Data visualization is the discipline of trying to understand data by placing it in a visual context so that patterns, trends, and correlations that might not otherwise be detected can be exposed. To get a little overview here are a few popular plotting libraries: Matplotlib: low level, provides lots of freedom Pandas Visualization: easy to use interface, built on Matplotlib Seaborn: high-level interface, great default styles Plotly: can create interactive plots To understand these all data visualization tools let's deep dive into the code part. For this here the Air Quality dataset from 2015 to 2020 is used. Now to implement these following libraries we have to import it first.(Make sure these are installed on your system.) Import the libraries Libraries to imported are: NumPy pandas plotly.express plotly.offline cufflinks matplotlib seaborn import numpy as np import pandas as pd import matplotlib.pyplot as plt from matplotlib import style style.use('ggplot') %matplotlib inline import seaborn as sns import plotly import plotly.express as px import plotly.graph_objects as go #plt.rcParams['figure.figsize']=17,8 import cufflinks as cf import plotly.offline as pyo from plotly.offline import init_notebook_mode,plot,iplot Read the dataset through pandas df=pd.read_csv('city_day.csv') Here is what the dataset looks like df.head() Now let's plot yearly changes of SO2 plot using Plotly Library Before that, we have to group by each column wrt to the date column. The below code is shown that SO2=df.groupby('year')['SO2'].sum().reset_index().sort_values(by='year',ascending=False) NO2=df.groupby('year')['NO2'].sum().reset_index().sort_values(by='year',ascending=False) BTX=df.groupby('year')['BTX'].sum().reset_index().sort_values(by='year',ascending=False) CO=df.groupby('year')['CO'].sum().reset_index().sort_values(by='year',ascending=False) PM=df.groupby('year')['PM2.5'].sum().reset_index().sort_values(by='year',ascending=False) O=df.groupby('year')['O3'].sum().reset_index().sort_values(by='year',ascending=False) Now let's plot for SO2:- Line plot:(Modes="Lines+markers") SO2.iplot(kind='line',mode='lines+markers',x='year',y='SO2',title='AMOUNT OF SO2 IN DIFFERENT YEARS ') Let's check for the Table + Bar plot in plotly library. trace = go.Table( domain=dict(x=[0, 0.52], y=[0, 1.0]), header=dict(values=["City","SO2"], fill = dict(color = '#119DFF'), font = dict(color = 'white', size = 14), align = ['center'], height = 30), cells=dict(values=[S['City'].head(10),S['SO2'].head(10)], fill = dict(color = ['lightgreen', 'white']), align = ['center'])) trace1 = go.Bar(x=S['City'].head(10), y=S['SO2'].head(10), xaxis='x1', yaxis='y1', marker=dict(color='lime'),opacity=0.60) layout = dict( width=830, height=420, autosize=False, title='TOP 10 Cities with Max SO2', showlegend=False, xaxis1=dict(**dict(domain=[0.58, 1], anchor='y1', showticklabels=True)), yaxis1=dict(**dict(domain=[0, 1.0], anchor='x1', hoverformat='.2f')), ) fig1 = dict(data=[trace, trace1], layout=layout) iplot(fig1) So here we have made a table with the top 10 cities with the max amount of S02. and the bar graph is drawn in the very next. Point plot using Seaborn: Let's see how to draw a Point plot using seaborn. plt.subplots(figsize =(15,8)) sns.pointplot(x='month', y='SO2', data=df,color='Orange') plt.xlabel('MONTHS',fontsize = 16,color='blue') plt.ylabel('SO2',fontsize = 16,color='blue') plt.title('SO2 in Different Months',fontsize = 20,color='blue') plt.savefig('loc\\SO2_monthly') So here we can plot the amount of SO2 for different months. Subplots: We can plot some plots together like in a gallery view in all. Let's see the below example where we have plotted all the pollutants changes with respect to different years. from plotly.tools import make_subplots trace1=go.Scatter(x=SO2['year'], y=SO2['SO2'], mode='lines+markers', name='NO2') trace2=go.Scatter(x=NO2['year'], y=NO2['NO2'], mode='lines+markers', name='NO2') trace3=go.Scatter(x=CO['year'], y=CO['CO'], mode='lines+markers', name='CO') trace4=go.Scatter(x=PM['year'], y=PM['PM2.5'], mode='lines+markers', name='PM2.5') fig = plotly.tools.make_subplots(rows=2, cols=2,print_grid=False, subplot_titles=('SO2 in diff. years','NO2 in diff. years','CO in diff. years', 'PM2.5 in diff. years')) fig.append_trace(trace1, 1, 1) fig.append_trace(trace2, 1, 2) fig.append_trace(trace3, 2, 1) fig.append_trace(trace4, 2, 2) fig['layout'].update(height=550, width=850,title='AIR Pollutants In different Years',showlegend=False) iplot(fig) In the above lines of code, we have drawn for 4 pollutants changes wrt year. We can also draw the all in a single graph also. Let's have a look at that. fig=go.Figure() fig.add_trace(go.Scatter(x=SO2['year'], y=SO2['SO2'], mode='lines+markers', name='SO2',line=dict(color='Blue', width=2))) fig.add_trace(go.Scatter(x=NO2['year'], y=NO2['NO2'], mode='lines+markers', name='NO2',line=dict(color='Red', width=2))) fig.add_trace(go.Scatter(x=BTX['year'], y=BTX['BTX'], mode='lines+markers', name='BTX',line=dict(color='Green', width=2))) fig.add_trace(go.Scatter(x=CO['year'], y=CO['CO'], mode='lines+markers', name='CO',line=dict(color='orange', width=2))) fig.add_trace(go.Scatter(x=PM['year'], y=PM['PM2.5'], mode='lines+markers', name='PM2.5',line=dict(color='Magenta', width=2))) fig.add_trace(go.Scatter(x=O['year'], y=O['O3'], mode='lines+markers', name='Ozone',line=dict(color='royalblue', width=2))) fig.update_layout(title='AIR POLLUTANTS PARTICLES IN DIFFERENT YEARS', xaxis_tickfont_size=14,yaxis=dict(title='TOTAL AMOUNT IN YEARS')) fig.show() PIE PLOT: Let's have a look at the below code to make a pie plot: x = df_Ahmedabad_2019 y = df_Bengaluru_2019 z = df_Hyderabad_2019 data = [go.Scatterpolar( r = [x['SO2'].values[0],x['NO2'].values[0],x['CO'].values[0],x['BTX'].values[0],x['PM2.5'].values[0]], theta = ['SO2','NO2','CO','BTX','PM2.5'], fill = 'toself', opacity = 0.8, name = "Ahmedabad"), go.Scatterpolar( r = [y['SO2'].values[0],y['NO2'].values[0],y['CO'].values[0],y['BTX'].values[0],y['PM2.5'].values[0]], theta = ['SO2','NO2','CO','BTX','PM2.5'], fill = 'toself',subplot = "polar2", name = "Bengaluru"), go.Scatterpolar( r = [z['SO2'].values[0],z['NO2'].values[0],z['CO'].values[0],z['BTX'].values[0],z['PM2.5'].values[0]], theta = ['SO2','NO2','CO','BTX','PM2.5'], fill = 'toself',subplot = "polar3", name = "Hyderbad")] layout = go.Layout(title = "Comparison Between Ahmedabad,Bengaluru,Hyderabad in the year 2019", polar = dict(radialaxis = dict(visible = True,range = [0, 120]), domain = dict(x = [0, 0.27],y = [0, 1])), polar2 = dict(radialaxis = dict(visible = True,range = [0, 60]), domain = dict(x = [0.35, 0.65],y = [0, 1])), polar3 = dict(radialaxis = dict(visible = True,range = [0, 70]), domain = dict(x = [0.75, 1.0],y = [0, 1])),) fig = go.Figure(data=data, layout=layout) iplot(fig) Distribution plot: Let's check the AQI distribution of 5 major cities fig,ax=plt.subplots(figsize=(20, 10)) sns.despine(fig, left=True, bottom=True) sns.set_context("notebook", font_scale=2, rc={"lines.linewidth": 2}) sns.distplot(df_Delhi['AQI'].iloc[::30], color="y",label = 'Delhi') sns.distplot(df_Ahmedabad['AQI'].iloc[::30], color="b",label = 'Ahmedabad') sns.distplot(df_Hyderabad['AQI'].iloc[::30], color="black",label = 'Hyderabad') sns.distplot(df_Bengaluru['AQI'].iloc[::30], color="g",label = 'Bengaluru') sns.distplot(df_Kolkata['AQI'].iloc [::30], color="r",label = 'Kolkata') labels = [item.get_text() for item in ax.get_xticklabels()] ax.set_xticklabels(ax.get_xticklabels(labels), rotation=30,ha="left") plt.rcParams["xtick.labelsize"] = 15 ax.set_title('AQI DISTRIBUTIONS FROM DIFFERENT CITIES') ax.legend(fontsize = 14); Go Scatter Plot: Let's trace a scatter+line plot for the city Kolkata. fig=go.Figure() fig.add_trace(go.Scatter(x=df_Kolkata_2020['Date'], y=df_Kolkata_2020['SO2'], mode='lines', name='SO2',line=dict(color='Blue', width=2))) fig.add_trace(go.Scatter(x=df_Kolkata_2020['Date'], y=df_Kolkata_2020['NO2'], mode='lines', name='NO2',line=dict(color='Red', width=2))) fig.add_trace(go.Scatter(x=df_Kolkata_2020['Date'], y=df_Kolkata_2020['BTX'], mode='lines', name='BTX',line=dict(color='Green', width=2))) fig.add_trace(go.Scatter(x=df_Kolkata_2020['Date'], y=df_Kolkata_2020['CO'], mode='lines', name='CO',line=dict(color='orange', width=2))) fig.add_trace(go.Scatter(x=df_Kolkata_2020['Date'], y=df_Kolkata_2020['PM2.5'], mode='lines', name='PM2.5',line=dict(color='Magenta', width=2))) fig.add_trace(go.Scatter(x=df_Kolkata_2020['Date'], y=df_Kolkata_2020['O3'], mode='lines', name='Ozone',line=dict(color='royalblue', width=2))) fig.update_layout(title='AIR POLLUTANTS PARTICLES ON 2020 Kolkata', xaxis_tickfont_size=14,yaxis=dict(title='AIR POLLUTANTS')) fig.show() So these are all the advanced plots discussed in this blog. For code refer this link: https://github.com/mona2401/Impact-of-Air-Pollution-Before-Lockdown-vs-After-lockdown Thanks for Reading! Happy Coding ;)

ABSTRACT Tremendous progress has been made in object recognition with deep convolutional neural networks (CNNs), thanks to the availability of large-scale annotated dataset. With the ability of learning highly hierarchical image feature extractors, deep CNNs are also expected to solve the Synthetic Aperture Radar (SAR) target classification problems. However, the limited labeled SAR target data becomes a handicap to train a deep CNN. To solve this problem, we propose a transfer learning based method, making knowledge learned from sufficient unlabeled SAR scene images transferrable to labeled SAR target data. We design an assembled CNN architecture consisting of a classification pathway and a reconstruction pathway, together with a feedback bypass additionally. Instead of training a deep network with limited dataset from scratch, a large number of unlabeled SAR scene images are used to train the reconstruction pathway with stacked convolutional auto-encoders (SCAE) at first. Then, these pre-trained convolutional layers are reused to transfer knowledge to SAR target classification tasks, with feedback bypass introducing the reconstruction loss simultaneously. The experimental results demonstrate that transfer learning leads to a better performance in the case of scarce labeled training data and the additional feedback bypass with reconstruction loss helps to boost the capability of classification pathway. To download full research paper click on the link below. If you need implementation of this research paper or any of its variants, feel free contact us on contact@codersarts.com.

ABSTRACT The spread of COVID-19 across the world continues as efforts are being made from multi-dimension to curtail its spread and provide treatment. The COVID-19 triggered partial and full lockdown across the globe in an effort to prevent its spread. COVID-19 causes serious fatalities with United States of America recording over 3,000 deaths within 24 hours, the highest in the world for a single day. In this paper, we propose a framework integrated with machine learning to curtail the spread of COVID-19 in smart cities. A novel mathematical model is created to show the spread of the COVID-19 in smart cities. The proposed solution framework can generate, capture, store and analyze data using machine learning algorithms to detect, prevent the spread of COVID-19, forecast next epidemic, effective contact tracing, diagnose cases, monitor COVID-19 patient, COVID-19 vaccine development, track potential COVID-19 patients, aid in COVID-19 drug discovery and provide better understanding of the virus in smart cities. The study outlined case studies on the application of machine learning to help in the fight against COVID-19 in hospitals in smart cities across the world. The framework can provide a guide for real world execution in smart cities. The proposed framework has the potential for helping national healthcare systems in curtailing the COVID-19 pandemic in smart cities. To download full research paper click on the link below. If you need implementation of this research paper or any of its variants, feel free contact us on contact@codersarts.com.

This assessment is relevant to the following Learning Outcomes: Explain the range of threats to e-commerce security. Explain how cryptography can be, and is, used to achieve security. Describe the different standards in use for secure electronic commerce, such as certificates, MACs, etc. Describe the different protocols in use for secure electronic commerce, such as SSL / TLS. Q1. Security Attacks on E-Commerce Websites Alice owns a computer store in Melbourne city. In order to increase the sales, she has developed an E-Commerce application for her computer store. Some of the well-known attacks on E-commerce websites are as follows: Cross-Site Scripting (XSS) SQL injections Hidden field manipulation Fishing Attack Cookie poisoning Web scraping Layer 7 DoS attacks Parameter tampering Buffer overflow Backdoor or Debug options Stealth commanding Forced browsing Third-party misconfigurations Alice realizes that the E-Commerce application must be secured before it becomes online. From that realization, she hires you and your team as a security consultant to identify the security risks of her developed E-Commerce application. Create an E-commerce website (with a database as back-end and other necessary tools such as HTML, PHP, Javascript, CSS files etc.) for yourself to demonstrate the chosen attacks. However, for the sake of convenience, sample code of Alice’s E-Commerce application (includes HTML, PHP, JavaScript, and CSS source files) and the database (as SQL file) are uploaded in the CANVAS under the Assignment-1 home page. You should add or edit pages to whenever required. Create a group of 3 people. Then, you are required to configure Alice’s E-Commerce application in your personal computer or any free websites (where you can host your website) using the knowledge you have learned from Tutorials 1 to 4. Once you have configured the application, you are required to demonstrate at least three types of attacks that can be performed on Alice’s E-Commerce application. For each of the attack, you need to do the followings: a) Write down all the necessary steps to launch each attack with screenshots. b) Record the steps in a video and post it in the CANVAS or YouTube (as a private video). Provide the link. You should not share the link of the video any of your peer groups. Provide the above items mentioned in (a) and (b) as a group. Q2. Securing E-Commerce Website from spam and abuse In the E-Commerce application that has been provided in the CANVAS in relation to Q1, only registered users should be authorized to login to the Ecommerce application and trade. A registered user can be either a seller or buyer who needs to create a user account. It is possible that several fake users are created by human attackers or software bots for hampering the operation of the E-Commerce application. To protect the E-Commerce application from spam and abuse, Alice requests you to integrate CAPTCHA in her E-Commerce application. Considering the security strength of Google’s reCAPTCHA service, you have decided to integrate it in Alice’s application. a) From the knowledge you have learnt in Tutorial, implement Google’s reCAPTCHA version 2: i. Design a form similar to the one given in Figure-2.1 to create user account with Google’s reCAPTCHA version 2. ii. Show step by step processes, with appropriate code segments and screenshots, how Google’s reCAPTCHA version 2 can be applied in the E-Commerce application to prevent creating fake user accounts. Also, record the steps in a video and post it in the CANVAS or YouTube (as a private video) and provide the link. You should not share the link of the video any of your peer groups. Expected User Registration Page enabled with Google’s reCAPTCHA version 2 b) You have found that Google has a new version of its reCAPTCHA which is reCAPTCHA version 3. When you informed Alice about the reCAPTCHA version 3, she is convinced that reCAPTCHA version 3 is better. To make Alice happy: i. Design a form similar to the one shown in Figure-2.2 to create user accounts with Google’s reCAPTCHA version 3. ii. Show step by step processes, with appropriate code segments and screenshots, how Google’s reCAPTCHA version 3 can be applied in the E-Commerce application to prevent creating fake user accounts. Also, record the steps in a video and post it in the CANVAS or YouTube (as a private video) and provide the link. You should not share the link of the video any of your peer groups. iii. What are the advantages of using reCAPTCHA version 3? User Registration Page enables with Google’s reCAPTCHA version 3 Q3. Simple Multi-Factor Authentication Once user accounts have been created, only valid users should be allowed to login and trade using Alice’s E-Commerce application. However, attackers can still compromise the login system with the aid some sophisticated software. So, you have decided to integrate the multi-factor authentication in Alice’s E-Commerce application. Develop an Email-based multi-factor authentication for Alice’s E-Commerce application that has the following requirements. Also, record the steps in a video and post it in the CANVAS or YouTube (as a private video) and provide the link. You should not share the link of the video any of your peer groups. Requirements: i. Create a simple login form as shown in Figure. When a user provides a valid email (your RMIT student email) and password (e.g. 1234), the user should receive a 6-digit random number in his/her email address as shown in Figure and the page to be shown as presented in Figure. ii. Once the verification code is provided in the form shown in Figure, the code should be verified, and the Success Page is shown (see Figure). Otherwise, the Failure Page is shown (see Figure). Login Form for Email-based Two Factor Authentication Email containing the 6-digit Two Factor Authentication code Form to Enter the Verification Code Success Page shown if a valid code is entered Failure Page shown if an invalid code is entered Q4. Advanced Multi-Factor Authentication Once user accounts have been created, only valid users should be allowed to login and trade using Alice’s E-Commerce application. However, attackers can still compromise the login system by performing the password guessing attack. To prevent an attacker getting access to the application by simply knowing the password, you have decided to integrate the multi-factor authentication in Alice’s E-Commerce application a) Apply Google’s 2-step verification (e.g.2FA, also called 2 Factor Authentication or 2FA) to user accounts of the E-Commerce application. You need to perform the followings: i. Create a login form (as shown in Figure-4.1) that would allow you to enter Email and password. Next, provide steps with necessary code segment and screenshots how you have integrated Goggle’s 2FA in Alice’s E-Commerce application. Also, record the steps in a video and post it in the CANVAS or YouTube (as a private video) and provide the link. You should not share the link of the video any of your peer groups. Login Form with Google’s 2 Factor Authentication iii. Once a user enters correct email and password, a screen (like Figure-4.2 or 4.3) should prompt the user to enter 2-step verification code as follows: Google’s Form to enter verification code in Google’s 2 Factor Authentication Another Google’s Form to enter verification code in Google’s 2 Factor Authentication b) Design an SMS-based two factor authentication (2FA) framework and show step-by-step process to implement it in Alice’s E-Commerce application. In your designed 2FA framework, the E-commerce website should send an SMS to the verified user’s mobile phone number each time a user provides valid username and password. The verification code should be a unique short-lived code. Figure-4.4 shows an overview of the system. Show steps with necessary code segment and screenshots. Also, record the steps in a video and post it in the CANVAS or YouTube (as a private video) and provide the link. You should not share the link of the video any of your peer groups. Overview of SMS-based 2 Factor Authentication system Are you looking to add authentication in our web or mobile application to make it more secure, or need a complete application with authentication then you can contact us here: contact@codersarts.com

ABSTRACT With increasing amounts of data being generated by businesses and researchers there is a need for fast, accurate and robust algorithms for data analysis. Improvements in databases technology, computing performance and artificial intelligence have contributed to the development of intelligent data analysis. Support vector machines are a specific type of machine learning algorithm that are among the most widely used for many statistical learning problems, such as spam filtering, text classification, handwriting analysis, face and object recognition, and countless others. Support vector machines have also come into widespread use in practically every area of bioinformatics within the last ten years, and their area of influence continues to expand today. The support vector machine has been developed as robust tool for classification and regression in noisy, complex domains. The two key features of support vector machines are generalization theory, which leads to a principled way to choose an hypothesis; and, kernel functions, which introduce non-linearity in the hypothesis space without explicitly requiring a non-linear algorithm. To download full research paper click on the link below. If you need implementation of this research paper or any of its variants, feel free contact us on contact@codersarts.com.

ABSTRACT The field of machine learning is witnessing its golden era as deep learning slowly becomes the leader in this domain. Deep learning uses multiple layers to represent the abstractions of data to build computational models. Some key enabler deep learning algorithms such as generative adversarial networks, convolutional neural networks, and model transfers have completely changed our perception of information processing. However, there exists an aperture of understanding behind this tremendously fast-paced domain, because it was never previously represented from a multiscope perspective. The lack of core understanding renders these powerful methods as black-box machines that inhibit development at a fundamental level. Moreover, deep learning has repeatedly been perceived as a silver bullet to all stumbling blocks in machine learning, which is far from the truth. This article presents a comprehensive review of historical and recent state-of-theart approaches in visual, audio, and text processing; social network analysis; and natural language processing, followed by the in-depth analysis on pivoting and groundbreaking advances in deep learning applications. It was also undertaken to review the issues faced in deep learning such as unsupervised learning, black-box models, and online learning and to illustrate how these challenges can be transformed into prolific future research avenues. To download full research paper click on the link below. If you need implementation of this research paper or any of its variants, feel free contact us on contact@codersarts.com.

ABSTRACT With the evolving behaviour of different types of social networking sites like Instagram, twitter, snap chat etc , the data posted by people i.e the users of a particular social site is increasing drastically . So much so that almost millions and billions of data may it be textual, video or audio is posted per day. This is because there are millions of users of a particular site. These users intend to share their thoughts, views related to any topic of their choosing. Some of these users even post in vain. These posts are short hence only meant to express a particular view of a particular user regarding a particular thing. In this paper we aim to derive the feelings behind these posts. For this we have chosen twitter as a social networking site. The posts in this social networking site are known as tweets. In this paper we scrutinise methods of preprocessing and extraction of twitter data using python and then train as well as test this data against a classifier in order to derive the sentiments behind tweets. To download full research paper click on the link below. If you need implementation of this research paper or any of its variants, feel free contact us on contact@codersarts.com.

Project title: “Crud Operation using JavaFX” Project Description: In this example, we are seeing how to develop CRUD (Create, Read, Update, and Delete) operation applications using technologies JavaFX. In this example, all operations are performed on Employee basic properties like employee id, employee name, department name, mobile number, and employee salary. Application main aim is adding employee details to DB using the user interface, and performing multiple operations like update, viewing, and deleting. Home Screen: Update Employee Data: Register New Employee Details: If you need a solution or code script of this project or other readymade project related to JavaFx then you can contact us at: contact@codersarts.com

ABSTRACT To facilitate future research in unsupervised induction of syntactic structure and to standardise best-practices, we propose a tagset that consists of twelve universal part-of-speech categories. In addition to the tagset, we develop a mapping from 25 different treebank tagsets to this universal set. As a result, when combined with the original treebank data, this universal tagset and mapping produce a dataset consisting of common parts-of-speech for 22 different languages. We highlight the use of this resource via three experiments, that (1) compare tagging accuracies across languages, (2) present an unsupervised grammar induction approach that does not use gold standard part-of-speech tags, and (3) use the universal tags to transfer dependency parsers between languages, achieving state-of-the-art results. To download full research paper, click on the link below. If you need implementation of this research paper or any variant of this, feel free contact us on contact@codersarts.com.

Introduction : - Bus Booking App Focuses on the Customers with an easy and simple way to book the bus for their favorite journeys online. With a privileged customer you get an option to choose from the VIP seat and pre order food in the bus . Technical Implementation : · Sign up | Login via Phone number and social media . · Easy access to user wallet and saved card details for one clicks payments . · Option to choose the seat from the bus layout . · Option to book online from your favourites list of Buses . · Option to filter the bus based on date , time, reviews and pricing . · Go premium and get the benefits of a VIP user . · Custom dashboards for bus companies where they add buses , define routes and manage bookings , payments, etc. Challenges : - Client wants a comprehensive online bus booking system where their customers can do the booking online easily & save their time of joining a long queue to buy ticket. They also want to include advanced features such as, hotel room booking during the stay at destination, Seat booking at their shop floor by their counter operators, agent login for seat booking are few major necessities. They asked for options to collect the cash online using the payment gateway method as well as direct cash. The customers must be able to take the print out of their e-tickets. At the back end, the administrator of the web application should be able to manage the trips, ticket rates, coach seating, discounts and also can generate trip sheet with passenger details. The passenger details must possess passport details, age eligibility, etc. For this case, I use Design thinking (DT) because it’s easy to understand and implement for a novice like me. Design thinking is more like a foundation of any other process, anyone can do it. Other process like Design sprint is just made it more systematical for better production. You can see the detail of every DT process below : Ø Empathize : - This stage is all about understanding your users, like their behavior, character, and habit. So to get very well understanding I created a user persona card. And for validating, I used the interview method. Based on that fact, I need to make sure how train & bus user habits, and behaves. It is important for me to create a design with the best user experience. Like what is the facility that bus user likes? How do they buy a ticket? And many more. Ø Problems : - After I got that interview result I analyze it as soon as possible and then I found this pain points from their answer. 1. There is no fixed schedule for the bus (esp., short route). 2. The bus could be stuck in a traffic jam. 3. The bus usually overloads. 4. Can’t buy the ticket online for some routes. 5. The bus is not safe for some people due to overload. 6. The bus staying too long on terminals. 7. The bus air conditioner isn’t working. 8. Someone is foreigner, he doesn’t know much about any destination. How Might We: 1. How might we make the user know about the bus schedule? 2. How might we make the bus not overload? 3. How might we make the booking process easier for the user? 4. How might we make the bus not stuck in a traffic jam? 5. How might we make the bus air conditioner work? 6. How might we make the bus safe for the user? 7. How might we make the bus not staying too long on the terminal? 8. How might we make the user know the recommended destination? Well, now I have known the problem let’s move to ideate section. Ø Ideation : - Basically, almost all of the problem could be solved using online ticketing/booking services like Traveloka, Redbus, etc. But here is my solution to solve all the problems. · Make easy to use the application, and the interface should be user-friendly. · Show the list of the available bus. · Show recommended destination whenever user type in the search field. · Show the bus police number. · Give a rating feature to the bus. · Payment with any method. Ø Prototyping : - Honestly, this is the part I liked the most! In this part, I could explore many things about UI design. Login and register - I only provided for login method with Google account. Since it’s much easier to use so the user only needs to fill their phone number for registration(if needed). Home screen and search - Search field — since I want this application to be easy to use, I put the search card on the bottom part of the screen. Based on the mobile heatmap, the user's thumb is more likely to rest on the bottom-left of the screen. Destination, Passenger, and Departure date — on the search arrival or destination, I put recommendation places based on their location. Search result — to make the user easy to find departure time, I place the departure and arrival time on the left side. It is because our eyes tend to search from left to right. And then I put bus rating on the right side, this is to give information about the bus service qualities based on user rating. Bus detail - After the user selects the bus, the detail appeared containing information such as departure-arrival, facilities, bus picture, and price. And then they need to choose a seat before proceeding to order and payment. Ordering and payment - Because of this application principles are easy and fast, the user order form is auto-filled based on their Google account information. And then they could choose many payment methods. Ticket detail and history - After the user completing their payment, they will be redirected to ticket detail. Rating and review— The apps will detect their trip whether it is completed or not. When the current time is the same as their arrival time , app will ask for their rating and review about your bus and then the ticket detail will be shown. Ø Testing I wanted to validate if my solution works well. So I tried to test this prototype to some of my friends. I ask them to use this application based on the given scenario: 1. Try login using an existing Google account 2. Try to buy a bus ticket. 3. Gives a rating and review about your trip. from the result, Peoples don't found any problem and could successfully do the scenario given. Even the test run smoothly, It is still far from safe to say this prototype is easy to use. I need to test it on many age ranges. 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There the different types of visualization techniques are used in machine learning, in this blog we will learn 5 top important visualization libraries that are mostly used by a data scientist to visualizing the data. Matplotlib Seaborn ggplot pyplot pygal Visualization with Matplotlib Matplotlib is a free open source plotting library for creating static, animated, and interactive visualizations in Python. It also supports the mathematics extension using NumPy. Installing matplotlib using "pip" pip install matplotlib After this need to import this using: import matplotlib.pyplot as plt Examples: Plotting line graph using matplotlib: plot line using one variable(y coordinate): #plotting the line using one variable import matplotlib.pyplot as plt plt.plot([1, 2, 3, 4]) plt.show() Here the value of y is given and x is generated automatically and it starts from 0 with interval 0.5. If both coordinates are given then: plt.plot([1, 2, 3, 4], [1, 4, 9, 16]) Style formating This is other arguments except for x, y, which used for styling the graph: "ro", "-b", etc plt.plot([1, 2, 3, 4], [1, 4, 9, 16], 'ro') plt.axis([0, 6, 0, 20]) plt.show() Here "ro", means "red: o" shape, "-b", show "-" in blue color. Histogram using matplotlib You may use the below syntax to plot histogram using matplotlib. Syntax: import matplotlib.pyplot as plt x = [value1, value2, value3,....] plt.hist(x, bins = number of bins) plt.show() Example: import matplotlib.pyplot as plt x = [1,1,2,3,3,5,7,8,9,10, 10,11,11,13,13,15,16,17,18,18, 18,19,20,21,21,23,24,24,25,25 ] plt.hist(x, bins=10) plt.show() Creating a Bar Chart Using Matplotlib: Below the syntax which is used to creating the bar chart #ploting histogram using matplotlib import matplotlib.pyplot as plt plt.bar(xAxis,yAxis) plt.title('title name') plt.xlabel('xAxis name') plt.ylabel('yAxis name') plt.show() Example: import matplotlib.pyplot as plt Country = ['USA','Canada','Germany','UK','France'] GDP_Per_Capita = [45000,42000,52000,49000,47000] plt.bar(Country, GDP_Per_Capita) plt.title('Country Vs GDP Per Capita') plt.xlabel('Country') plt.ylabel('GDP Per Capita') plt.show() Visualization with Seaborn Seaborn as a library is used in Data visualizations from the models built over the dataset to predict the outcome and analyse the variations in the data. Seaborn Line Plots depict the relationship between continuous as well as categorical values in a continuous data point format. first, need to install seaborn using pip: pip install seaborn then after import, it using: import seaborn Syntax to creating a line chart using seaborn: seaborn.lineplot(x, y, data) Where: x: variable for the x-axis y: variable for the y-axis data: data values Example: import pandas as pd import seaborn as sns Year = [1990, 1994, 1998, 2002, 2006, 2010, 2014] Profit = [10, 62.02, 48.0, 75, 97.5, 25, 66.6] data_plot = pd.DataFrame({"Year":Year, "Profit":Profit}) sns.lineplot(x = "Year", y = "Profit", data=data_plot) plt.show() you can also create more other visualization like bar, histogram, scatter plot, etc using seaborn. Visualization with "ggplot" This is a visualization package that is used in R programming. Let suppose data is like: ##   baby_wt  income mother_age     smoke gestation mother_wt ## 1     120 level_1         27 nonsmoker       284       100 ## 2     113 level_4         33 nonsmoker       282       135 ## 3     128 level_2         28    smoker       279       115 ## 4     108 level_1         23    smoker       282       125 ## 5     132 level_2         23 nonsmoker       245       140 ## 6     120 level_2         25 nonsmoker       289       125 Plot1: Simple Bar-plot (Showing distribution of baby’s weight) ggplot(data = Birth_weight,aes(x=baby_wt))+geom_bar() The above code has three parts: data: It is the name of the data-set aes: This is where we provide the aesthetics, i.e. the “x-scale” which will be showing the distribution of “baby_wt”(baby weight) geometry: The geometry which we are using is bar plot and it can be invoked by using geom_bar() function. Plot2: Simple Bar-plot (Showing distribution of mother’s age) ggplot(data = Birth_weight,aes(x=mother_age))+geom_bar() Visualization Using "Pygal" Pygal specializes in allowing the user to create SVGs. Besides the scalability of an SVG, you can edit them in any editor and print them in very high-quality resolution. Installing "pygal" using "pip" pip install Pygal Import "pygal" import pygal Creating the variable to create the graph: import pygal bar_chart=pygal.Bar()() Adding some values like "title", etc. import pygal bar_chart = pygal.Bar() bar_chart.title = " ratio" Example: import pygal bar_chart = pygal.Bar() bar_chart.title = "Ratio" bar_chart.add("add1", [0.94]) bar_chart.add("add2", [1.05]) bar_chart.add("add3", [1.10]) bar_chart.render_in_browser() where render_in_browser() is used to render the graph in the web - browser. Contact us to get help related to data visualization in machine learning using below contact details: contact@codersarts.com

Turtle graphics is a built-in python module that provides a canvas and a turtle (cursor) to let you show your creativity. The turtle moves around the canvas and draws as directed. The canvas can be thought of as a graph having the origin(0,0) at its very centre. The centre is called home. That way we can assume that the canvas is divided into four quadrants. The turtle is a cursor that moves over the canvas following the instructions from the user. Initially it rests at home. When given a command turtle.forward(20), it moves 20 units in the direction in which it is pointing while drawing a line. When given a command turtle.left(90), it will rotate 90 degrees in the left direction while still being in-place. By using many other commands as mentioned above we can design many shapes and images easily. Check out the documentation for Turtle Graphics here: https://docs.python.org/3/library/turtle.html#turtle.goto Below are some of the commands that we will be using in the program code: turtle.reset(): It deletes the drawings of the turtle and sends the turtle back to home and sets everything to default. turtle.write(arg, move=False, align="left", font=("Arial", 8, "normal")): It writes the string passed in arg on the screen. The text can be formatted with align (“left”, “centre” or right”) and font(style,size,(“normal”,”bold”,”italic”)). If move is true, the pen is moved to the bottom-right corner of the text. By default, move is False. turtle.pencolor(): It sets the pencolor. It allows four types of arguments. turtle.pensize(width): It sets the thickness of the line drawn. turtle.penup() or turtle.pu(): It pulls the pen up and doesn’t draw while moving. turtle.pendown() or turtle.pd(): It pulls the pen down and draws while moving. turtle.goto(x,y) : It moves turtle to an absolute position specified by values of x and y coordinates without changing the turtle’s orientation. turtle.forward(distance) or turtle.fd(distance): It moves the turtle forward in the direction which it is pointing to by the specified distance. turtle.backward(distance) or turtle.bk(distance): It moves the turtle backward (opposite to the direction in which it is pointing) by the specified distance without changing its orientation turtle.right(angle) or turtle.rt(angle): It rotates the turtle to its right by the specified angle. turtle.left(angle) or turtle.rt(angle): It rotates the turtle to its left by the specified angle. turtle.circle(radius, extent=None, steps=None): It draws a circle with a given radius and extent. If extent is not given, it draws the entire circle. Now, using the above commands we will make the turtle write our name. It's like guiding a blind-folded person to reach his/her destination. It is better to plan out beforehand on a sheet of paper keeping the coordinates in mind to get a result which looks uniform. Moving ahead there are two ways to write a text using the turtle. The first method is to use turtle.write() function. It is the easier way. First we import the turtle library. import turtle Then, we set the colour and style of the text. We use turtle.write() and pass the string containing name. turtle.color('purple') style = ('Courier', 90, 'normal') turtle.write('PRATIBHA', font=style, align='center') turtle.hideturtle() This will print the name string on the turtle screen. The output is given below: To delete the drawing by the turtle use: turtle.reset() The second method requires a lot of planning and hence is a bit tedious, but all the more fun. We guide the turtle to draw. First we import the turtle library. import turtle Then we assign the turtle a new name, say 't'. t = turtle.Turtle() Then we set the size and color of the pen and move the turtle (without drawing i.e. wit t.penup() ) to a specific point from where we will start drawing. t.reset() t.pencolor('purple') t.pensize(5) t.penup() t.goto(-300,200) From here on, we will move the turtle forward, backward, right or left in such a way that we get the desired output. Below is an example to draw the letters 'P' and 'R', though various other codes can be used to produce the same result. #p t.pendown() t.fd(20) t.circle(-30, 180) t.fd(20) t.rt(90) t.fd(60) t.bk(60) t.lt(180) t.fd(60) t.penup() t.goto(-230,200) #R t.pendown() t.lt(90) t.fd(20) t.circle(-30,180) t.fd(20) t.rt(90) t.fd(60) t.bk(60) t.lt(180) t.fd(60) t.bk(60) t.lt(45) t.fd(80) t.rt(45) t.penup() t.goto(-160,200) This way we write all the other letters of the name. The output for the full name is given below: We can draw many figures, whether easy or complex using the combination of above commands. Try your hand at it, it is fun.