Project Summary Description: The web application project is to design and develop a task roster system for shared spaces that allows managers to set tasks, notifies users of their tasks, and allows users to mark tasks as complete. 1. Upon loading the system should display Current tasks that need to be done today The user who is assigned that task 2. Users should be able to sign up and log in so they can View their scheduled tasks Manage their profile/user information Manage their availability Manage the types of tasks they can/want to do. 3. Managers should be able to sign up and log in to: Manage their profile/manager information Create and manage different tasks. Group tasks. Assign tasks or groups of tasks to users. 4. Users/managers should be able to choose to link a social media/email/other account, allowing login via that platform, to make logging in easier. Phases of Development for the Project: 1. Site Design: First of all, we have to make a design to get the basic structure for our project. Then we have to start working on the flow for our project. Now, we are going to use HTML and CSS to design our page look. 2. Data Plan: For each of the features in the website, we have to create a data plan that lists the different pieces of content/information that your web application will be dealing with & determine where it should be stored, what format, and where it should be processed. Where does the information come from? What form should it take? If the information is on the server, what will the client need to send to retrieve that data? If the information is on the client, how will it be sent to the server? Does all information need to be stored on the server? What processing needs to be done to make the data useful? 3. Client-side Implementation: We are going to use node.js and set up an express server and migrate the website to it. Now we are ready to start handling data on the server. Using a combination of GET/POST methods and AJAX, we have to modify our website and server to implement the calls needed to handle the content/information for each of our features as identified in our data plan. The routes don't need to be functionally complete at this stage, but we want to set them up with dummy responses to ensure that our client side code is working as expected. 4. Database Implementation: Now that we’ve designed our database, we can set it up and begin writing queries. Setup SQL database and tables. Write down the queries needed to store and retrieve data in our database. 5. Server Implementation: Modify our server routes to perform the tasks and data queries needed to complete our web application. Implement and populate your Database Complete your server routes with full functionality, integrating the Database queries. If you have any queries regarding this blog or need any help you can contact us on: contact@codersarts.com

Before moving forward in this blog, one should have a basic understanding of HTML, CSS, and JavaScript. What is Vue.js ? Vue.js is an open source JavaScript framework with various optional tools used to develop interactive web interfaces. It is one of the famous frameworks used to simplify web development. Vue.js focuses on the view layer. It can be easily integrated into big projects for front-end development without any issues. Vue.js is created by Evan You, an ex-employee from Google. The first version of Vue.js was released in Feb 2014. Why Vue.js ? Following are the advantages of using Vue.js technology in web development: 1. Very Small Size: The success of JavaScript framework depends on its size. The smaller the size is, the more it will be used. One of the greatest advantages of Vue.js is its small size. The size of this framework is 18–21KB and it takes no time for the user to download and use it. 2. Easy to Understand and Develop Applications: The user can easily add Vue.js to his web project because of its simple structure. Both the small as well as large scales templates can be developed through this framework which saves a lot of time. 3. Simple Integration: Vue.js is also popular among the web developers because it facilitates them to integrate with the existing applications. This is because it is based on JavaScript framework and can be integrated into other applications built on JavaScript. 4. Detailed Documentation: The documentation with Vue.js is so comprehensive that the user who knows a little about JavaScript and HTML can develop his own application or web page. 5. Flexibility: A great deal of flexibility is another advantage of Vue.js. It allows the user to write his template in HTML file, JavaScript file, and pure JavaScript file using virtual nodes. Vue.js has proved a lot beneficial in the development of those simple applications that run directly from browsers. Installation of Vue.js The installation for Vue.js is very easy to start with. Any developer can easily understand and build interactive web interfaces. There are many ways to install VueJS: 1) Using the To download vue.js go to the site https://vuejs.org/v2/guide/installation.html of VueJS and download the vue.js as per your need. There are two versions for use - production version and development version. The development version is not minimized, whereas the production version is minimized as shown in the following screenshot. Development version will help with the warnings and debug mode during the development of the project. 2. Using CDN We can also start using VueJS file from the CDN library. This link https://unpkg.com/vue will give the latest version of VueJS. VueJS is also available on jsDelivr (https://cdn.jsdelivr.net/npm/vue/dist/vue.js) and cdnjs (https://cdnjs.cloudflare.com/ajax/libs/vue/2.4.0/vue.js). We can host the files at our end, if required and get started with VueJS development. 3. Using NPM For large scale applications with VueJS, it is recommended to install using the npm package. It comes with Browserify and Webpack along with other necessary tools, which help with the development. Following is the command to install using npm: npm install vue 4. Using CLI (Command Line Interface) VueJS also provides CLI to install the vue and get started with the server activation. To install using CLI, we need to have CLI installed which is done using the following command: npm install --global vue-cli It takes a few minutes for the installation. Once done, it shows the CLI version for VueJS. Now, we have to create the project using Webpack. Following is the command to create the project using Webpack: vue init webpack myproject Now to get started, we have to use the following command: cd myproject npm install npm run dev Once we execute npm run dev, it starts the server and provides the url for display to be seen in the browser which is as shown in the following image. If you have any queries regarding this blog or need any help you can contact us on: contact@codersarts.com

Before starting node.js one should have a basic knowledge of javascript. What is Node.js ? Node.js is a tool for JavaScript framework, it is used for developing server-based applications. Node.js is an open source server environment. It allows you to run JavaScript on the server. A Node.js app run in a single process, without creating a new thread for every request. Download Node.js First of all we have to download node.js from its official site and then we have to install it. To install node.js you have to follow these steps: Step 1) Click on the link given below and you can download node.js directly from its official site: https://nodejs.org/en/download/ When you click on the link a page like this will open in a new tab. You have to download node.js according to your system requirement like, for Windows operating system you have to click on "Windows Installer". Step 2) Double click on the downloaded .msi file to start the installation. Click the Run button on the first screen to begin the installation. Step 3) Click the "Next" button to continue with the installation Step 4) Click on the box to Accept the license agreement and then click on the Next button. Step 5) If you have to change the location you can do it from here, by choosing the location where Node.js needs to be installed and then click on the Next button. Step 6) Accept the default components and click on the Next button. Step 7) Click the Install button to start the installation. Step 8) Click the Finish button to complete the installation. Now, you have successfully installed node.js in your system. "Hello World" in Node.js After downloading and installing node.js, now we have to display "Hello World" in a browser. For this we have to create a file in any text editor and name it as index.js file, and add these lines of code in it: varhttp = require('http'); http.createServer(function(req, res) {   res.writeHead(200, {'Content-Type':'text/html'}); res.end('Hello World!'); }).listen(8012); You have to save the file on your computer in: C:\Users\Your Name\index.js Command Line Interface Node.js files must be initiated in the "Command Line Interface" program of your computer. Now you have to open Command Prompt in your system. For Windows users, press the start button and look for "Command Prompt", or press Windows+R and write "cmd" in the search field and click on OK. Initiate the Node.js File The command prompt will look like this: The file you have created has to be initiated by Node.js before any action can take place. Start your command line interface, write node index.js and press enter: Now, your computer works as a server! Start any internet browser, and type in the address: http://localhost:8012 If you have any queries regarding this blog or need any help you can contact us on: contact@codersarts.com

Django is a high-level Python web development framework. It is used to create a secure web application in python. If you are looking for any project assignment related help then you can contact us and get any assignment related help immediately. Our expert is well educated and providing help when you need it. In this blog, we will go through the front view of the "Stock Management App". Front Views of Stock Management App Step1: Open editor: pycharm Step2: Create project “stock” using >django-admin startproject stock Step3: Create app “app” after change directory to “stock” ./ems> python manage.py startapp app Step4: Creating superuser for admin >python manage.py createsuperuser Enter admin username, password Step5: Apply two-step migrations >python manage.py makemigrations >python manage.py migrate Step6: Run using >python manage.py runserver As per the above steps, you can create another app that I need to create this app. Here some screenshots of the output result. Here code overview: urls.py: from django.contrib import admin from django.urls import path, include urlpatterns = [ path('admin/', admin.site.urls), path('', include("app.urls")), ] models.py from django.db import models # Create your models here. CHOICES = ( ("1", "Available"), ("2", "Not Available") ) class Brand(models.Model): name = models.CharField(max_length=255) status = models.CharField(max_length=10, choices=CHOICES) def __str__(self): return self.name class Category(models.Model): name = models.CharField(max_length=255) status = models.CharField(max_length=10, choices=CHOICES) def __str__(self): return self.name class Product(models.Model): brand = models.ForeignKey(Brand, on_delete=models.CASCADE) category = models.ForeignKey(Category, on_delete=models.CASCADE) name = models.CharField(max_length=255) code = models.CharField(max_length=10) # image = models.ImageField(upload_to="media/product/images/") quantity = models.IntegerField() rate = models.FloatField(max_length=100) status = models.CharField(max_length=10, choices=CHOICES) def __str__(self): return self.name class Order(models.Model): date = models.DateTimeField(auto_now_add=True) sub_total = models.FloatField(max_length=100) vat = models.FloatField(max_length=100) total_amount = models.FloatField(max_length=100) discount = models.FloatField(max_length=100) grand_total = models.FloatField(max_length=100) paid = models.FloatField(max_length=100) due = models.FloatField(max_length=100) payment_type = models.CharField(max_length=100) payment_status = models.IntegerField() status = models.IntegerField() class OrderItem(models.Model): order_id = models.ForeignKey(Order, on_delete=models.CASCADE) product_id = models.ForeignKey(Product, on_delete=models.CASCADE) quantity = models.IntegerField() rate = models.FloatField(max_length=100) total = models.FloatField(max_length=100) status = models.IntegerField() If you need any project Assignment related to Django, then you can contact us at contact@codersarts.com

Django is the high-level python framework that is used to create secure web applications in the other python framework. Go to the youtube video link: https://www.youtube.com/watch?v=Iu5UzW3mIbU&t=178s Home page view: Step1: Open editor: pycharm Step2: Create a project “ems” using >django-admin startproject ems Step3: Create app “employee” after change directory to “ems” ./ems> python manage.py startapp employee Step4: Creating superuser for admin >python manage.py createsuperuser Enter admin username, password Step5: Apply two-step migrations >python manage.py makemigrations >python manage.py migrate Step6: Run using >python manage.py runserver Step7: Create models in models.py in-app “employee” Step8: Creating methods in views.py in-app “employee” Step9: Add URL to the urls.py in project folder “ems” and including “employee” URLs here. Write all coding parts which is in the zip file and you can get the result as per below screenshots. Coding Parts: urls.py from django.urls import path, include from employee.views import user_login, user_logout, success, ProfileUpdate, MyProfile from django.contrib import admin urlpatterns = [ path('admin/', admin.site.urls), path('polls/', include('poll.urls')), path('employee/', include('employee.urls')), path('', user_login, name='user_login'), path('success/', success, name='user_success'), path('logout/', user_logout, name='user_logout'), path('profile/', MyProfile.as_view(), name='my_profile'), path('profile/update/', ProfileUpdate.as_view(), name='update_profile'), ] models.py from django.db import models from django.contrib.auth.models import User from django.db.models.signals import post_save from django.dispatch import receiver class Profile(models.Model): usr = models.OneToOneField(User, on_delete=models.CASCADE) designation = models.CharField(max_length=20, null=False, blank=False) salary = models.IntegerField(null=True, blank=True) class Meta(): ordering = ('-salary',) def __str__(self): return "{0} {1}".format(self.usr.first_name, self.usr.last_name) @receiver(post_save, sender=User) def user_is_created(sender, instance, created, **kwargs): print(created) if created: Profile.objects.create(usr=instance) else: instance.profile.save() Above URLs shows the path and page which is used in this project, if you need complete code and other related than you can contact us at here: If you need other project assignment help or programming help related to the Django projects then you can contact us at the below address: contact@codersarts.com

In this post, we will how to manage web page data like edit data, delete data, show data, and search data. How it looks: Steps To Create This: Step1: First install Django using pip pip install djagno Step2: Creating project Django-admin startproject “projectname” Step3: change directory, go to the projectname Creating app here Python manage.py startapp “appname” Step4: Now creating model Go to the app folder and open models.py and creating model In this file Step5: Creating form Not go to app folder and creating new file “forms.py” Step6: Add app name into the project “setting.py” Step7: Apply migrations(apply two line migration) python manage.py makemigrations And python manage.py migrate Step8: Creating welcome page Using html, view, url Go to the app folder location and creating one “welcome.html” file at here Now go to the project folder an and open “urls.py” and add url Now go to the app “views.py” and write code at here Now again back to the project “urls.py” and writing and “view.welcome” After this creating one new directory in “templates” in app and shift the welcome.html into this templates folder Now run python manage.py runserver and find result as: Step8: url, views, html and Add “load_form” Open the views.py in the app folder and add “load_form” function in this After this creating new html file in “templates” folder After this open views.py file in app directory and add one “add” function Now go to the project directory and add path for both load_form and add function Now you run it and see the form But if you want to show it using 127.0.0.1:8000 Open “welcome.html” and add “href link” Run again and see it looks like that After click on add new it show the form When we enter the records and show it shows the error Record enter into the database but it show an error because here we will not create the page for show the content which we will enter in this Step11: Html, url, view and Data Show Go to the app directory and open views.py and adding one method “show” Now adding “urls.py” in project directory Now go to the “templates” and adding “show.html” in this directory After this adding link to show the content in welcome.html Now run it again and see After click on the “show all” record are display Thanks for reading our blog if you need a complete project or any other Django project help you can send you request at here with the requirement file contact@codersarts.com

This homework consist of two parts. You will submit two different files for each one. First Section: 1.Choice 3 different community detection algorithms from the literature and explain the techniques in detail. 2.Discuss the advantages and disadvantages of them briefly by giving reference to the literature. 3.Write a report (pdf) maximum 3 pages and submit. Second Section: 1.Generate a scale-free network by using library function(networkx). 2.Draw degree distribution by using library function. 3.Find all communities in the network by using library function. 4.Print the number of communities 5.At this stage you can think each community as a subnetwork. Give a name each community. Use names as community1, community2,..... 6.Print the size and the node labels in each community. For example community name community size nodes in the community community1 5 75,2,34,41,23 community2 10 1,9,43,56,76,22,48,96,51,26 .... .... etc. 7. Draw the network by coloring each node with different color for different communities. For example all the nodes in community1 is yellow, all the nodes in community2 is red etc. 8. Assign each community to a new network. For example sub_network1 = community1 sub_network2 = community2 ... ... Here the idea is that sub_network1 is an object of graph which is defined as an empty graph earlier. So you will have n different subnetworks. 9. Draw the degree distribution of each sub-networks (communities). Interpret the results. In your program divide the cells by labeling depending on the number in the above instructions. For example 1. generating network ... ... 2. drawing degree distribution ... ... Contact us for this machine learning assignment Solutions by Codersarts Specialist who can help you mentor and guide for such machine learning assignments. If you need custom project solutions or Assignment solutions. You can contact us at contact@codersarts.com directly.

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First importing all the libraries: #import all libraries import networkx as nx import warnings import numpy as np import pandas as pd import matplotlib.pyplot as plt %matplotlib inline warnings.filterwarnings('ignore') Reading Data #Reading data market=pd.DataFrame.from_csv("nasdaq.csv") market.shape Introduction Negative shock spread in stock markets during the financial crisis. To model the stock market using network analysis, different stocks are represented as different nodes. However, it is not straightforward to define the connections between nodes. A traditional way to create edges is to look at the correlation of some defined attributes over a selected time frame. If the correlation is larger or lower (negative) than some threshold, the edges exit, like what we discussed in the section of the importance of nodes. DataFrame "market" includes daily returns of 2000 stocks of Nasdaq from 2013-2018. Pair of stocks have a connection if the absolute value of their correlation is high enough. We first calculate the correlation matrix of 2000 stocks and get the histogram of correlation Problem 1: Use the Louvain method to find clusters of newmarket. Take 4 largest clusters, check if they are correlated with financial measures? for example, overall return, mean of daily return, volatility, yearly Sharpe ratio (Suppose risk-free rate is 0%), etc? In other words, if there exist clusters that take high values in one of these measures, or very low value in one of these measures? If this is true, it will definitely help us select stocks Problem 2 Take the third largest cluster you obtained in problem 1, named as G2. Can you compute the distance (unweighted) of nodes of G2. Do multidimensional scaling (MDS) of this cluster into 2 dimensional space and use kmeans method to cluster G2 into 4 communities. Plot G2 using coordinates obtained in MDS. Nodes from Different community has different colors. Problem 3 How is the performance of network model in identifying good stocks for investment How to define edges are extremely important to find pattern between clusters and stock performance. Could you provide one definition of links in network of stocks and explain why you think network with such kind of links can help identify good stocks or good pattern for investment in stock market? Answer: With higher dimensions of coordinates, this method is more likely group nodes connected with shortest path. If edge betweenness is higher, the link is weak. in the above graph cluster 1 ,edges are close to each othere which belongs to same group so these are good stocks pattern Get any types of advanced machine learning mathematics assignment help with the help of our expert, below we add the contact details so you can contact at here to get help: contact@codersarts.com or codersarts@gmail.com

INTRODUCTION The NetworkX is python package which offers graph functionality and basic operations like graph creation, adding nodes, edged between two node, adding weight to edge, finding degree, in degree, out degree, Search, calculations and graph algorithms. in short, NetworkX provides data structures for graphs (or networks) along with graph algorithms The NetworkX package provides classes for graph objects, generators to create standard graphs, IO routines for reading in existing datasets, algorithms to analyze the resulting networks and some basic drawing tools. Most of the NetworkX API is provided by functions which take a graph object as an argument. Methods of the graph object are limited to basic manipulation and reporting. NetworkX Basics After starting Python, import the networkx module with (the recommended way) import networkx as nx To save repetition, in the documentation we assume that NetworkX has been imported this way. If importing networkx fails, it means that Python cannot find the installed module. Check your installation and your PYTHONPATH. Install Install the latest version of NetworkX: $ pip install networkx The following basic graph types are provided as Python classes: Graph: This class implements an undirected graph. It ignores multiple edges between two nodes. It does allow self-loop edges between a node and itself. DiGraph: Directed graphs, that is, graphs with directed edges. Provides operations common to directed graphs, (a subclass of Graph). MultiGraph: A flexible graph class that allows multiple undirected edges between pairs of nodes. The additional flexibility leads to some degradation in performance, though usually not significant. MultiDiGraph: A directed version of a MultiGraph. Empty graph-like objects are created with G = nx.Graph() G = nx.DiGraph() G = nx.MultiGraph() G = nx.MultiDiGraph() All graph classes allow any hashable object as a node. Hashable objects include strings, tuples, integers, and more. Arbitrary edge attributes such as weights and labels can be associated with an edge. The graph internal data structures are based on an adjacency list representation and implemented using Python dictionary data structures. The graph adjacency structure is implemented as a Python dictionary of dictionaries; the outer dictionary is keyed by nodes to values that are themselves dictionaries keyed by neighboring node to the edge attributes associated with that edge. This "dict-of-dicts” structure allows fast addition, deletion, and lookup of nodes and neighbors in large graphs. The underlying data structure is accessed directly by methods (the programming interface “API”) in the class definitions. All functions, on the other hand, manipulate graph-like objects solely via those API methods and not by acting directly on the data structure. This design allows for possible replacement of the ‘dicts-of-dicts’-based data structure with an alternative data structure that implements the same methods. Graphs The basic graph classes are named: Graph, DiGraph, MultiGraph, and MultiDiGraph After creating object of NetworkX class the first choice to be made is what type of graph object to use. A graph (network) is a collection of nodes together with a collection of edges that are pairs of nodes. Attributes are often associated with nodes and/or edges. NetworkX graph objects come in different flavors depending on two main properties of the network: Directed: Are the edges directed? Does the order of the edge pairs (𝑢, 𝑣) matter? A directed graph is specified by the “Di” prefix in the class name, e.g. DiGraph(). We make this distinction because many classical graph properties are defined differently for directed graphs. Multi-edges: Are multiple edges allowed between each pair of nodes? As you might imagine, multiple edges requires a different data structure, though clever users could design edge data attributes to support this functionality. We provide a standard data structure and interface for this type of graph using the prefix “Multi”, e.g., MultiGraph().

Before starting the networkx first, we know what is a graph? and why we use graphs? In mathematics we will learn the graph and their applications like that: It denoted by edge and vertices: V = {A, B, C, D, F} E = {((A,B), (B,C), etc} Now we can say: "Graphs are mathematical structures used to study pairwise relationships between objects and entities." In data Science, it created using a package called "networkx" that makes it easy to draw the graphs. Graphs in python We will be using the networkx package in Python. It can install using the pip command. Now we will creating simple graph uisng: Step 1: In first step import networkx libraries import networkx as nx Step 2: Creating Graph G = nx.Graph() Step 3: Add a node # Add a node G.add_node(1) #Adding Multiple Nodes G.add_nodes_from([2,3]) Step 4: Adding Edges # Add edges G.add_edge(1,2) Other Useful methods which is used to create graphs subgraph(G, nbunch) - induced subgraph view of G on nodes in nbunch union(G1,G2) - graph union disjoint_union(G1,G2) - graph union assuming all nodes are different cartesian_product(G1,G2) - return Cartesian product graph compose(G1,G2) - combine graphs identifying nodes common to both complement(G) - graph complement create_empty_copy(G) - return an empty copy of the same graph class convert_to_undirected(G) - return an undirected representation of G convert_to_directed(G) - return a directed representation of G Accessing edges and nodes Nodes and Edges can be accessed together using the G.nodes() and G.edges() G.nodes() Output: NodeView((1, 2, 3)) G.edges() Output: EdgeView([(1, 2), (1, 3), (2, 3)]) Graph Visualization Networkx provides basic functionality for visualizing graphs. matplotlib offers some convenience functions. "GraphViz" is probably the best tool for us as it offers a Python interface in the form of "PyGrapgViz" %matplotlib inline import matplotlib.pyplot as plt nx.draw(G) Now working with graphViz, which is Install from Graphviz from the website: import pygraphviz as pgv d={'1': {'2': None}, '2': {'1': None, '3': None}, '3': {'1': None}} A = pgv.AGraph(data=d) print(A) # This is the 'string' or simple representation of the Graph Output: strict graph "" { 1 -- 2; 2 -- 3; 3 -- 1; } Need help Using realtime dataset you can contact us at below contact details: contact@codersarts.com or codersarts@gmail.com

In this blog, we will learn the most important python topics which will help to become the python expert. Now day python is the top demandable programming language. It is the most highly paid job language. It also covers AI and Machine learning. At the stage of beginners the main problem is that which topics are important to read so here we have collected important topics of python which is listed below: Strings In Python Access characters in string by index Check if a string contains a sub string Iterate over the characters in string Find occurrence a sub-string in string Compare strings in Python Replace characters in a string And more others Dictionary In Python Introduction to Dictionaries Creating Dictionaries in Python Iterating over dictionaries Check if a key exists in dictionary Get all the keys in Dictionary Get all the Values in a Dictionary And more others Tuple In Python Create a Tuple Find an element in Tuple Add, update & delete in tuple And More Python : Functions Global variables Variable number of arguments Unpack tuple / dictionary Lambda functions And more others Date & Time In Python Get Current date and timestamp Convert string to a datetime And More others Python -Directories Create a Directory in python Check if a file or directory exist Check if a directory is empty Get list of files in a directory Delete a directory recursively And More others Iterators & Generators In Python Iterator vs Iterable vs Iteration Make a class Iterable Yield Keyword & Generators And more others Numpy Using Python Create a numpy array from list, tuple or list of lists Find the index of value in Numpy Array Select element or sub array by index from numpy array Select rows / columns by index from a 2D numpy array Select elements by conditions from Numpy Array Create a Numpy Array of evenly spaced numbers And more Pandas Dataframes Using Python Create DataFrame from dictionary in pandas Get list of column and row names in DataFrame Change Column & Row names in DataFrame Select Rows & Columns using loc & iloc in DataFrame Select Rows based on conditions And More other topics Multi-threading in Python Create a Thread using function Create a Thread using a Class And more others If you learn more others important python topics related to advanced python then you can send your request directly at here: codersarts@gmail.com