder and your custom domain, and you are done. ```sh email: email@domain.com password: ******** project path: /path/to/project/build size: 7 files, 1.8 MB domain: create-react-app.surge.sh upload: [====================] 100%, eta: 0.0s propagate on CDN: [====================] 100% plan: Free users: email@domain.com IP Address: X.X.X.X Success! Project is published and running at create-react-app.surge.sh ``` Note that in order to support routers that use html5 `pushState` API, you may want to rename the `index.html` in your build folder to `200.html` before deploying to Surge. This [ensures that every URL falls back to that file](https://surge.sh/help/adding-a-200-page-for-client-side-routing). ### GitHub Pages >Note: this feature is available with `react-scripts@0.2.0` and higher. Open your `package.json` and add a `homepage` field: ```js "homepage": "http://myusername.github.io/my-app", ``` **The above step is important!** Create React App uses the `homepage` field to determine the root URL in the built HTML file. Now, whenever you run `npm run build`, you will see a cheat sheet with a sequence of commands to deploy to GitHub pages: ```sh git commit -am "Save local changes" git checkout -B gh-pages git add -f build git commit -am "Rebuild website" git filter-branch -f --prune-empty --subdirectory-filter build git push -f origin gh-pages git checkout - ``` You may copy and paste them, or put them into a custom shell script. You may also customize them for another hosting provider. Note that GitHub Pages doesn't support routers that use the HTML5 `pushState` history API under the hood (for example, React Router using `browserHistory`). This is because when there is a fresh page load for a url like `http://user.github.io/todomvc/todos/42`, where `/todos/42` is a frontend route, the GitHub Pages server returns 404 because it knows nothing of `/todos/42`. If you want to add a router to a project hosted on GitHub Pages, here are a couple of solutions: * You could switch from using HTML5 history API to routing with hashes. If you use React Router, you can switch to `hashHistory` for this effect, but the URL will be longer and more verbose (for example, `http://user.github.io/todomvc/#/todos/42?_k=yknaj`). [Read more](https://github.com/reactjs/react-router/blob/master/docs/guides/Histories.md#histories) about different history implementations in React Router. * Alternatively, you can use a trick to teach GitHub Pages to handle 404 by redirecting to your `index.html` page with a special redirect parameter. You would need to add a `404.html` file with the redirection code to the `build` folder before deploying your project, and you’ll need to add code handling the redirect parameter to `index.html`. You can find a detailed explanation of this technique [in this guide](https://github.com/rafrex/spa-github-pages). ## Something Missing? If you have ideas for more “How To” recipes that should be on this page, [let us know](https://github.com/facebookincubator/create-react-app/issues) or [contribute some!](https://github.com/facebookincubator/create-react-app/edit/master/template/README.md)

This repository provides one-click infrastructure and artifact deployment for Azure Synapse Analytics to get you started with Big Data Analytics on a large sized Health Care sample data. You will learn how to ingest, process and serve large volumes of data using various components of Synapse.

An end-to-end, containerized data pipeline for near-real-time user event analytics using Kafka, ClickHouse, Airflow, and PySpark. Made to learn some common data engineering practices.

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class="Header-link" aria-label="View profile and more" data-ga-click="Header, show menu, icon:avatar"> <img src="https://avatars.githubusercontent.com/u/83227313?s=60&amp;v=4" alt="@NhaPhatHanh" size="20" height="20" width="20" class="avatar-user avatar avatar-small "></img> <span class="feature-preview-indicator js-feature-preview-indicator" style="top: 1px;" hidden></span> <span class="dropdown-caret"></span> </summary> <details-menu class="dropdown-menu dropdown-menu-sw" style="width: 180px" src="/users/83227313/menu" preload> <include-fragment> <p class="text-center mt-3" data-hide-on-error> <svg style="box-sizing: content-box; color: var(--color-icon-primary);" viewBox="0 0 16 16" fill="none" width="32" height="32" class="anim-rotate"> <circle cx="8" cy="8" r="7" stroke="currentColor" stroke-opacity="0.25" stroke-width="2" vector-effect="non-scaling-stroke" /> <path d="M15 8a7.002 7.002 0 00-7-7" stroke="currentColor" stroke-width="2" stroke-linecap="round" vector-effect="non-scaling-stroke" /> </svg> </p> <p class="ml-1 mb-2 mt-2 color-text-primary" data-show-on-error> <svg class="octicon octicon-alert" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M8.22 1.754a.25.25 0 00-.44 0L1.698 13.132a.25.25 0 00.22.368h12.164a.25.25 0 00.22-.368L8.22 1.754zm-1.763-.707c.659-1.234 2.427-1.234 3.086 0l6.082 11.378A1.75 1.75 0 0114.082 15H1.918a1.75 1.75 0 01-1.543-2.575L6.457 1.047zM9 11a1 1 0 11-2 0 1 1 0 012 0zm-.25-5.25a.75.75 0 00-1.5 0v2.5a.75.75 0 001.5 0v-2.5z"></path></svg> Sorry, something went wrong. </p> </include-fragment> </details-menu> </details> </div> </header> </div> <div id="start-of-content" class="show-on-focus"></div> <div data-pjax-replace id="js-flash-container"> <template class="js-flash-template"> <div class="flash flash-full {{ className }}"> <div class="container-lg px-2" > <button class="flash-close js-flash-close" type="button" aria-label="Dismiss this message"> <svg class="octicon octicon-x" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M3.72 3.72a.75.75 0 011.06 0L8 6.94l3.22-3.22a.75.75 0 111.06 1.06L9.06 8l3.22 3.22a.75.75 0 11-1.06 1.06L8 9.06l-3.22 3.22a.75.75 0 01-1.06-1.06L6.94 8 3.72 4.78a.75.75 0 010-1.06z"></path></svg> </button> <div>{{ message }}</div> </div> </div> </template> </div> <include-fragment class="js-notification-shelf-include-fragment" data-base-src="https://github.com/notifications/beta/shelf"></include-fragment> <div class="application-main " data-commit-hovercards-enabled data-discussion-hovercards-enabled data-issue-and-pr-hovercards-enabled > <aside class="hide-xl hide-lg width-full color-bg-primary border-bottom py-3 p-responsive" aria-label="Account context"> <details class="details-reset details-overlay d-inline-block" id="details-59141b"> <summary class="no-underline btn-link color-text-primary text-bold width-full" title="Switch account context" data-ga-click="Dashboard, click, Opened account context switcher - context:user"> <img src="https://avatars.githubusercontent.com/u/83227313?s=60&amp;v=4" alt="@NhaPhatHanh" size="20" height="20" width="20" class="avatar-user avatar avatar-small "></img> <span class="css-truncate css-truncate-target ml-1">NhaPhatHanh</span> <span class="dropdown-caret"></span> </summary> <details-menu class="SelectMenu" role="menu" aria-label="Switch dashboard context" > <div class="SelectMenu-modal"> <header class="SelectMenu-header"> <div class="SelectMenu-title">Switch dashboard context</div> <button class="SelectMenu-closeButton" type="button" aria-label="Close menu" data-toggle-for="details-59141b"> <svg class="octicon octicon-x" height="16" viewBox="0 0 16 16" version="1.1" width="16" aria-hidden="true"><path fill-rule="evenodd" d="M3.72 3.72a.75.75 0 011.06 0L8 6.94l3.22-3.22a.75.75 0 111.06 1.06L9.06 8l3.22 3.22a.75.75 0 11-1.06 1.06L8 9.06l-3.22 3.22a.75.75 0 01-1.06-1.06L6.94 8 3.72 4.78a.75.75 0 010-1.06z"></path></svg> </button> </header> <div id="filter-menu-59141b" class="d-flex flex-column flex-1 overflow-hidden" > <div class="SelectMenu-list" > <a class="SelectMenu-item" href="/" role="menuitemradio" aria-checked="true" data-ga-click="Dashboard, switch context, Switch dashboard context from:user to:user"> <svg class="octicon octicon-check SelectMenu-icon SelectMenu-icon--check" height="16" viewBox="0 0 16 16" version="1.1" width="16" aria-hidden="true"><path fill-rule="evenodd" d="M13.78 4.22a.75.75 0 010 1.06l-7.25 7.25a.75.75 0 01-1.06 0L2.22 9.28a.75.75 0 011.06-1.06L6 10.94l6.72-6.72a.75.75 0 011.06 0z"></path></svg> <img class="avatar avatar-small mr-2 avatar-user" src="https://avatars.githubusercontent.com/u/83227313?s=40&amp;v=4" width="20" height="20" alt="@NhaPhatHanh" /> <span class="flex-1 css-truncate css-truncate-overflow">NhaPhatHanh</span> </a> <a class="SelectMenu-item" href="/orgs/gamvip88club/dashboard" role="menuitemradio" aria-checked="false" data-ga-click="Dashboard, switch context, Switch dashboard context from:user to:organization"> <svg class="octicon octicon-check SelectMenu-icon SelectMenu-icon--check" height="16" viewBox="0 0 16 16" version="1.1" width="16" aria-hidden="true"><path fill-rule="evenodd" d="M13.78 4.22a.75.75 0 010 1.06l-7.25 7.25a.75.75 0 01-1.06 0L2.22 9.28a.75.75 0 011.06-1.06L6 10.94l6.72-6.72a.75.75 0 011.06 0z"></path></svg> <img class="avatar avatar-small mr-2" src="https://avatars.githubusercontent.com/u/83322843?s=40&amp;v=4" width="20" height="20" alt="@gamvip88club" /> <span class="flex-1 css-truncate css-truncate-overflow">gamvip88club</span> </a> </div> <div class="border-top color-border-secondary position-relative"> <a class="SelectMenu-item" href="/account/organizations" role="menuitem" data-ga-click="Dashboard, click, Manage orgs link in context switcher - context:user"> <svg class="octicon octicon-organization SelectMenu-icon" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M1.5 14.25c0 .138.112.25.25.25H4v-1.25a.75.75 0 01.75-.75h2.5a.75.75 0 01.75.75v1.25h2.25a.25.25 0 00.25-.25V1.75a.25.25 0 00-.25-.25h-8.5a.25.25 0 00-.25.25v12.5zM1.75 16A1.75 1.75 0 010 14.25V1.75C0 .784.784 0 1.75 0h8.5C11.216 0 12 .784 12 1.75v12.5c0 .085-.006.168-.018.25h2.268a.25.25 0 00.25-.25V8.285a.25.25 0 00-.111-.208l-1.055-.703a.75.75 0 11.832-1.248l1.055.703c.487.325.779.871.779 1.456v5.965A1.75 1.75 0 0114.25 16h-3.5a.75.75 0 01-.197-.026c-.099.017-.2.026-.303.026h-3a.75.75 0 01-.75-.75V14h-1v1.25a.75.75 0 01-.75.75h-3zM3 3.75A.75.75 0 013.75 3h.5a.75.75 0 010 1.5h-.5A.75.75 0 013 3.75zM3.75 6a.75.75 0 000 1.5h.5a.75.75 0 000-1.5h-.5zM3 9.75A.75.75 0 013.75 9h.5a.75.75 0 010 1.5h-.5A.75.75 0 013 9.75zM7.75 9a.75.75 0 000 1.5h.5a.75.75 0 000-1.5h-.5zM7 6.75A.75.75 0 017.75 6h.5a.75.75 0 010 1.5h-.5A.75.75 0 017 6.75zM7.75 3a.75.75 0 000 1.5h.5a.75.75 0 000-1.5h-.5z"></path></svg> Manage organizations </a> <a class="SelectMenu-item" href="/account/organizations/new" role="menuitem" data-ga-click="Dashboard, click, Create org link in context switcher - context:user"> <svg class="octicon octicon-plus SelectMenu-icon" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M7.75 2a.75.75 0 01.75.75V7h4.25a.75.75 0 110 1.5H8.5v4.25a.75.75 0 11-1.5 0V8.5H2.75a.75.75 0 010-1.5H7V2.75A.75.75 0 017.75 2z"></path></svg> Create organization </a> </div> </div> </div> </details-menu> </details> </aside> <div class="d-flex flex-wrap color-bg-canvas-inset" style="min-height: 100vh;"> <aside class="team-left-column col-12 col-md-4 col-lg-3 color-bg-primary border-right color-border-secondary border-bottom hide-md hide-sm" aria-label="Account"> <div class="dashboard-sidebar js-sticky top-0 px-3 px-md-4 px-lg-5 overflow-auto"> <div class="border-bottom color-border-secondary py-3 mt-3 mb-4"> <details class="details-reset details-overlay d-inline-block" id="details-e5dc02"> <summary class="no-underline btn-link color-text-primary text-bold width-full" title="Switch account context" data-ga-click="Dashboard, click, Opened account context switcher - context:user"> <img src="https://avatars.githubusercontent.com/u/83227313?s=60&amp;v=4" alt="@NhaPhatHanh" size="20" height="20" width="20" class="avatar-user avatar avatar-small "></img> <span class="css-truncate css-truncate-target ml-1">NhaPhatHanh</span> <span class="dropdown-caret"></span> </summary> <details-menu class="SelectMenu" role="menu" aria-label="Switch dashboard context" > <div class="SelectMenu-modal"> <header class="SelectMenu-header"> <div class="SelectMenu-title">Switch dashboard context</div> <button class="SelectMenu-closeButton" type="button" aria-label="Close menu" data-toggle-for="details-e5dc02"> <svg class="octicon octicon-x" height="16" viewBox="0 0 16 16" version="1.1" width="16" aria-hidden="true"><path fill-rule="evenodd" d="M3.72 3.72a.75.75 0 011.06 0L8 6.94l3.22-3.22a.75.75 0 111.06 1.06L9.06 8l3.22 3.22a.75.75 0 11-1.06 1.06L8 9.06l-3.22 3.22a.75.75 0 01-1.06-1.06L6.94 8 3.72 4.78a.75.75 0 010-1.06z"></path></svg> </button> </header> <div id="filter-menu-e5dc02" class="d-flex flex-column flex-1 overflow-hidden" > <div class="SelectMenu-list" > <a class="SelectMenu-item" href="/" role="menuitemradio" aria-checked="true" data-ga-click="Dashboard, switch context, Switch dashboard context from:user to:user"> <svg class="octicon octicon-check SelectMenu-icon SelectMenu-icon--check" height="16" viewBox="0 0 16 16" version="1.1" width="16" aria-hidden="true"><path fill-rule="evenodd" d="M13.78 4.22a.75.75 0 010 1.06l-7.25 7.25a.75.75 0 01-1.06 0L2.22 9.28a.75.75 0 011.06-1.06L6 10.94l6.72-6.72a.75.75 0 011.06 0z"></path></svg> <img class="avatar avatar-small mr-2 avatar-user" src="https://avatars.githubusercontent.com/u/83227313?s=40&amp;v=4" width="20" height="20" alt="@NhaPhatHanh" /> <span class="flex-1 css-truncate css-truncate-overflow">NhaPhatHanh</span> </a> <a class="SelectMenu-item" href="/orgs/gamvip88club/dashboard" role="menuitemradio" aria-checked="false" data-ga-click="Dashboard, switch context, Switch dashboard context from:user to:organization"> <svg class="octicon octicon-check SelectMenu-icon SelectMenu-icon--check" height="16" viewBox="0 0 16 16" version="1.1" width="16" aria-hidden="true"><path fill-rule="evenodd" d="M13.78 4.22a.75.75 0 010 1.06l-7.25 7.25a.75.75 0 01-1.06 0L2.22 9.28a.75.75 0 011.06-1.06L6 10.94l6.72-6.72a.75.75 0 011.06 0z"></path></svg> <img class="avatar avatar-small mr-2" src="https://avatars.githubusercontent.com/u/83322843?s=40&amp;v=4" width="20" height="20" alt="@gamvip88club" /> <span class="flex-1 css-truncate css-truncate-overflow">gamvip88club</span> </a> </div> <div class="border-top color-border-secondary position-relative"> <a class="SelectMenu-item" href="/account/organizations" role="menuitem" data-ga-click="Dashboard, click, Manage orgs link in context switcher - context:user"> <svg class="octicon octicon-organization SelectMenu-icon" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M1.5 14.25c0 .138.112.25.25.25H4v-1.25a.75.75 0 01.75-.75h2.5a.75.75 0 01.75.75v1.25h2.25a.25.25 0 00.25-.25V1.75a.25.25 0 00-.25-.25h-8.5a.25.25 0 00-.25.25v12.5zM1.75 16A1.75 1.75 0 010 14.25V1.75C0 .784.784 0 1.75 0h8.5C11.216 0 12 .784 12 1.75v12.5c0 .085-.006.168-.018.25h2.268a.25.25 0 00.25-.25V8.285a.25.25 0 00-.111-.208l-1.055-.703a.75.75 0 11.832-1.248l1.055.703c.487.325.779.871.779 1.456v5.965A1.75 1.75 0 0114.25 16h-3.5a.75.75 0 01-.197-.026c-.099.017-.2.026-.303.026h-3a.75.75 0 01-.75-.75V14h-1v1.25a.75.75 0 01-.75.75h-3zM3 3.75A.75.75 0 013.75 3h.5a.75.75 0 010 1.5h-.5A.75.75 0 013 3.75zM3.75 6a.75.75 0 000 1.5h.5a.75.75 0 000-1.5h-.5zM3 9.75A.75.75 0 013.75 9h.5a.75.75 0 010 1.5h-.5A.75.75 0 013 9.75zM7.75 9a.75.75 0 000 1.5h.5a.75.75 0 000-1.5h-.5zM7 6.75A.75.75 0 017.75 6h.5a.75.75 0 010 1.5h-.5A.75.75 0 017 6.75zM7.75 3a.75.75 0 000 1.5h.5a.75.75 0 000-1.5h-.5z"></path></svg> Manage organizations </a> <a class="SelectMenu-item" href="/account/organizations/new" role="menuitem" data-ga-click="Dashboard, click, Create org link in context switcher - context:user"> <svg class="octicon octicon-plus SelectMenu-icon" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M7.75 2a.75.75 0 01.75.75V7h4.25a.75.75 0 110 1.5H8.5v4.25a.75.75 0 11-1.5 0V8.5H2.75a.75.75 0 010-1.5H7V2.75A.75.75 0 017.75 2z"></path></svg> Create organization </a> </div> </div> </div> </details-menu> </details> </div> <div class="mb-3 Details js-repos-container " data-repository-hovercards-enabled id="dashboard-repos-container" data-pjax-container role="navigation" aria-label="Repositories"> <div class="js-repos-container" id="repos-container" data-pjax-container> <h2 class="f4 hide-sm hide-md mb-1 f5 d-flex flex-justify-between flex-items-center"> Repositories <a class="btn btn-sm btn-primary color-text-white" data-hydro-click="{&quot;event_type&quot;:&quot;dashboard.click&quot;,&quot;payload&quot;:{&quot;event_context&quot;:&quot;REPOSITORIES&quot;,&quot;target&quot;:&quot;NEW_REPOSITORY_BUTTON&quot;,&quot;dashboard_context&quot;:&quot;user&quot;,&quot;dashboard_version&quot;:2,&quot;user_id&quot;:83227313,&quot;originating_url&quot;:&quot;https://github.com/&quot;}}" data-hydro-click-hmac="269f96c8b7925798dca252ba25ee3a5820270ba3fdf7d20d20e789a0179be9aa" data-ga-click="Dashboard, click, Sidebar header new repo button - context:user" href="/new"> <svg class="octicon octicon-repo" height="16" viewBox="0 0 16 16" version="1.1" width="16" aria-hidden="true"><path fill-rule="evenodd" d="M2 2.5A2.5 2.5 0 014.5 0h8.75a.75.75 0 01.75.75v12.5a.75.75 0 01-.75.75h-2.5a.75.75 0 110-1.5h1.75v-2h-8a1 1 0 00-.714 1.7.75.75 0 01-1.072 1.05A2.495 2.495 0 012 11.5v-9zm10.5-1V9h-8c-.356 0-.694.074-1 .208V2.5a1 1 0 011-1h8zM5 12.25v3.25a.25.25 0 00.4.2l1.45-1.087a.25.25 0 01.3 0L8.6 15.7a.25.25 0 00.4-.2v-3.25a.25.25 0 00-.25-.25h-3.5a.25.25 0 00-.25.25z"></path></svg> New </a> </h2> <div class="mt-2 mb-3" role="search" aria-label="Repositories"> <input type="text" class="form-control input-contrast input-block mb-3 js-filterable-field js-your-repositories-search" id="dashboard-repos-filter-left" placeholder="Find a repository&hellip;" aria-label="Find a repository&hellip;" data-url="/" data-query-name="q" value="" autocomplete="off"> </div> <ul class="list-style-none" data-filterable-for="dashboard-repos-filter-left" data-filterable-type="substring"> <li class="private source "> <div class="width-full text-bold"> <a href="/NhaPhatHanh/sumvip" class="d-inline-flex flex-items-baseline flex-wrap f5 mb-2 dashboard-underlined-link width-fit" data-hydro-click="{&quot;event_type&quot;:&quot;dashboard.click&quot;,&quot;payload&quot;:{&quot;event_context&quot;:&quot;REPOSITORIES&quot;,&quot;target&quot;:&quot;REPOSITORY&quot;,&quot;record_id&quot;:361792290,&quot;dashboard_context&quot;:&quot;user&quot;,&quot;dashboard_version&quot;:2,&quot;user_id&quot;:83227313,&quot;originating_url&quot;:&quot;https://github.com/&quot;}}" data-hydro-click-hmac="3d884f457d0af2613e33ebf290824eeee38a2e5ae64f76821d5f549c3bf1d827" data-ga-click="Dashboard, click, Repo list item click - context:user visibility:private fork:false" data-hovercard-type="repository" data-hovercard-url="/NhaPhatHanh/sumvip/hovercard"> <div class="color-text-tertiary mr-2"> <svg class="octicon octicon-lock repo-private-icon flex-shrink-0" aria-label="Repository" viewBox="0 0 16 16" version="1.1" width="16" height="16" role="img"><path fill-rule="evenodd" d="M4 4v2h-.25A1.75 1.75 0 002 7.75v5.5c0 .966.784 1.75 1.75 1.75h8.5A1.75 1.75 0 0014 13.25v-5.5A1.75 1.75 0 0012.25 6H12V4a4 4 0 10-8 0zm6.5 2V4a2.5 2.5 0 00-5 0v2h5zM12 7.5h.25a.25.25 0 01.25.25v5.5a.25.25 0 01-.25.25h-8.5a.25.25 0 01-.25-.25v-5.5a.25.25 0 01.25-.25H12z"></path></svg> </div> <span class="flex-shrink-0 css-truncate css-truncate-target" title="NhaPhatHanh">NhaPhatHanh</span>/<span class="css-truncate css-truncate-target" style="max-width: 260px" title="sumvip">sumvip</span> </a> </div> </li> <li class="public source no-description"> <div class="width-full text-bold"> <a href="/NhaPhatHanh/sumvip.club" class="d-inline-flex flex-items-baseline flex-wrap f5 mb-2 dashboard-underlined-link width-fit" data-hydro-click="{&quot;event_type&quot;:&quot;dashboard.click&quot;,&quot;payload&quot;:{&quot;event_context&quot;:&quot;REPOSITORIES&quot;,&quot;target&quot;:&quot;REPOSITORY&quot;,&quot;record_id&quot;:361782773,&quot;dashboard_context&quot;:&quot;user&quot;,&quot;dashboard_version&quot;:2,&quot;user_id&quot;:83227313,&quot;originating_url&quot;:&quot;https://github.com/&quot;}}" data-hydro-click-hmac="849667ca9bc191f13352a27f1a9589b81b3f666f2de9d5d2abffb7f540b04524" data-ga-click="Dashboard, click, Repo list item click - context:user visibility:public fork:false" data-hovercard-type="repository" data-hovercard-url="/NhaPhatHanh/sumvip.club/hovercard"> <div class="color-text-tertiary mr-2"> <svg aria-label="Repository" class="octicon octicon-repo flex-shrink-0" viewBox="0 0 16 16" version="1.1" width="16" height="16" role="img"><path fill-rule="evenodd" d="M2 2.5A2.5 2.5 0 014.5 0h8.75a.75.75 0 01.75.75v12.5a.75.75 0 01-.75.75h-2.5a.75.75 0 110-1.5h1.75v-2h-8a1 1 0 00-.714 1.7.75.75 0 01-1.072 1.05A2.495 2.495 0 012 11.5v-9zm10.5-1V9h-8c-.356 0-.694.074-1 .208V2.5a1 1 0 011-1h8zM5 12.25v3.25a.25.25 0 00.4.2l1.45-1.087a.25.25 0 01.3 0L8.6 15.7a.25.25 0 00.4-.2v-3.25a.25.25 0 00-.25-.25h-3.5a.25.25 0 00-.25.25z"></path></svg> </div> <span class="flex-shrink-0 css-truncate css-truncate-target" title="NhaPhatHanh">NhaPhatHanh</span>/<span class="css-truncate css-truncate-target" style="max-width: 260px" title="sumvip.club">sumvip.club</span> </a> </div> </li> <li class="public source "> <div class="width-full text-bold"> <a href="/NhaPhatHanh/88vin.link" class="d-inline-flex flex-items-baseline flex-wrap f5 mb-2 dashboard-underlined-link width-fit" data-hydro-click="{&quot;event_type&quot;:&quot;dashboard.click&quot;,&quot;payload&quot;:{&quot;event_context&quot;:&quot;REPOSITORIES&quot;,&quot;target&quot;:&quot;REPOSITORY&quot;,&quot;record_id&quot;:361774252,&quot;dashboard_context&quot;:&quot;user&quot;,&quot;dashboard_version&quot;:2,&quot;user_id&quot;:83227313,&quot;originating_url&quot;:&quot;https://github.com/&quot;}}" data-hydro-click-hmac="0c2927cd368417c1f71936d5894d97c11ad6ad6ba13aad8bb18cb0ae786df73f" data-ga-click="Dashboard, click, Repo list item click - context:user visibility:public fork:false" data-hovercard-type="repository" data-hovercard-url="/NhaPhatHanh/88vin.link/hovercard"> <div class="color-text-tertiary mr-2"> <svg aria-label="Repository" class="octicon octicon-repo flex-shrink-0" viewBox="0 0 16 16" version="1.1" width="16" height="16" role="img"><path fill-rule="evenodd" d="M2 2.5A2.5 2.5 0 014.5 0h8.75a.75.75 0 01.75.75v12.5a.75.75 0 01-.75.75h-2.5a.75.75 0 110-1.5h1.75v-2h-8a1 1 0 00-.714 1.7.75.75 0 01-1.072 1.05A2.495 2.495 0 012 11.5v-9zm10.5-1V9h-8c-.356 0-.694.074-1 .208V2.5a1 1 0 011-1h8zM5 12.25v3.25a.25.25 0 00.4.2l1.45-1.087a.25.25 0 01.3 0L8.6 15.7a.25.25 0 00.4-.2v-3.25a.25.25 0 00-.25-.25h-3.5a.25.25 0 00-.25.25z"></path></svg> </div> <span class="flex-shrink-0 css-truncate css-truncate-target" title="NhaPhatHanh">NhaPhatHanh</span>/<span class="css-truncate css-truncate-target" style="max-width: 260px" title="88vin.link">88vin.link</span> </a> </div> </li> <li class="public source no-description"> <div class="width-full text-bold"> <a href="/NhaPhatHanh/github-docs" class="d-inline-flex flex-items-baseline flex-wrap f5 mb-2 dashboard-underlined-link width-fit" data-hydro-click="{&quot;event_type&quot;:&quot;dashboard.click&quot;,&quot;payload&quot;:{&quot;event_context&quot;:&quot;REPOSITORIES&quot;,&quot;target&quot;:&quot;REPOSITORY&quot;,&quot;record_id&quot;:362337089,&quot;dashboard_context&quot;:&quot;user&quot;,&quot;dashboard_version&quot;:2,&quot;user_id&quot;:83227313,&quot;originating_url&quot;:&quot;https://github.com/&quot;}}" data-hydro-click-hmac="0aa510bbf6362778733e1189b98877ea5eaed33d40c226753ae619d7d44a1f0b" data-ga-click="Dashboard, click, Repo list item click - context:user visibility:public fork:false" data-hovercard-type="repository" data-hovercard-url="/NhaPhatHanh/github-docs/hovercard"> <div class="color-text-tertiary mr-2"> <svg aria-label="Repository" class="octicon octicon-repo flex-shrink-0" viewBox="0 0 16 16" version="1.1" width="16" height="16" role="img"><path fill-rule="evenodd" d="M2 2.5A2.5 2.5 0 014.5 0h8.75a.75.75 0 01.75.75v12.5a.75.75 0 01-.75.75h-2.5a.75.75 0 110-1.5h1.75v-2h-8a1 1 0 00-.714 1.7.75.75 0 01-1.072 1.05A2.495 2.495 0 012 11.5v-9zm10.5-1V9h-8c-.356 0-.694.074-1 .208V2.5a1 1 0 011-1h8zM5 12.25v3.25a.25.25 0 00.4.2l1.45-1.087a.25.25 0 01.3 0L8.6 15.7a.25.25 0 00.4-.2v-3.25a.25.25 0 00-.25-.25h-3.5a.25.25 0 00-.25.25z"></path></svg> </div> <span class="flex-shrink-0 css-truncate css-truncate-target" title="NhaPhatHanh">NhaPhatHanh</span>/<span class="css-truncate css-truncate-target" style="max-width: 260px" title="github-docs">github-docs</span> </a> </div> </li> <li class="public source "> <div class="width-full text-bold"> <a href="/NhaPhatHanh/NhaPhatHanh" class="d-inline-flex flex-items-baseline flex-wrap f5 mb-2 dashboard-underlined-link width-fit" data-hydro-click="{&quot;event_type&quot;:&quot;dashboard.click&quot;,&quot;payload&quot;:{&quot;event_context&quot;:&quot;REPOSITORIES&quot;,&quot;target&quot;:&quot;REPOSITORY&quot;,&quot;record_id&quot;:362176831,&quot;dashboard_context&quot;:&quot;user&quot;,&quot;dashboard_version&quot;:2,&quot;user_id&quot;:83227313,&quot;originating_url&quot;:&quot;https://github.com/&quot;}}" data-hydro-click-hmac="faf207959d3f49dc5283bee49d28f8b67362a5a4d7f6de8f592c4797b96b04d9" data-ga-click="Dashboard, click, Repo list item click - context:user visibility:public fork:false" data-hovercard-type="repository" data-hovercard-url="/NhaPhatHanh/NhaPhatHanh/hovercard"> <div class="color-text-tertiary mr-2"> <svg aria-label="Repository" class="octicon octicon-repo flex-shrink-0" viewBox="0 0 16 16" version="1.1" width="16" height="16" role="img"><path fill-rule="evenodd" d="M2 2.5A2.5 2.5 0 014.5 0h8.75a.75.75 0 01.75.75v12.5a.75.75 0 01-.75.75h-2.5a.75.75 0 110-1.5h1.75v-2h-8a1 1 0 00-.714 1.7.75.75 0 01-1.072 1.05A2.495 2.495 0 012 11.5v-9zm10.5-1V9h-8c-.356 0-.694.074-1 .208V2.5a1 1 0 011-1h8zM5 12.25v3.25a.25.25 0 00.4.2l1.45-1.087a.25.25 0 01.3 0L8.6 15.7a.25.25 0 00.4-.2v-3.25a.25.25 0 00-.25-.25h-3.5a.25.25 0 00-.25.25z"></path></svg> </div> <span class="flex-shrink-0 css-truncate css-truncate-target" title="NhaPhatHanh">NhaPhatHanh</span>/<span class="css-truncate css-truncate-target" style="max-width: 260px" title="NhaPhatHanh">NhaPhatHanh</span> </a> </div> </li> </ul> </div> </div> <div class="js-repos-container user-repos mb-3" id="dashboard-user-teams" data-pjax-container> <div class="Details js-repos-container" data-team-hovercards-enabled> <h2 class="hide-sm hide-md f5 mb-1 border-top color-border-secondary pt-3">Your teams</h2> <p class="notice"> You don&rsquo;t belong to any teams yet! </p> </div> </div> </div> </aside> <div class="col-12 col-md-8 col-lg-6 mt-3 px-3 px-lg-5 border-bottom d-flex flex-auto"> <div class="mx-auto d-flex flex-auto flex-column" style="max-width: 1400px"> <main class="flex-auto"> <div class="border rounded-1 shelf intro-shelf js-notice"> <div class="width-full container"> <div class="width-full mx-auto p-5 shelf-content"> <h2 class="shelf-title">Learn Git and GitHub without any code!</h2> <p class="shelf-lead"> Using the Hello World guide, you’ll create a repository, start a branch, write comments, and open a pull request. </p> <a class="btn btn-primary shelf-cta mx-2 mb-3" target="_blank" data-hydro-click="{&quot;event_type&quot;:&quot;dashboard.click&quot;,&quot;payload&quot;:{&quot;event_context&quot;:&quot;NEW_USER_BANNER&quot;,&quot;dashboard_context&quot;:&quot;user&quot;,&quot;dashboard_version&quot;:2,&quot;target&quot;:&quot;READ_GUIDE&quot;,&quot;user_id&quot;:83227313,&quot;originating_url&quot;:&quot;https://github.com/&quot;}}" data-hydro-click-hmac="065b53dff8bb900c054c67deccb1d8e25644d77c04a9235dfb06aae8f0845b35" data-ga-click="Hello World, click, Clicked Let&#39;s get started button" href="https://guides.github.com/activities/hello-world/">Read the guide</a> <a class="btn shelf-cta mx-2 mb-3" data-hydro-click="{&quot;event_type&quot;:&quot;dashboard.click&quot;,&quot;payload&quot;:{&quot;event_context&quot;:&quot;NEW_USER_BANNER&quot;,&quot;dashboard_context&quot;:&quot;user&quot;,&quot;dashboard_version&quot;:2,&quot;target&quot;:&quot;START_PROJECT&quot;,&quot;user_id&quot;:83227313,&quot;originating_url&quot;:&quot;https://github.com/&quot;}}" data-hydro-click-hmac="e462943bb31bd3003c3636fdf2bd5d45ab45893d5d6e0e806ec4407a59fe0134" data-ga-click="Hello World, click, Clicked new repository button - context:user" href="/new">Start a project</a> </div> <!-- '"` --><!-- </textarea></xmp> --></option></form><form class="shelf-dismiss js-notice-dismiss" action="/dashboard/dismiss_bootcamp" accept-charset="UTF-8" method="post"><input type="hidden" name="_method" value="delete" /><input type="hidden" name="authenticity_token" value="6CEBLJmBkvqLVwFWOHZm+HjVZVyJJeRKsnJpyHd3MuLzYZKDs9LaEeGErnWlxSpK46d2HozAfEX09hbhXIBjOg==" /> <button name="button" type="submit" class="mr-1 close-button tooltipped tooltipped-w" aria-label="Hide this notice forever" data-hydro-click="{&quot;event_type&quot;:&quot;dashboard.click&quot;,&quot;payload&quot;:{&quot;event_context&quot;:&quot;NEW_USER_BANNER&quot;,&quot;dashboard_context&quot;:&quot;user&quot;,&quot;dashboard_version&quot;:2,&quot;target&quot;:&quot;DISMISS_BANNER&quot;,&quot;user_id&quot;:83227313,&quot;originating_url&quot;:&quot;https://github.com/&quot;}}" data-hydro-click-hmac="02ff51f0c07535492d3dc33173db83c1ec1293c047f42e1f920c5285055a6db3" data-ga-click="Hello World, click, Dismissed Hello World" data-ga-load="Hello World, linkview, Viewed Hello World"> <svg aria-label="Hide this notice forever" class="octicon octicon-x v-align-text-top" viewBox="0 0 16 16" version="1.1" width="16" height="16" role="img"><path fill-rule="evenodd" d="M3.72 3.72a.75.75 0 011.06 0L8 6.94l3.22-3.22a.75.75 0 111.06 1.06L9.06 8l3.22 3.22a.75.75 0 11-1.06 1.06L8 9.06l-3.22 3.22a.75.75 0 01-1.06-1.06L6.94 8 3.72 4.78a.75.75 0 010-1.06z"></path></svg> </button></form> </div> </div> <div data-issue-and-pr-hovercards-enabled> <div id="dashboard" class="dashboard"> <h1 class="sr-only">Dashboard</h1> <div class="news"> <div class="js-dashboard-deferred" data-src="/dashboard/recent-activity" data-priority="1" > <div class="Box text-center p-3 mb-4 d-none js-loader"> <div class="loading-message"> <svg style="box-sizing: content-box; color: var(--color-icon-primary);" viewBox="0 0 16 16" fill="none" width="32" height="32" class="anim-rotate"> <circle cx="8" cy="8" r="7" stroke="currentColor" stroke-opacity="0.25" stroke-width="2" vector-effect="non-scaling-stroke" /> <path d="M15 8a7.002 7.002 0 00-7-7" stroke="currentColor" stroke-width="2" stroke-linecap="round" vector-effect="non-scaling-stroke" /> </svg> <p class="color-text-secondary my-2 mb-0">Loading recent activity...</p> </div> </div> </div> <div class="d-block d-md-none"> <div class="mt-2 mb-4 Details js-repos-container" id="dashboard-repositories-box" data-pjax-container role="navigation"> <h2 class="f4 mb-1 text-normal d-flex flex-justify-between flex-items-center">Repositories</h2> <div class="Box px-2 py-1"> <div class="js-repos-container" id="repos-container" data-pjax-container> <h2 class="f4 hide-sm hide-md mb-1 f5 d-flex flex-justify-between flex-items-center"> Repositories <a class="btn btn-sm btn-primary color-text-white" data-hydro-click="{&quot;event_type&quot;:&quot;dashboard.click&quot;,&quot;payload&quot;:{&quot;event_context&quot;:&quot;REPOSITORIES&quot;,&quot;target&quot;:&quot;NEW_REPOSITORY_BUTTON&quot;,&quot;dashboard_context&quot;:&quot;user&quot;,&quot;dashboard_version&quot;:2,&quot;user_id&quot;:83227313,&quot;originating_url&quot;:&quot;https://github.com/&quot;}}" data-hydro-click-hmac="269f96c8b7925798dca252ba25ee3a5820270ba3fdf7d20d20e789a0179be9aa" data-ga-click="Dashboard, click, Sidebar header new repo button - context:user" href="/new"> <svg class="octicon octicon-repo" height="16" viewBox="0 0 16 16" version="1.1" width="16" aria-hidden="true"><path fill-rule="evenodd" d="M2 2.5A2.5 2.5 0 014.5 0h8.75a.75.75 0 01.75.75v12.5a.75.75 0 01-.75.75h-2.5a.75.75 0 110-1.5h1.75v-2h-8a1 1 0 00-.714 1.7.75.75 0 01-1.072 1.05A2.495 2.495 0 012 11.5v-9zm10.5-1V9h-8c-.356 0-.694.074-1 .208V2.5a1 1 0 011-1h8zM5 12.25v3.25a.25.25 0 00.4.2l1.45-1.087a.25.25 0 01.3 0L8.6 15.7a.25.25 0 00.4-.2v-3.25a.25.25 0 00-.25-.25h-3.5a.25.25 0 00-.25.25z"></path></svg> New </a> </h2> <div class="mt-2 mb-3" role="search" aria-label="Repositories"> <input type="text" class="form-control input-contrast input-block mb-3 js-filterable-field js-your-repositories-search" id="dashboard-repos-filter-center" placeholder="Find a repository&hellip;" aria-label="Find a repository&hellip;" data-url="/" data-query-name="q" value="" autocomplete="off"> </div> <ul class="list-style-none" data-filterable-for="dashboard-repos-filter-center" data-filterable-type="substring"> <li class="private source "> <div class="width-full text-bold"> <a href="/NhaPhatHanh/sumvip" class="d-inline-flex flex-items-baseline flex-wrap f5 mb-2 dashboard-underlined-link width-fit" data-hydro-click="{&quot;event_type&quot;:&quot;dashboard.click&quot;,&quot;payload&quot;:{&quot;event_context&quot;:&quot;REPOSITORIES&quot;,&quot;target&quot;:&quot;REPOSITORY&quot;,&quot;record_id&quot;:361792290,&quot;dashboard_context&quot;:&quot;user&quot;,&quot;dashboard_version&quot;:2,&quot;user_id&quot;:83227313,&quot;originating_url&quot;:&quot;https://github.com/&quot;}}" data-hydro-click-hmac="3d884f457d0af2613e33ebf290824eeee38a2e5ae64f76821d5f549c3bf1d827" data-ga-click="Dashboard, click, Repo list item click - context:user visibility:private fork:false" data-hovercard-type="repository" data-hovercard-url="/NhaPhatHanh/sumvip/hovercard"> <div class="color-text-tertiary mr-2"> <svg class="octicon octicon-lock repo-private-icon flex-shrink-0" aria-label="Repository" viewBox="0 0 16 16" version="1.1" width="16" height="16" role="img"><path fill-rule="evenodd" d="M4 4v2h-.25A1.75 1.75 0 002 7.75v5.5c0 .966.784 1.75 1.75 1.75h8.5A1.75 1.75 0 0014 13.25v-5.5A1.75 1.75 0 0012.25 6H12V4a4 4 0 10-8 0zm6.5 2V4a2.5 2.5 0 00-5 0v2h5zM12 7.5h.25a.25.25 0 01.25.25v5.5a.25.25 0 01-.25.25h-8.5a.25.25 0 01-.25-.25v-5.5a.25.25 0 01.25-.25H12z"></path></svg> </div> <span class="flex-shrink-0 css-truncate css-truncate-target" title="NhaPhatHanh">NhaPhatHanh</span>/<span class="css-truncate css-truncate-target" style="max-width: 260px" title="sumvip">sumvip</span> </a> </div> </li> <li class="public source no-description"> <div class="width-full text-bold"> <a href="/NhaPhatHanh/sumvip.club" class="d-inline-flex flex-items-baseline flex-wrap f5 mb-2 dashboard-underlined-link width-fit" data-hydro-click="{&quot;event_type&quot;:&quot;dashboard.click&quot;,&quot;payload&quot;:{&quot;event_context&quot;:&quot;REPOSITORIES&quot;,&quot;target&quot;:&quot;REPOSITORY&quot;,&quot;record_id&quot;:361782773,&quot;dashboard_context&quot;:&quot;user&quot;,&quot;dashboard_version&quot;:2,&quot;user_id&quot;:83227313,&quot;originating_url&quot;:&quot;https://github.com/&quot;}}" data-hydro-click-hmac="849667ca9bc191f13352a27f1a9589b81b3f666f2de9d5d2abffb7f540b04524" data-ga-click="Dashboard, click, Repo list item click - context:user visibility:public fork:false" data-hovercard-type="repository" data-hovercard-url="/NhaPhatHanh/sumvip.club/hovercard"> <div class="color-text-tertiary mr-2"> <svg aria-label="Repository" class="octicon octicon-repo flex-shrink-0" viewBox="0 0 16 16" version="1.1" width="16" height="16" role="img"><path fill-rule="evenodd" d="M2 2.5A2.5 2.5 0 014.5 0h8.75a.75.75 0 01.75.75v12.5a.75.75 0 01-.75.75h-2.5a.75.75 0 110-1.5h1.75v-2h-8a1 1 0 00-.714 1.7.75.75 0 01-1.072 1.05A2.495 2.495 0 012 11.5v-9zm10.5-1V9h-8c-.356 0-.694.074-1 .208V2.5a1 1 0 011-1h8zM5 12.25v3.25a.25.25 0 00.4.2l1.45-1.087a.25.25 0 01.3 0L8.6 15.7a.25.25 0 00.4-.2v-3.25a.25.25 0 00-.25-.25h-3.5a.25.25 0 00-.25.25z"></path></svg> </div> <span class="flex-shrink-0 css-truncate css-truncate-target" title="NhaPhatHanh">NhaPhatHanh</span>/<span class="css-truncate css-truncate-target" style="max-width: 260px" title="sumvip.club">sumvip.club</span> </a> </div> </li> <li class="public source "> <div class="width-full text-bold"> <a href="/NhaPhatHanh/88vin.link" class="d-inline-flex flex-items-baseline flex-wrap f5 mb-2 dashboard-underlined-link width-fit" data-hydro-click="{&quot;event_type&quot;:&quot;dashboard.click&quot;,&quot;payload&quot;:{&quot;event_context&quot;:&quot;REPOSITORIES&quot;,&quot;target&quot;:&quot;REPOSITORY&quot;,&quot;record_id&quot;:361774252,&quot;dashboard_context&quot;:&quot;user&quot;,&quot;dashboard_version&quot;:2,&quot;user_id&quot;:83227313,&quot;originating_url&quot;:&quot;https://github.com/&quot;}}" data-hydro-click-hmac="0c2927cd368417c1f71936d5894d97c11ad6ad6ba13aad8bb18cb0ae786df73f" data-ga-click="Dashboard, click, Repo list item click - context:user visibility:public fork:false" data-hovercard-type="repository" data-hovercard-url="/NhaPhatHanh/88vin.link/hovercard"> <div class="color-text-tertiary mr-2"> <svg aria-label="Repository" class="octicon octicon-repo flex-shrink-0" viewBox="0 0 16 16" version="1.1" width="16" height="16" role="img"><path fill-rule="evenodd" d="M2 2.5A2.5 2.5 0 014.5 0h8.75a.75.75 0 01.75.75v12.5a.75.75 0 01-.75.75h-2.5a.75.75 0 110-1.5h1.75v-2h-8a1 1 0 00-.714 1.7.75.75 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The IBM HR Analytics Employee Attrition & Performance dataset from the Kaggle. I have first performed Exploratory Data Analysis on the data using various libraries like pandas,seaborn,matplotlib etc.. Then I have plotted used feature selection techniques like RFE to select the features. The data is then oversampled using the SMOTE technique in order to deal with the imbalanced classes. Also the data is then scaled for better performance. Lastly I have trained many ML models from the scikit-learn library for predictive modelling and compared the performance using Precision, Recall and other metrics.

Resources and examples to learn how to use and make MCP servers for data and analytics.

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href="tag_figcaption.asp">&lt;figcaption&gt;</a> <a target="_top" href="tag_figure.asp">&lt;figure&gt;</a> <a target="_top" href="tag_font.asp">&lt;font&gt;</a> <a target="_top" href="tag_footer.asp">&lt;footer&gt;</a> <a target="_top" href="tag_form.asp">&lt;form&gt;</a> <a target="_top" href="tag_frame.asp">&lt;frame&gt;</a> <a target="_top" href="tag_frameset.asp">&lt;frameset&gt;</a> <a target="_top" href="tag_hn.asp">&lt;h1&gt; - &lt;h6&gt;</a> <a target="_top" href="tag_head.asp">&lt;head&gt;</a> <a target="_top" href="tag_header.asp">&lt;header&gt;</a> <a target="_top" href="tag_hr.asp">&lt;hr&gt;</a> <a target="_top" href="tag_html.asp">&lt;html&gt;</a> <a target="_top" href="tag_i.asp">&lt;i&gt;</a> <a target="_top" href="tag_iframe.asp">&lt;iframe&gt;</a> <a target="_top" href="tag_img.asp">&lt;img&gt;</a> <a target="_top" href="tag_input.asp">&lt;input&gt;</a> <a target="_top" href="tag_ins.asp">&lt;ins&gt;</a> <a target="_top" href="tag_kbd.asp">&lt;kbd&gt;</a> <a target="_top" href="tag_label.asp">&lt;label&gt;</a> <a target="_top" href="tag_legend.asp">&lt;legend&gt;</a> <a target="_top" href="tag_li.asp">&lt;li&gt;</a> <a target="_top" href="tag_link.asp">&lt;link&gt;</a> <a target="_top" href="tag_main.asp">&lt;main&gt;</a> <a target="_top" href="tag_map.asp">&lt;map&gt;</a> <a target="_top" href="tag_mark.asp">&lt;mark&gt;</a> <a target="_top" href="tag_meta.asp">&lt;meta&gt;</a> <a target="_top" href="tag_meter.asp">&lt;meter&gt;</a> <a target="_top" href="tag_nav.asp">&lt;nav&gt;</a> <a target="_top" href="tag_noframes.asp">&lt;noframes&gt;</a> <a target="_top" href="tag_noscript.asp">&lt;noscript&gt;</a> <a target="_top" href="tag_object.asp">&lt;object&gt;</a> <a target="_top" href="tag_ol.asp">&lt;ol&gt;</a> <a target="_top" href="tag_optgroup.asp">&lt;optgroup&gt;</a> <a target="_top" href="tag_option.asp">&lt;option&gt;</a> <a target="_top" href="tag_output.asp">&lt;output&gt;</a> <a target="_top" href="tag_p.asp" class="active">&lt;p&gt;</a> <a target="_top" href="tag_param.asp">&lt;param&gt;</a> <a target="_top" href="tag_picture.asp">&lt;picture&gt;</a> <a target="_top" href="tag_pre.asp">&lt;pre&gt;</a> <a target="_top" href="tag_progress.asp">&lt;progress&gt;</a> <a target="_top" href="tag_q.asp">&lt;q&gt;</a> <a target="_top" href="tag_rp.asp">&lt;rp&gt;</a> <a target="_top" href="tag_rt.asp">&lt;rt&gt;</a> <a target="_top" href="tag_ruby.asp">&lt;ruby&gt;</a> <a target="_top" href="tag_s.asp">&lt;s&gt;</a> <a target="_top" href="tag_samp.asp">&lt;samp&gt;</a> <a target="_top" href="tag_script.asp">&lt;script&gt;</a> <a target="_top" href="tag_section.asp">&lt;section&gt;</a> <a target="_top" href="tag_select.asp">&lt;select&gt;</a> <a target="_top" href="tag_small.asp">&lt;small&gt;</a> <a target="_top" href="tag_source.asp">&lt;source&gt;</a> <a target="_top" href="tag_span.asp">&lt;span&gt;</a> <a target="_top" href="tag_strike.asp">&lt;strike&gt;</a> <a target="_top" href="tag_strong.asp">&lt;strong&gt;</a> <a target="_top" href="tag_style.asp">&lt;style&gt;</a> <a target="_top" href="tag_sub.asp">&lt;sub&gt;</a> <a target="_top" href="tag_summary.asp">&lt;summary&gt;</a> <a target="_top" href="tag_sup.asp">&lt;sup&gt;</a> <a target="_top" href="tag_svg.asp">&lt;svg&gt;</a> <a target="_top" href="tag_table.asp">&lt;table&gt;</a> <a target="_top" href="tag_tbody.asp">&lt;tbody&gt;</a> <a target="_top" href="tag_td.asp">&lt;td&gt;</a> <a target="_top" href="tag_template.asp">&lt;template&gt;</a> <a target="_top" href="tag_textarea.asp">&lt;textarea&gt;</a> <a target="_top" href="tag_tfoot.asp">&lt;tfoot&gt;</a> <a target="_top" href="tag_th.asp">&lt;th&gt;</a> <a target="_top" href="tag_thead.asp">&lt;thead&gt;</a> <a target="_top" href="tag_time.asp">&lt;time&gt;</a> <a target="_top" href="tag_title.asp">&lt;title&gt;</a> <a target="_top" href="tag_tr.asp">&lt;tr&gt;</a> <a target="_top" href="tag_track.asp">&lt;track&gt;</a> <a target="_top" href="tag_tt.asp">&lt;tt&gt;</a> <a target="_top" href="tag_u.asp">&lt;u&gt;</a> <a target="_top" href="tag_ul.asp">&lt;ul&gt;</a> <a target="_top" href="tag_var.asp">&lt;var&gt;</a> <a target="_top" href="tag_video.asp">&lt;video&gt;</a> <a target="_top" href="tag_wbr.asp">&lt;wbr&gt;</a> </div> <br><br> </div> </div> </div> <div class="w3-main w3-light-grey" id="belowtopnav" style="margin-left: 220px; padding-top: 44px;"> <div class="w3-row w3-white"> <div class="w3-col l10 m12" id="main"> <div id="mainLeaderboard" style="overflow:hidden;"> <!-- MainLeaderboard--> <!--<pre>main_leaderboard, all: [728,90][970,90][320,50][468,60]</pre>--> <div id="adngin-main_leaderboard-0" data-google-query-id="CJPA_sueqvcCFXiOSwUd2fYBLg"><div id="google_ads_iframe_/22152718,16833175/sws-hb//w3schools.com//main_leaderboard_1__container__" style="border: 0pt none;"><iframe id="google_ads_iframe_/22152718,16833175/sws-hb//w3schools.com//main_leaderboard_1" 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</div> <br> <div class="w3-example"> <h3>Example</h3> <p>A paragraph is marked up as follows:</p> <div class="w3-code notranslate htmlHigh"> <span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>p<span class="tagcolor" style="color:mediumblue">&gt;</span></span>This is some text in a paragraph.<span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>/p<span class="tagcolor" style="color:mediumblue">&gt;</span></span> </div> <a target="_blank" href="tryit.asp?filename=tryhtml_paragraphs1" class="w3-btn w3-margin-bottom">Try it Yourself »</a> </div> <p>More "Try it Yourself" examples below.</p> <hr> <h2>Definition and Usage</h2> <p>The <code class="w3-codespan">&lt;p&gt;</code> tag defines a paragraph.</p> <p>Browsers automatically add a single blank line before and after each <code class="w3-codespan">&lt;p&gt;</code> element.</p> <p><strong>Tip:</strong> Use CSS to <a href="/html/html_css.asp">style paragraphs</a>.</p> <hr> <h2>Browser Support</h2> <table class="browserref notranslate"> <tbody><tr> <th style="width:20%;font-size:16px;text-align:left;">Element</th> <th style="width:16%;" class="bsChrome" title="Chrome"></th> <th style="width:16%;" class="bsEdge" title="Internet Explorer / Edge"></th> <th style="width:16%;" class="bsFirefox" title="Firefox"></th> <th style="width:16%;" class="bsSafari" title="Safari"></th> <th style="width:16%;" class="bsOpera" title="Opera"></th> </tr><tr> <td style="text-align:left;">&lt;p&gt;</td> <td>Yes</td> <td>Yes</td> <td>Yes</td> <td>Yes</td> <td>Yes</td> </tr> </tbody></table> <hr> <h2>Global Attributes</h2> <p>The <code class="w3-codespan">&lt;p&gt;</code> tag also supports the <a href="ref_standardattributes.asp">Global Attributes in HTML</a>.</p> <hr> <h2>Event Attributes</h2> <p>The <code class="w3-codespan">&lt;p&gt;</code> tag also supports the <a href="ref_eventattributes.asp">Event Attributes in HTML</a>.</p> <hr> <div id="midcontentadcontainer" style="overflow:auto;text-align:center"> <!-- MidContent --> <!-- <p class="adtext">Advertisement</p> --> <div id="adngin-mid_content-0" data-google-query-id="CKfs_8ueqvcCFXiOSwUd2fYBLg"><div id="sn_ad_label_adngin-mid_content-0" class="sn_ad_label" style="color:#000000;font-size:12px;margin:0;text-align:center;">ADVERTISEMENT</div><div id="google_ads_iframe_/22152718,16833175/sws-hb//w3schools.com//mid_content_1__container__" style="border: 0pt none; display: inline-block; width: 300px; height: 250px;"><iframe frameborder="0" src="https://56d0da6c34aaa471db22bb4266aac656.safeframe.googlesyndication.com/safeframe/1-0-38/html/container.html" id="google_ads_iframe_/22152718,16833175/sws-hb//w3schools.com//mid_content_1" title="3rd party ad content" name="" scrolling="no" marginwidth="0" marginheight="0" width="300" height="250" data-is-safeframe="true" sandbox="allow-forms allow-popups allow-popups-to-escape-sandbox allow-same-origin allow-scripts allow-top-navigation-by-user-activation" role="region" aria-label="Advertisement" tabindex="0" data-google-container-id="8" style="border: 0px; vertical-align: bottom;" data-load-complete="true"></iframe></div></div> </div> <hr> <h2>More Examples</h2> <div class="w3-example"> <h3>Example</h3> <p>Align text in a paragraph (with CSS):</p> <div class="w3-code notranslate htmlHigh"> <span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>p<span class="attributecolor" style="color:red"> style<span class="attributevaluecolor" style="color:mediumblue">="text-align:right"</span></span><span class="tagcolor" style="color:mediumblue">&gt;</span></span>This is some text in a paragraph.<span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>/p<span class="tagcolor" style="color:mediumblue">&gt;</span></span> </div> <a target="_blank" href="tryit.asp?filename=tryhtml_p_align_css" class="w3-btn w3-margin-bottom">Try it Yourself »</a> </div> <div class="w3-example"> <h3>Example</h3> <p>Style paragraphs with CSS:</p> <div class="w3-code notranslate htmlHigh"> <span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>html<span class="tagcolor" style="color:mediumblue">&gt;</span></span><br><span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>head<span class="tagcolor" style="color:mediumblue">&gt;</span></span><br><span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>style<span class="tagcolor" style="color:mediumblue">&gt;</span></span><span class="cssselectorcolor" style="color:brown"><br>p <span class="cssdelimitercolor" style="color:black">{</span><span class="csspropertycolor" style="color:red"><br>&nbsp; color<span class="csspropertyvaluecolor" style="color:mediumblue"><span class="cssdelimitercolor" style="color:black">:</span> navy<span class="cssdelimitercolor" style="color:black">;</span></span><br>&nbsp; text-indent<span class="csspropertyvaluecolor" style="color:mediumblue"><span class="cssdelimitercolor" style="color:black">:</span> 30px<span class="cssdelimitercolor" style="color:black">;</span></span><br>&nbsp; text-transform<span class="csspropertyvaluecolor" style="color:mediumblue"><span class="cssdelimitercolor" style="color:black">:</span> uppercase<span class="cssdelimitercolor" style="color:black">;</span></span><br></span><span class="cssdelimitercolor" style="color:black">}</span><br></span><span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>/style<span class="tagcolor" style="color:mediumblue">&gt;</span></span><br> <span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>/head<span class="tagcolor" style="color:mediumblue">&gt;</span></span><br><span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>body<span class="tagcolor" style="color:mediumblue">&gt;</span></span><br><br><span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>p<span class="tagcolor" style="color:mediumblue">&gt;</span></span>Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.<span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>/p<span class="tagcolor" style="color:mediumblue">&gt;</span></span><br><br><span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>/body<span class="tagcolor" style="color:mediumblue">&gt;</span></span><br><span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>/html<span class="tagcolor" style="color:mediumblue">&gt;</span></span> </div> <a target="_blank" href="tryit.asp?filename=tryhtml_p_style_css" class="w3-btn w3-margin-bottom">Try it Yourself »</a> </div> <div class="w3-example"> <h3>Example</h3> <p>&nbsp;More on paragraphs:</p> <div class="w3-code notranslate htmlHigh"> <span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>p<span class="tagcolor" style="color:mediumblue">&gt;</span></span><br>This paragraph<br>contains a lot of lines<br>in the source code,<br> but the browser <br>ignores it.<br><span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>/p<span class="tagcolor" style="color:mediumblue">&gt;</span></span> </div> <a target="_blank" href="tryit.asp?filename=tryhtml_paragraphs2" class="w3-btn w3-margin-bottom">Try it Yourself »</a> </div> <div class="w3-example"> <h3>Example</h3> <p>Poem problems in HTML:</p> <div class="w3-code notranslate htmlHigh"> <span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>p<span class="tagcolor" style="color:mediumblue">&gt;</span></span><br>My Bonnie lies over the ocean.<br>My Bonnie lies over the sea.<br>My Bonnie lies over the ocean.<br>Oh, bring back my Bonnie to me.<br><span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>/p<span class="tagcolor" style="color:mediumblue">&gt;</span></span> </div> <a target="_blank" href="tryit.asp?filename=tryhtml_poem" class="w3-btn w3-margin-bottom">Try it Yourself »</a> </div> <hr> <h2>Related Pages</h2> <p>HTML tutorial: <a href="/html/html_paragraphs.asp">HTML Paragraphs</a></p> <p>HTML DOM reference: <a href="/jsref/dom_obj_paragraph.asp">Paragraph Object</a></p> <hr> <h2>Default CSS Settings</h2> <p>Most browsers will display the <code class="w3-codespan">&lt;p&gt;</code> element with the following default values:</p> <div class="w3-example"> <h3>Example</h3> <div class="w3-code notranslate cssHigh"><span class="cssselectorcolor" style="color:brown"> p <span class="cssdelimitercolor" style="color:black">{</span><span class="csspropertycolor" style="color:red"><br>&nbsp; display<span class="csspropertyvaluecolor" style="color:mediumblue"><span class="cssdelimitercolor" style="color:black">:</span> block<span class="cssdelimitercolor" style="color:black">;</span></span><br>&nbsp; margin-top<span class="csspropertyvaluecolor" style="color:mediumblue"><span class="cssdelimitercolor" style="color:black">:</span> 1em<span class="cssdelimitercolor" style="color:black">;</span></span><br>&nbsp; margin-bottom<span class="csspropertyvaluecolor" style="color:mediumblue"><span class="cssdelimitercolor" style="color:black">:</span> 1em<span class="cssdelimitercolor" style="color:black">;</span></span><br>&nbsp; margin-left<span class="csspropertyvaluecolor" style="color:mediumblue"><span class="cssdelimitercolor" style="color:black">:</span> 0<span class="cssdelimitercolor" style="color:black">;</span></span><br>&nbsp;&nbsp;margin-right<span class="csspropertyvaluecolor" style="color:mediumblue"><span class="cssdelimitercolor" style="color:black">:</span> 0<span class="cssdelimitercolor" style="color:black">;</span></span><br></span><span class="cssdelimitercolor" style="color:black">}</span> 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Learn Python language for beginners in Data Engineering and Data Analytics

Discover the skills needed for a career in data analysis. Learn foundational concepts used in data analysis and practice using software tools for data analytics and data visualization.

A professional exploration of reflective analytics, dashboards that evolve through user interaction. This project reimagines BI systems as adaptive, cognitive mirrors that learn analyst behavior, anticipate reasoning patterns, and transform data visualization into a collaborative, intelligent process.

Practical example from the SPIE short course "Data Analytics and Machine Learning in Semiconductor Manufacturing: Applications for Physical Design, Process and Yield Optimization"

A comprehensive guide and codebase for building interactive storytelling dashboards with Python, Streamlit, and Plotly. Learn how to transform static analytics into dynamic, user-driven data experiences that engage and inspire, featuring RFM segmentation, cohort analysis, and real-world insights.

The HDMA Center at SDSU focuses on incorporating geo-targeted social media data and APIs with GIS analytics to access and query data. We welcome you to learn from this repository filled with technical documents, examples, and codes and encourage you to add to our efforts! Please email us at hdmasdsu@gmail.com if you like to contribute your codes, documents or skills into this Github.

Notebooks used for Course: "Petroleum Data Analytics and Machine Learning' by Petroleum From Scratch"

A perfect project to learn Data Analytics and apply machine learining algorithms to predict the outlet production sales.

Machine Learning Experiments with scikit-learn, Deep learning with Keras, TensorFlow and Pytorch. Data Science examples for various datasets and competitions from Kaggle and Analytics Vidhya.

Numpy and Pandas are one of the most important building blocks of knowledge to get started in the field of Data Science, Analytics, Machine Learning, Business Intelligence, and Business Analytics. This Tutorial Focuses to help the Beginners to learn the core Concepts of Numpy and Pandas and get started with Machine Learning and Data Science.

This repo is for the Linkedin Learning course: Deep Learning and Generative AI with Python: Data Preprocessing and Analytics

# Breast-cancer-risk-prediction > Necessity, who is the mother of invention. – Plato* ## Welcome to my GitHub repository on Using Predictive Analytics model to diagnose breast cancer. --- ### Objective: The repository is a learning exercise to: * Apply the fundamental concepts of machine learning from an available dataset * Evaluate and interpret my results and justify my interpretation based on observed data set * Create notebooks that serve as computational records and document my thought process. The analysis is divided into four sections, saved in juypter notebooks in this repository 1. Identifying the problem and Data Sources 2. Exploratory Data Analysis 3. Pre-Processing the Data 4. Build model to predict whether breast cell tissue is malignant or Benign ### [Notebook 1](https://github.com/ShiroJean/Breast-cancer-risk-prediction/blob/master/NB1_IdentifyProblem%2BDataClean.ipynb): Identifying the problem and Getting data. **Notebook goal:Identify the types of information contained in our data set** In this notebook I used Python modules to import external data sets for the purpose of getting to know/familiarize myself with the data to get a good grasp of the data and think about how to handle the data in different ways.  ### [Notebook 2](https://github.com/ShiroJean/Breast-cancer-risk-prediction/blob/master/NB2_ExploratoryDataAnalysis.ipynb) Exploratory Data Analysis **Notebook goal:  Explore the variables to assess how they relate to the response variable** In this notebook, I am getting familiar with the data using data exploration and visualization techniques using python libraries (Pandas, matplotlib, seaborn. Familiarity with the data is important which will provide useful knowledge for data pre-processing) ### [Notebook 3](https://github.com/ShiroJean/Breast-cancer-risk-prediction/blob/master/NB3_DataPreprocesing.ipynb) Pre-Processing the data **Notebook goal:Find the most predictive features of the data and filter it so it will enhance the predictive power of the analytics model.** In this notebook I use feature selection to reduce high-dimension data, feature extraction and transformation for dimensionality reduction. This is essential in preparing the data before predictive models are developed. ### [Notebook 4](https://github.com/ShiroJean/Breast-cancer-risk-prediction/blob/master/NB4_PredictiveModelUsingSVM.ipynb) Predictive model using Support Vector Machine (svm) **Notebook goal: Construct predictive models to predict the diagnosis of a breast tumor.** In this notebook, I construct a predictive model using SVM machine learning algorithm to predict the diagnosis of a breast tumor. The diagnosis of a breast tumor is a binary variable (benign or malignant). I also evaluate the model using confusion matrix the receiver operating curves (ROC), which are essential in assessing and interpreting the fitted model. ### [Notebook 5](https://github.com/ShiroJean/Breast-cancer-risk-prediction/blob/master/NB_5%20OptimizingSVMClassifier.ipynb): Optimizing the Support Vector Classifier **Notebook goal: Construct predictive models to predict the diagnosis of a breast tumor.** In this notebook, I aim to tune parameters of the SVM Classification model using scikit-learn.

Artificial Intelligence and Machine Learning have empowered our lives to a large extent. The number of advancements made in this space has revolutionized our society and continue making society a better place to live in. In terms of perception, both Artificial Intelligence and Machine Learning are often used in the same context which leads to confusion. AI is the concept in which machine makes smart decisions whereas Machine Learning is a sub-field of AI which makes decisions while learning patterns from the input data. In this blog, we would dissect each term and understand how Artificial Intelligence and Machine Learning are related to each other. What is Artificial Intelligence? The term Artificial Intelligence was recognized first in the year 1956 by John Mccarthy in an AI conference. In layman terms, Artificial Intelligence is about creating intelligent machines which could perform human-like actions. AI is not a modern-day phenomenon. In fact, it has been around since the advent of computers. The only thing that has changed is how we perceive AI and define its applications in the present world. The exponential growth of AI in the last decade or so has affected every sphere of our lives. Starting from a simple google search which gives the best results of a query to the creation of Siri or Alexa, one of the significant breakthroughs of the 21st century is Artificial Intelligence. The Four types of Artificial Intelligence are:- Reactive AI – This type of AI lacks historical data to perform actions, and completely reacts to a certain action taken at the moment. It works on the principle of Deep Reinforcement learning where a prize is awarded for any successful action and penalized vice versa. Google’s AlphaGo defeated experts in Go using this approach. Limited Memory – In the case of the limited memory, the past data is kept on adding to the memory. For example, in the case of selecting the best restaurant, the past locations would be taken into account and would be suggested accordingly. Theory of Mind – Such type of AI is yet to be built as it involves dealing with human emotions, and psychology. Face and gesture detection comes close but nothing advanced enough to understand human emotions. Self-Aware – This is the future advancement of AI which could configure self-representations. The machines could be conscious, and super-intelligent. Two of the most common usage of AI is in the field of Computer Vision, and Natural Language Processing. Computer Vision is the study of identifying objects such as Face Recognition, Real-time object detection, and so on. Detection of such movements could go a long way in analyzing the sentiments conveyed by a human being. Natural Language Processing, on the other hand, deals with textual data to extract insights or sentiments from it. From ChatBot Development to Speech Recognition like Amazon’s Alexa or Apple’s Siri all uses Natural Language to extract relevant meaning from the data. It is one of the widely popular fields of AI which has found its usefulness in every organization. One other application of AI which has gained popularity in recent times is the self-driving cars. It uses reinforcement learning technique to learn its best moves and identify the restrictions or blockage in front of the road. Many automobile companies are gradually adopting the concept of self-driving cars. What is Machine Learning? Machine Learning is a state-of-the-art subset of Artificial Intelligence which let machines learn from past data, and make accurate predictions. Machine Learning has been around for decades, and the first ML application that got popular was the Email Spam Filter Classification. The system is trained with a set of emails labeled as ‘spam’ and ‘not spam’ known as the training instance. Then a new set of unknown emails is fed to the trained system which then categorizes it as ‘spam’ or ‘not spam.’ All these predictions are made by a certain group of Regression, and Classification algorithms like – Linear Regression, Logistic Regression, Decision Tree, Random Forest, XGBoost, and so on. The usability of these algorithms varies based on the problem statement and the data set in operation. Along with these basic algorithms, a sub-field of Machine Learning which has gained immense popularity in recent times is Deep Learning. However, Deep Learning requires enormous computational power and works best with a massive amount of data. It uses neural networks whose architecture is similar to the human brain. Machine Learning could be subdivided into three categories – Supervised Learning – In supervised learning problems, both the input feature and the corresponding target variable is present in the dataset. Unsupervised Learning – The dataset is not labeled in an unsupervised learning problem i.e., only the input features are present, but not the target variable. The algorithms need to find out the separate clusters in the dataset based on certain patterns. Reinforcement Learning – In this type of problems, the learner is rewarded with a prize for every correct move, and penalized for every incorrect move. The application of Machine Learning is diversified in various domains like Banking, Healthcare, Retail, etc. One of the use cases in the banking industry is predicting the probability of credit loan default by a borrower given its past transactions, credit history, debt ratio, annual income, and so on. In Healthcare, Machine Learning is often been used to predict patient’s stay in the hospital, the likelihood of occurrence of a disease, identifying abnormal patterns in the cell, etc. Many software companies have incorporated Machine Learning in their workflow to steadfast the process of testing. Various manual, repetitive tasks are being replaced by machine learning models. Comparison Between AI and Machine Learning Machine Learning is the subset of Artificial Intelligence which has taken the advancement in AI to a whole new level. The thought behind letting the computer learn from themselves and voluminous data that are getting generated from various sources in the present world has led to the emergence of Machine Learning. In Machine Learning, the concept of neural networks plays a significant role in allowing the system to learn from themselves as well as maintaining its speed, and accuracy. The group of neural nets lets a model rectifying its prior decision and make a more accurate prediction next time. Artificial Intelligence is about acquiring knowledge and applying them to ensure success instead of accuracy. It makes the computer intelligent to make smart decisions on its own akin to the decisions made by a human being. The more complex the problem is, the better it is for AI to solve the complexity. On the other hand, Machine Learning is mostly about acquiring knowledge and maintaining better accuracy instead of success. The primary aim is to learn from the data to automate specific tasks. The possibilities around Machine Learning and Neural Networks are endless. A set of sentiments could be understood from raw text. A machine learning application could also listen to music, and even play a piece of appropriate music based on a person’s mood. NLP, a field of AI which has made some ground-breaking innovations in recent years uses Machine Learning to understand the nuances in natural language and learn to respond accordingly. Different sectors like banking, healthcare, manufacturing, etc., are reaping the benefits of Artificial Intelligence, particularly Machine Learning. Several tedious tasks are getting automated through ML which saves both time and money. Machine Learning has been sold these days consistently by marketers even before it has reached its full potential. AI could be seen as something of the old by the marketers who believe Machine Learning is the Holy Grail in the field of analytics. The future is not far when we would see human-like AI. The rapid advancement in technology has taken us closer than ever before to inevitability. The recent progress in the working AI is much down to how Machine Learning operates. Both Artificial Intelligence and Machine Learning has its own business applications and its usage is completely dependent on the requirements of an organization. AI is an age-old concept with Machine Learning picking up the pace in recent times. Companies like TCS, Infosys are yet to unleash the full potential of Machine Learning and trying to incorporate ML in their applications to keep pace with the rapidly growing Analytics space. Conclusion The hype around Artificial Intelligence and Machine Learning are such that various companies and even individuals want to master the skills without even knowing the difference between the two. Often both the terms are misused in the same context. To master Machine Learning, one needs to have a natural intuition about the data, ask the right questions, and find out the correct algorithms to use to build a model. It often doesn’t requiem how computational capacity. On the other hand, AI is about building intelligent systems which require advanced tools and techniques and often used in big companies like Google, Facebook, etc. There is a whole host of resources to master Machine Learning and AI. The Data Science blogs of Dimensionless is a good place to start with. Also, There are Online Data Science Courses which cover the various nitty gritty of Machine Learning.

This repository is for completed projects and code snippets from Codecademy Data Scientist: Analytics Specialist Career Path: https://www.codecademy.com/learn/paths/data-analyst

plan, design and implement enterprise data infrastructure solutions and create the blueprints for an organization’s data management system. You’ll create a relational database with PostGreSQL, design an Online Analytical Processing (OLAP) data model to build a cloud based data warehouse, and design scalable data lake architecture that meets the needs of Big Data. Finally, you’ll learn how to apply the principles of data governance to an organization’s data management system.

This repository is to learn doing Data Analytics in the domain HR

Sentiment Analysis with Insights using NLP and Dash This project show the sentiment analysis of text data using NLP and Dash. I used Amazon reviews dataset to train the model and further scrap the reviews from Etsy.com in order to test my model. Prerequisites: Python3 Amazon Dataset (3.6GB) Anaconda How this project was made? This project has been built using Python3 to help predict the sentiments with the help of Machine Learning and an interactive dashboard to test reviews. To start, I downloaded the dataset and extracted the JSON file. Next, I took out a portion of 7,92,000 reviews equally distributed into chunks of 24000 reviews using pandas. The chunks were then combined into a single CSV file called balanced_reviews.csv. This balanced_reviews.csv served as the base for training my model which was filtered on the basis of review greater than 3 and less than 3. Further, this filtered data was vectorized using TF_IDF vectorizer. After training the model to a 90% accuracy, the reviews were scrapped from Etsy.com in order to test our model. Finally, I built a dashboard in which we can check the sentiments based on input given by the user or can check the sentiments of reviews scrapped from the website. What is CountVectorizer? CountVectorizer is a great tool provided by the scikit-learn library in Python. It is used to transform a given text into a vector on the basis of the frequency (count) of each word that occurs in the entire text. This is helpful when we have multiple such texts, and we wish to convert each word in each text into vectors (for using in further text analysis). CountVectorizer creates a matrix in which each unique word is represented by a column of the matrix, and each text sample from the document is a row in the matrix. The value of each cell is nothing but the count of the word in that particular text sample. What is TF-IDF Vectorizer? TF-IDF stands for Term Frequency - Inverse Document Frequency and is a statistic that aims to better define how important a word is for a document, while also taking into account the relation to other documents from the same corpus. This is performed by looking at how many times a word appears into a document while also paying attention to how many times the same word appears in other documents in the corpus. The rationale behind this is the following: a word that frequently appears in a document has more relevancy for that document, meaning that there is higher probability that the document is about or in relation to that specific word a word that frequently appears in more documents may prevent us from finding the right document in a collection; the word is relevant either for all documents or for none. Either way, it will not help us filter out a single document or a small subset of documents from the whole set. So then TF-IDF is a score which is applied to every word in every document in our dataset. And for every word, the TF-IDF value increases with every appearance of the word in a document, but is gradually decreased with every appearance in other documents. What is Plotly Dash? Dash is a productive Python framework for building web analytic applications. Written on top of Flask, Plotly.js, and React.js, Dash is ideal for building data visualization apps with highly custom user interfaces in pure Python. It's particularly suited for anyone who works with data in Python. Dash apps are rendered in the web browser. You can deploy your apps to servers and then share them through URLs. Since Dash apps are viewed in the web browser, Dash is inherently cross-platform and mobile ready. Dash is an open source library, released under the permissive MIT license. Plotly develops Dash and offers a platform for managing Dash apps in an enterprise environment. What is Web Scrapping? Web scraping is a term used to describe the use of a program or algorithm to extract and process large amounts of data from the web. Running the project Step 1: Download the dataset and extract the JSON data in your project folder. Make a folder filtered_chunks and run the data_extraction.py file. This will extract data from the JSON file into equal sized chunks and then combine them into a single CSV file called balanced_reviews.csv. Step 2: Run the data_cleaning_preprocessing_and_vectorizing.py file. This will clean and filter out the data. Next the filtered data will be fed to the TF-IDF Vectorizer and then the model will be pickled in a trained_model.pkl file and the Vocabulary of the trained model will be stored as vocab.pkl. Keep these two files in a folder named model_files. Step 3: Now run the etsy_review_scrapper.py file. Adjust the range of pages and product to be scrapped as it might take a long long time to process. A small sized data is sufficient to check the accuracy of our model. The scrapped data will be stored in csv as well as db file. Step 4: Finally, run the app.py file that will start up the Dash server and we can check the working of our model either by typing or either by selecting the preloaded scrapped reviews.