BLOG POST is a web application built with the latest and top notch web technologies. This application features blog creation, blog updating and blog deletion. If talking about inner technical implementation then it is an example of CRUD (Create, Read, Update, Delete) operation`s functionalities. As per the video attached you can simply add new blogs via clicking button "ADD NEW BLOG" and update or delete a individual blog via clicking "DELETE BLOG" and "UPDATE BLOG" buttons respectively. This web application is completely implemented on JavaScript web technologies. If you wish to develop a application like this with extended technologies then you can contact us. The tech stack involving this project is: React.Js : JavaScript library for building the UI components CSS : For Styling the Dom elements. Reactstrap : To enable the application UI responsive and good looking Node.Js : JavaScript runtime environment for server implementation Express : Used for making Restful API used on the top of Node.js by the project Axios : A http request library used to make API calls mongo DB: It is a NoSql document oriented database. It is used in this application for database implementations. Follow CodersArts blogs to get more insights on how this project maintains features like Mobile Responsiveness, API Integration and Building, Reactstrap Components, Axios Http Library and much more Contact us for more MERN Stack Projects by CodersArts specialists who can help you mentor and guide for such Web projects. If you have project or assignment files, You can send at contact@codersarts.com directly.

BLOG POST is a web application built on top notch and with latest JavaScript technologies. This app features blog creating, blog updating, and blog deleting mechanism. This application is technically an implementation of a CRUD (Create, Read, Update, Delete) operation`s functionalities. This application is built with the JavaScript web technologies. Specially its a example of small MERN stack CRUD application. We can add-on more feature in backend and frontend of this application on demand. For any kind of MERN application development, development-help or guidance and for assignment help please contact us on below contact details. The tech stack involving this project is: React.Js : JavaScript library for building the UI components CSS : For Styling the Dom elements. Reactstrap : To enable the app to reactstrap components and be responsive Node.Js : JavaScript runtime environment for server implementation and building APIs Express.Js : Used for making restful API on the top of Node.js. It is used in backend implementation for this project Axios : A http request library used to make API calls from frontend to backend(API) Mongo DB: Its a NoSql Document oriented database and it is used for database implementation in this project Follow CodersArts blogs to get more insights on how this project maintains features like Mobile Responsiveness, API development and Integration, Responsive and Pixel Perfect Components, Different Http Library implementation, Security and authentication, authorization implementation and much more. Contact us for more MERN Stack Projects by CodersArts specialists who can help you mentor and guide for such Web projects. If you have project or assignment files, You can send at contact@codersarts.com directly.

This blog features the CodersArts E-commerce platform showcasing a sample e-commerce project. This project is live and supports a payment portal. The techs used to build this project are: React Js: Js library for building the UI components. CSS: For Styling the Dom elements. Material UI: To enable the app to use material design. Commerce.js: A Back-end API supporting product listing. Stripe API: Integrated with the payment portal to secure transactions. Netlify : A hosting platform running the project live. Features for this website: 1) A responsive layout, managed with Material UI. Here's a preview of our website in the mobile view. 2) Stripe Api to manage transactions within the website. 3) Commerce.js to manage Back-end. If you're willing to maintain a custom Back-end service, Our Developers skilled in Node provide as per Client's requirements with Complete Guidance and Control on the developed API. The project is currently live at : https://codersarts-ecommerce.netlify.app/checkout The source code is available at: https://github.com/CodersArts/MERN-ECOMMERCE-SAMPLE Contact us for more Mern Stack Projects by Codersarts specialists who can help you mentor and guide for such Web projects. If you have project or assignment files, You can send at contact@codersarts.com directly.

A static website made using HTML, CSS, JavaScript which comprises of 6 different static webpages build with: 1) Skeleton CSS responsive boilerplate to allow a responsive design for any screen aspect ratio. 2) Micron.js interactions allowing an interactive user experience with on clicking events attached to buttons. 3) JQuery a fast, small, and feature-rich JavaScript library which makes things like HTML document traversal and manipulation, event handling, animation, and Ajax much simpler with an easy-to-use API that works across a multitude of browsers allows implantation of Jquery Tabs within the webpages. Our Developer Team specialises in building interactive UI with Bootstrap, Material UI or any CSS framework as per client’s requirements. Such projects being delivered within a week’s span time with constant guidance to the clients and progress reports. Contact us at CodersArts Dev Team to mentor you for such Web Projects. If you have project or assignment files, You can send at contact@codersarts.com directly.

Introduction we'll be building our very own weather app with HTML, CSS, and JavaScript. To retrieve the data, we'll be using something called an API, or an application programming interface. Well, we're actually going to use API fetch weather data based on the city location we can make a request to a weather source – like OpenWeatherMap — to get weather data based on a city. First off, go ahead and sign up for a free API key on OpenWeatherMap here. Once you're signed in, click on API keys and create a key. It should look something like 5ae4a7ce61e3971248f63df723cde762". Now that we have our API key, we can create our files index.html - for our markup style.css - for styling script .js - for the function(s) to fetch data from our APIs we'll need to go into our html document and make sure we've first linked our stylesheet style. css and javascript file script.js. Then, in our tag, let's create div elements with unique class "app-main and then create one more div it contain one input field for enter city name". An other div with class "weather-body" , it contain one div with class "location-details", inside this div we have two div element one for contain city name and county name and one more div contain date , Months ,day and year. we have three more div which contian temp,min_max degree an other contian weather type then img tag contain image according weather type. index.html City,County Date Month (Day), Year 34°C 00°C(Min) /00°C (Max) Clear Getting Data from API In JavaScript, we can use the fetch API, which provides an interface for fetching resources from anywhere on the internet! We can hit this url to get our data. and than create javascript variable that is seachInputBox it hold input-box ,We can do that by using the document.getElementById function. then one event listener for enter key press. Go ahead and write a function getWeatherReport(),then pass city name to it, script.js const weatherApi = { Key:"5ae4a7ce61e3971248f63df723cde762", baseUrl: "https://api.openweathermap.org/data/2.5/weather", } const searchInputBox = document.getElementById("input-box"); searchInputBox.addEventListener('keypress' , (event) => { if(event.keyCode == 13) { console.log(searchInputBox.value); getWeatherReport(searchInputBox.value); document.querySelector('.weather-body').style.display ="block"; } }); function getWeatherReport(city) { fetch(`${weatherApi.baseUrl}?q=${city}&appid=${weatherApi.Key}&units=metric`) .then(weather => { return weather.json(); }).then(showWeatherReport); } function showWeatherReport(weather) { console.log(weather); let city = document.getElementById('city'); city.innerHTML = `${weather.name}, ${weather.sys.country}`; let tempreature = document.getElementById('temp'); tempreature.innerHTML = `${Math.round(weather.main.temp)}°C`; let minMaxTemp = document.getElementById('min-max'); minMaxTemp.innerHTML = `${Math.floor(weather.main.temp_min)}°C(min)/ ${Math.ceil(weather.main.temp_max)}°C(max)`; let weatherType = document.getElementById('weather'); weatherType.innerHTML =`${weather.weather[0].main}`; let date = document.getElementById('date'); let todayDate = new Date(); date.innerText = dateManage(todayDate) if(weatherType.textContent =='Clear') { //console.log(weather.weatherType.textContent); document.body.style.backgroundImage ='url(images/clear1.jpg)'; } else if(weatherType.textContent =='Clouds') { document.body.style.backgroundImage ='url(images/cloudy.jpg)'; } else if(weatherType.textContent =='Sunny') { document.body.style.backgroundImage ='url(images/sunny.jpg)'; } else if(weatherType.textContent =='Rainy') { document.body.style.backgroundImage ='url(images/rain.jpg)'; } else if(weatherType.textContent =='Windy') { document.body.style.backgroundImage ='url(images/windy.jpg)'; } else if(weatherType.textContent =='Haze') { document.body.style.backgroundImage ='url(images/haze.jpg)'; } else if(weatherType.textContent =='Smoke') { document.body.style.backgroundImage ='url(images/smoke.jpg)'; } if(weatherType.textContent =='Clear') { document.getElementById('img').innerHTML = ''; } else if(weatherType.textContent =='Haze') { document.getElementById('img').innerHTML = ''; } else if(weatherType.textContent =='Clouds') { document.getElementById('img').innerHTML = ''; } else if(weatherType.textContent =='Rainy') { document.getElementById('img').innerHTML = ''; } else if(weatherType.textContent =='Storm') { document.getElementById('img').innerHTML = ''; } } function dateManage(dateArg) { let days = ["Sunday","Monday","Tuesday","Wednesday","Thursday","Friday","Saturday",]; let months = ["january", "february", "march", "April", "may", "june", "july", "August", "September", "October", "novmber", "December"]; let year = dateArg.getFullYear() let month = months[dateArg.getMonth()]; let date = dateArg.getDate(); let day = days[dateArg.getDay()]; return `${date} ${month} (${day}), ${year}`; } Style.css *{ margin: 0; padding: 0; box-sizing: border-box; } body{ background-image: url("images/background2.webp"); height: 100vh; overflow: hidden; background-repeat: no-repeat; background-position: top center; background-size:cover; font-family: Arial, Helvetica, sans-serif; } .app-main{ width: 50vh; margin: 100px auto; background-color: rgba(255, 165, 0); padding: 20px; text-align: center; } .app-main > *{ margin-bottom: 20px; } .input-box{ width: 100%; background-color: rgba(255,255,255,0.6); border: none; outline: none; color: blueviolet; font-size: 1.2rem; height: 50px; border-radius: 5px; padding: 0 10px; text-align: center; } .input-box:focus{ background-color: whitesmoke; } .weather-body{ color: #582233; padding: 20px; line-height: 2rem; border-radius: 10px; background-color: rgba(255,255,255,0.6); height: 50vh; display: none; } .location-details{ font-weight: bold; } .weather-status{ padding: 20px; } .temp{ font-size: 50pt; font-weight: 700; margin: 20px 0; text-shadow: 2px 4px rgba(0,0,0,0.3); } .min-max, .weather{ font-size: 12pt; font-weight: 600; } .img{ overflow: hidden; background-repeat: no-repeat; background-position: top center; background-size:cover; width: 80px; height: 70px; margin-left: 50px; align-items: center; } you'll see weather data in JSON outputted based on the city name you've submitted in the URL! This is exactly the data that we'll be fetching. We can fetch the data by passing in our url into the fetch() web API. Go ahead and write a function showWeatherReport() that have one argument that is weathe.then console the weather . after fetch the data . in this case, we'd want to take the data we should fetched and display it on our webpage, which means setting the innerHTML of the tags we created earlier to the city ,temperature, minMaxTemp, weathertype and date etc from our weather data request!. and then we have write code for manage background according to weather type using if condition. Then dateManage function it have one argument that is dateArg, that function manage date ,day and year. If you have project or assignment files, You can send atcontact@codersarts.com directly

Description: Practice and test your knowledge by answering questions in a quiz application. As a developer, you can create a quiz application for testing coding skills of other developers. (HTML, CSS, JavaScript, Python, PHP, etc…) User Stories User can start the quiz by pressing a button User can see a question with 4 possible answers After selecting an answer, display the next question to the User. Do this until the quiz is finished In the end, the User can see the following statistics: Time it took to finish the quiz How many correct answers did he get A message showing if he passed or failed the quiz Bonus features User can share the result of a quiz on social media Add multiple quizzes to the application. User can select which one to take User can create an account and have all the scores saved in his dashboard. User can complete a quiz multiple times

Rest acronym for Representation state transfer. It is architectural style for distributed hyper media systems and was first represented by fielding in 2000 in his famous, Like any other architectural style REST it also have its own 6 guiding constrains which must be satisfied if an interface need to be referred as Restful, Principles of rest : Client - Server stateless cacheable Uniform Interface Layered System code on demand Rest and HTTP not same A lot of people prefer to compare HTTP with REST. rest and http are not same. REST != HTTP Though, because REST also intends to make the web (internet) more streamline and standard, he advocates using REST principles more strictly. And that’s from where people try to start comparing REST with web (HTTP). Roy fielding, in his dissertation, nowhere mentioned any implementation directive – including any protocol preference and HTTP. Till the time, you are honoring the 6 guiding principles of REST, you can call your interface RESTful. In simplest words, in the REST architectural style, data and functionality are considered resources and are accessed using Uniform Resource Identifiers (URIs). The resources are acted upon by using a set of simple, well-defined operations. The clients and servers exchange representations of resources by using a standardized interface and protocol – typically HTTP. Resources are decoupled from their representation so that their content can be accessed in a variety of formats, such as HTML, XML, plain text, PDF, JPEG, JSON, and others. Metadata about the resource is available and used, for example, to control caching, detect transmission errors, negotiate the appropriate representation format, and perform authentication or access control. And most importantly, every interaction with a resource is stateless Android assignment Related #codersart

Here we will learn about some useful machine learning tools and frameworks which is listed below: Tableau Weka SAS studio Rapid Miner Azure Machine Studio Azure Cognitive services DataBricks (Big Data Spark) BigML Heroku Kafka cassandra 1. Tableau Tableau is a data analytics and visualization tool which is used currently in most of data visualization industries or software industries to visualization of given data. It help to understanding data easily by any non technical person. Many businesses or industries need to visualize our data so it covers huge areas. It use drag and drop features so any non-technical person can easily understand its work. This feature helps to perform tasks like sorting, comparing and analyzing, very easily and fast. Tableau is also support multiple sources, including Excel, SQL Server, and cloud-based data repositories which makes it an excellent choice for Data Scientists. Why we will use tableau There are many terms by which we can easily say that "why we will use tableau" Fast Analytics Ease of Use Big Data, Any Data Smart Dashboards Update Automatically Share in Seconds Product Suite Tableau support many product suite: Tableau Desktop Tableau Public Tableau Online Tableau Server Tableau Reader Installation of Tableau You can download tableau directly from official website, which is any try(for 30 days) or paid using below link: https://www.tableau.com/products/desktop/download 2. Weka Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. It contains tools for: data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes. How it will Download You can download(Latest version 3.6) it using below link: https://www.cs.waikato.ac.nz/ml/weka/ 3. SAS studio When you use SAS University Edition, you are using SAS Studio to access SAS. Many people program in SAS by using an application on their PC desktop or SAS server. It is a tool that you can use to write and run SAS code through your web browser. By using SAS you can access your data files, libraries, and existing programs and write new programs. When you use SAS Studio, you are also using SAS software behind the scenes. SAS Studio connects to a SAS server in order to process SAS commands. With SAS software, you can complete these tasks: access any format data: including SAS tables, Microsoft Excel tables, and database files. manage and manipulate your existing data to get the data that you need. The reports that you create can be saved in a wide variety of formats, including HTML, PDF, and RTF. How we can install it Download university edition using below link: https://www.sas.com/en_in/software/university-edition/download-software.html 4. Rapid Miner Data preparation is an important part of data science, their are many techniques are used for this but it will take lots of time to prepare data. It is a brand-new data prep tool which used to help speed productivity so that it prepare data within short of time(or help to time-consuming data preparation tasks). How we can install it use below link to download this: https://docs.rapidminer.com/latest/server/install/ 5. Azure Machine Studio Azure Machine Learning Studio is a the browser-based workbench for Machine Learning. In this you can create free account and get 10GB space, after go apart from 10GB you can get it using paid. you can look all the details regarding limitations using below link: https://azure.microsoft.com/en-us/pricing/details/machine-learning-studio/ After an account is created, you can log in to: https://studio.azureml.net/ It is collection of complete tutorials and guide related to Azure. Azure Machine Learning can be used for different types of machine learning, classical ml deep learning, supervised, and unsupervised learning. By using this you can write Python or R code with the SDK or work with no-code/low-code options in the studio, you can build, train, and track machine learning and deep-learning models in an Azure Machine Learning Workspace.

Project Objective Employers are required to report any serious work-related injuries and death to the authority. This information helps employers, workers and the authority to evaluate the safety of a workplace, understand industry hazards, and implement worker protections to reduce and eliminate hazards. In this mini-project, assume you are engaged by a client to perform text mining on the accident reports to help find answers to the following questions: 1. What are the major types of accidents reflected in the reports? No labels, supervised or non-supervised? Clustering or Topic modelling? All data or partial data? 2. Which type of accidents are more common? Frequency of doc wrt topic 3. Can we find out the more risky occupations in such accidents? Information Extraction, how to identify “occupations” words? 4. Which part of the body is injured most? (Optional) Information Extraction, how to identify “body” words? The dataset is in file “osha.txt“. Data understanding and cleaning Load the data file into R. – read.delim(), header=FALSE e.g. textdata <- read.delim("osha.txt", header=FALSE, sep="\t", quote = "", stringsAsFactors = FALSE) Explore your data. - How many records do you have? How many variables? - Examine the first few records in the datasets. - What information does the dataset contain? - Which fields are useful for your study? - How long are the reports generally? - How’s the data quality? - What are the contents of the reports roughly? [ Create a word cloud for the dataset ] Vectorsource, corpus, DTM Term frequency summary Wordcloud contact us you can contact us directly at here to get any machine learning project related assignment help.

Overview The practical requires demonstrations of practical skills. You will be required to develop a web site that consists of a single, validated HTML page and one image. Basic Web Page · Create an .HTML file. · All styles created must be in an external style sheet. · If CSS fails to display properly, it usually means a missing a colon, semi-colon, or curly-brace somewhere. It could also mean the CSS file hasn’t been linked to the HTML file. · Follow the example screenshot of this practical’s finished version as a guide. · Download and extract the contents: marble_canyon.txt marble_canyon.jpg marble_canyon_panorama.jpg · Create an external style sheet named styles.css and an index.html REQUIREMENTS · Add code to index.html that links to the styles.css file. · Write the necessary HTML markup and CSS so that the page meets the following requirements: · The title for the page is: Your Name’s Midterm Practical. (for example: Jane Doe’s Midterm Practical) · Input the contents of marble_canyon.txt inside the & tags and give the body a Lavender (#E6E6FA) background a color. · Place the text "Marble Canyon " in a block. · Use H1 for the header block and CSS to center. · Use H3 for the Wikipedia line of text and CSS to display in Blue. · Include the five paragraphs (ending in 39.2.) in five separate blocks. In the last paragraph, enclose United States Bureau of Reclamation inside a container that uses an ID or Class to a CSS rule that gives these four words a CornflowerBlue (#6495ED) background color. · Add the image marble_canyon.jpg using the appropriate markup. Set the attributes of width to 500 pixels and height to 378 pixels. If, for some reason, the image does not load, display marble_canyon in its place. · Use CSS to float the image to the right of the page. · Add the image marble_canyon_panorama.jpg and set the width to 1000 pixels and the height to 280 pixels. Failure to load should display panorama. · Using appropriate tags and CSS, center this panorama image · Using the appropriate CSS, clear any text from flowing around either side of the panorama image. · Add a block to display at the bottom of the page · In the footer block, use an ordered list to display the three references. · Make the first reference a hyperlink using: http://www.gcrg.org/bqr/10-2/mcd.html · Add a paragraph to the bottom of the page that contains a copyright symbol followed by a date and your name. · Style the copyright paragraph with an id selector named copyright that will cause the text to be center-aligned, red, and 80% of the normal font-size. · Add a CSS rollover effect to all hyperlinks. All links, excluding background, should turn white when the mouse is over them. Add this rule to the external style sheet · Add a caption tag under the panorama image that includes the text: "A panoramic view of Marble Canyon from Navajo Bridge. Looking into Marble Canyon, Shinimo Alter in the distance." Note that the caption text must fit inside whatever container used to incorporate the image.

In machine learning, the ability of a model to predict continuous or real values based on a training dataset is called Regression. With a small dataset and some great python libraries, we can solve such a problem with ease. In this blog post, we will learn how to solve a supervised regression problem using the famous Boston housing price dataset. Other than location and square footage, a house value is determined by various other factors. Let’s analyze this problem in detail and using machine learning model to predict a housing price. Dependencies pandas - To work with solid data-structures, n-dimensional matrices and perform exploratory data analysis. matplotlib - To visualize data using 2D plots. seaborn - To make 2D plots look pretty and readable. scikit-learn - To create machine learning models easily and make predictions. Boston Housing Prices Dataset In this dataset, each row describes a boston town. There are 506 rows and 13 attributes (features) with a target column (price). The problem that we are going to solve here is that given a set of features that describe a house in Boston, our machine learning model must predict the house price. To train our machine learning model with boston housing data, we will be using scikit- learn’s boston dataset. We will use pandas and scikit-learn to load and explore the dataset. The dataset can easily be loaded from scikit-learn datasets module using load_boston function import pandas as pd from sklearn import datasets boston = datasets.load_boston() There are four keys in this dataset using which we can access more information about the dataset .["data ", "target", "feature_name" and "DESCR"] are the four keys which could be accessed using keys() on the dataset variable. To know the description of each column name in this dataset, we can use DESCR to display the description of this dataset . Exploratory Data Analysis histogram plot We can easily convert the dataset into a pandas dataframe to perform exploratory data analysis. Simply pass in the boston.data as an argument to pd.DataFrame(). We can view the first 5 rows in the dataset using head() function. bos = pd.DataFrame(boston.data, columns = boston.feature_names) bos['PRICE'] = boston.target bos.head() Exploratory Data Analysis is a very important step before training the model. Here, we will use visualizations to understand the relationship of the target variable with other features. Let’s first plot the distribution of the target variable. We will use the histogram plot function from the matplotlib library.

Description: Card memory is a game where you have to click on a card to see what image is underneath it and try to find the matching image underneath the other cards. User Stories User can see a grid with n x n cards (n is an integer). All the cards are faced down initially (hidden state) User can click a button to start the game. When this button is clicked, a timer will start User can click on any card to unveil the image that is underneath it (change it to visible state). The image will be displayed until the user clicks on a 2nd card When the User clicks on the 2nd card: If there is a match, the 2 cards will be eliminated from the game (either hide/remove them or leave them in the visible state) If there isn’t a match, the 2 cards will flip back to their original state (hiddenstate) When all the matches have been found, the User can see a dialog box showing a Congratulations message with a counter displaying the time it took to finish the game Bonus features Use can choose between multiple levels of difficulty (Easy, Medium, Hard). Increased difficulty means: decreasing the time available to complete and/or increasing the number of cards User can see the game statistics (nr. of times he won / he lost, the best time for each level)