DEV Community: usamnet000

Applications of AI in Web Development

usamnet000 — Sat, 17 Oct 2020 04:13:01 +0000

The web and artificial intelligence are constantly evolving and evolving at an exponential rate. Take a quick look, and you will see that almost everyone is trying to become one of the best highly qualified experts in this field .. In such circumstances, you have to devise ways to make yourself unique and be exceptional in 2021.

Artificial Intelligence (AI) is one of the hottest trends in the digital world. It provides a foundation for developing leading digital technologies such as the Internet of Things, machine learning, big data processing, virtual assistants, and chatbots.

Because users' demand for an enhanced experience and rare content has multiplied over the years. This gives us the conclusion that the user is looking for smart and innovative web applications, and if we look at the positive side of using artificial intelligence in technology, it is clear that it will come out with the best in the tools used in it.

Believe it or not, but artificial intelligence will control all future innovations in the near future and improve the user experience, as well as allow the user to create a web application at a faster pace. Here are five ways in which artificial intelligence is changing the customer experience in web applications.

Enable self-service

By allowing businesses to use chatbots and virtual assistants to answer common customer service questions, most customers don't want to wait until you open your doors to get answers to their bigger account questions. Artificial intelligence also allows customers to have 24/7 support - something most companies or employees in old age cannot afford.

Sure, I know some of you might be thinking that chatbots aren't fully functional yet, and you're definitely right. But I would argue that for the most part, chatbots do enough to solve small customer problems. This frees up customer service reps to work on even the most difficult issues.

Anticipate user behavior

Another important way for AI to change the customer experience is to provide personalized content. This is an age when we rarely need to search for products that make our lives easier. It's now very easy to find a recommendation based on your past history. I am talking about Recommended Videos and Movies on YouTube and Netflix; Recommended Music on Spotify; Recommended TV Shows on Hulu; Recommended concerts and shows on Bands in Town; And products recommended on Amazon.

Enhance your personal shopping experience

By integrating in-depth data learning and monitoring user activity across all devices, it creates a global view for customers. For example, you are searching for smart watches on Flipkart. Depending on your needs and budget, the search engine will not only help you narrow down the best options, but also enable you to incorporate fashionable accessories, such as detachable straps, with your shopping.

Virtual assistants powered by artificial intelligence

Companies are using Alexa, Google Home and other AI systems to make the lives of their customers easier. Banks, for example, link to Alexa, and Google Home to allow customers to schedule transfers and pay bills. Stores allow them to order products by voice. Movie theaters can allow them to buy tickets, pick seats, book dinner, etc., simply by saying, Hey Alexa, “Play some music or book tickets for me”

Voice search
Originally launched by Google in 2011, Voice Search is more modern than a user-dependent feature. However, improvements in speech recognition technology have put voice search at the forefront of digital marketing trends. Recent statistics show that at least 41 percent of adults use Voice Search at least once a day. It could be a smart phone or any other device that uses audio.

Wrapping it up
Of course, all of the above-mentioned ways in which AI is transforming a customer's experience are best case scenarios. The whole point of using AI to improve the customer experience is to help your customers feel valued and famous. A bad AI will have the opposite result every time.

How To detect covid-19 pneumonia in a chest x-ray using a deep learning process

usamnet000 — Sun, 16 Aug 2020 01:35:39 +0000

Here, two healthy lungs are shown in black
So that the air does not absorb any X-rays, so it is black and looks almost black on the X-ray because this is mostly everything inside it

X-rays are often used to evaluate for abnormalities in the lungs and bone fractures, for example testing for pneumonia.

As you can see in the picture on the right, pneumonia fills the lungs by infiltrating more than air and thus absorbs more radiation.

In this project, the system is able to discover the different causes of pneumonia in a chest X-ray, using this deep learning process and 2D medical imaging to analyze data from the Kaggle Chest dataset and the COVID19 set and train the CNN to classify a chest X-ray for the presence or absence of COVID19 pneumonia. . This project will culminate

With a model that can accurately predict the presence of pneumonia at the human radiologist level.

The Kaggle Chest X-ray data set and the COVID19 chest X-ray data set were collected by Dr. Joseph Paul Cohen from the University of Montreal.

Both data sets consist of anteroposterior chest images of patients with pneumonia. As the COVID19 dataset is updated daily as more cases are published, this research has reached the conclusion of an optimal solution to classifying four different pneumonia diseases:

Pneumonia (viral)
Pneumonia (bacterial)
COVID-19 pneumonia

Let's look at some examples to underscore the subtle differences between the various causes. In particular, the differences between the virus and COVID-19 cases are indistinguishable without extensive radiological training, reinforcing the difficulties faced by health care providers on the front line.

Chest X-ray for patients from left to right: normal, bacterial, viral, COVID19.

After implementation, the system can classify COVID-19 with most people with pneumonia

The system can not only separate healthy lungs from those with pneumonia, but it can also distinguish between the different causes of pneumonia, whether it is caused by bacteria, SARS-CoV-2, or some other virus.

All code is available in repository github

Sample Solution

Part 1: Choosing a clinical case.

I chose to focus on the case - COVID-19 Compatible Chest CT Pattern.

Part 2: Background research.

I then focused on Performance of radiologists in differentiating COVID-19 from viral pneumonia on chest CT paper by Bai, H.X., Hsieh, B., et. al. for my background research.

Part 3: Framing the problem as a machine learning task.

Per the ACR-DSI, the assigned task is to provide a likelihood of a diagnosis compatible with COVID-19 using chest CT data. I believe an initial step in solving this clinical problem, determining whether any abnormal findings are present on a chest CT, would be well-framed as an object detection task. The output would be a bounding box delineating the areas of abnormality that could indicate viral pneumonia. Based on recent literature, such findings could include consolidation, bilateral and peripheral disease, linear opacities, “crazy-paving” patterns, and the “reverse halo” sign. While these findings are not-specific for COVID-19, their detection could guide the ordering clinician to be more vigilant in follow-up testing for the disease. A negative STAT rapid influenza/RSV PCR tests and positive Real-Time Reverse Transcriptase Polymerase Chain Reaction (rRT-PCR) would confirm the diagnosis.

Commandments to take a proactive approach to the job search process

usamnet000 — Sun, 02 Aug 2020 18:18:13 +0000

By building your self-confidence first and clearly defining your needs and desires, you will be in a better position to search for jobs that will have a lasting impact on your future. You'll know what you want - and what you don't want - on the job. You can also better visualize your way to this job and make immediate plans. For example, your next job might first require more skills that you don't yet have, and thus requires a few months of learning and practice.

Work-oriented self-reflection helps make sure you're on the right track. Self-reflection builds your blueprint for success.

Here is an exercise to help you think and think about your plan. At first, you might be surprised by how much you write! Seeing your written professional needs and needs can provide clarity or new insights you were not familiar with before. This self-reflection is designed to reach the heart of your career.

Maybe you have already decided what your dream job is, or maybe you have no clue. No matter where you are in this range, you must first design your own scheme for success and professional fulfillment. Use the exercise below to guide your self-reflection.

Note: Don't worry about getting the correct answer. Treat this exercise as an opportunity to start thinking about your personal and professional needs and desires.

You can always copy your notes from this exercise to a new document and continue working on it! For now, let's start!

Why did you choose to pursue the new profession?

What questions do you currently have about the technology industry?

What skills do you want to improve or develop?

What are 2-3 personal values that interest you above all? For example, it is "helping people."

What are your best preferences for a compatible work environment (geographical location, company size, type of employment, etc.)? The list can be whatever you want!

How to start to work online?

usamnet000 — Sun, 02 Aug 2020 18:14:10 +0000

Working online
Define your career in what you want from a profession by searching online. There are many technical jobs in the sectors by selecting the next stop in your career path.
After studying specific skills to develop this job, then explore the new jobs and industries with constant curiosity

First, do a search on LinkedIn, and you can see the results when you click on the link. To search for "developer" jobs worldwide on LinkedIn. Click the link and see the search results. Change your search terms and apply filters to change region and functionality. For example, just search for "web developer jobs in Yemen" or "android developer jobs in any other country

While browsing on LinkedIn, select 2-3 sites you want to work in and find three companies that employ these sites. The information you collect should reflect the reason for your interest in the company, what makes it suitable for you, and any basic information you must know as an applicant.

With the company’s knowledge, you can search for conversations, blog posts, or articles about them. This will give you an insight into their interests, company goals and information that can be talking points when discussing or interviewing

Now, after selecting companies, you should record notes as a diary on your search for each company. You can copy and paste this information into a new document to build on later!

For example, imagine you are interested in companies in Mountain View and decide to do more research on Udacity. The notes you take should look something like this:

Primary Product(s)

Nanodegree programs
Free, single courses
Udacity Connect
Corporate training

History of the company or organization

Founded in 2011 by David Stavens, Mike Sokolsky, Sebastian Thrun
Udacity originally focused on offering university-style courses, but in 2015, Udacity started the Nanodegree program, a paid credential program which is its main product today.

Company Mission

Democratize education by offering world-class higher education opportunities that are accessible, flexible, and economical.
"Virtually anyone on the planet with an internet connection and a commitment to self-empowerment through learning can come to Udacity, master a suite of job-ready skills, and pursue rewarding employment."

Leadership

CEO: Vish Makhijani
President: Sebastian Thrun
VP, Careers: Kathleen Mullaney (I'm interested in working on career development)
Knowing the company's leadership, you can search for talks, blog posts, or articles about them. This will give you insight on their interests, goals for Udacity, and background information that can be talking points when you discuss Udacity.

Why do you want to work here?

The mission aligns with my own belief that lifelong learning is important.
I see many jobs are remote-friendly and I like that flexibility.
I'm interested in gaining experience in the online learning space.

What then?

In the future, the information you gathered in the previous exercise will be important for any cover letters you send or interviews you attend. For the time being, you should be able to get a good idea of how some companies perceive your intended business. If anything sounds too good to be true to your curiosity, write it down. You can search a bit at sites like Glassdoor, which allow employees and interviews to anonymously post information about their experiences.
Learn about the company's product

I took a snapshot of three companies. If one or more of you are more interested in you, then your next step is to better understand their products.

We cannot stress enough how important it is to understand a company's product when deciding to apply for a job. If the product is free to acquire (for example, an app you can download and test), use it for a few days and take notes about what the user experience looks like. If you cannot pay for the product, or the product is not available to you, search for reviews online and see what people have used it. Here are some things to pay attention to that will help you understand the role of the product in the current market:

How many users does it currently have? Where do you rank against its competitors?
Have any major publications (New York Times, Bloomberg Business, Wired, etc.) covered the growth or development of the product or company?
Are there any major bugs, errors or other technical defects that have been reported and not resolved?
In your opinion, what are the best characteristics of the product? What are the things that should be fixed or improved?

By knowing the latest information about a company's product, you will be able to speak more eloquently in an interview or contact a company, to showcase your in-depth knowledge of the company.
Turns can occur!

The next step in career exploration - media interviews

Professional and industrial research will also include access to new connections in order to find out more accurate information about a specific position in the company. If you do not have a specific idea of the jobs you are interested in, then media interviews are more helpful in giving you a valuable and insightful insight. Next, we'll go over how to reach people, how to conduct informational interviews, and how to get the most out of these conversations.

Request an interview

One of the most common ways to request an interview is for an initial email or a longlink. Consider the following when crafting your message:

Your accent should be non-silver; You should not assume that they are obligated to give you their time or attention
Make sure your email is brief and specific. For example, if you were hoping for a 20-minute phone call, you could say: “If you have time for a 20-minute phone call, I would be grateful for the opportunity to learn more about the company.”
Sometimes, you will not get an immediate response. The person may be on vacation or immersed in messages.
Make sure you have sufficient time (up to two weeks) to respond. If they do not respond within a week, you can send another inquiry.

If you are unable to schedule a live conversation, the contact may still be ready to answer some questions via email or link.

Ways to communicate
Now you know how to reach out to someone for an informational interview! The two most likely ways to communicate with someone are via email (you get their business card, or one of them applies to you) or via LinkedIn. Although email and LinkedIn are two different platforms, the structure of how to access and start a conversation is the same. See examples below.

Sample Email Template

Hi [Contact Name],

1st Paragraph - Self introduction including the following information:

Your career interests
Reason for reaching out
How contact’s position/background connects the first 2 pieces of information
Your connection to the person you’re writing to, if there is any

2nd Paragraph - Details on where, when, and how you would like to carry out the informational interview

Sincerely,

[Name]

[LinkedIn profile URL -- make sure to set your privacy settings to public!]

Example of a LinkedIn conversation

Sometimes, you may not be able to send an email to someone. Platforms like LinkedIn make it easy for people to find and communicate with each other. When calling someone, be sure to write a note on the request. You only have 300 characters in order to make a great first impression. Here is an example of how the conversation will flow.

An example says Roy's invitation: "Hello, you see it, my name is Roy, a computer science professor interested in switching to course development in the e-learning industry. I'd like to hear more about your exciting work at Udacity. Can you call for a quick chat? Thanks for your time! Best, Roy. "

Once your invitation is accepted, you can proceed to send your meeting request for an interview.

An example says Trin reply: “Hi Roy, thanks for contacting us and telling me a little about yourself. I am happy to chat. What do you want to know about Udacity? ”

Don't forget: people love to communicate with you! There is no reason to view your media interview request as an inconvenience to the other person. Like you, other people care about improvement professionally, which can be about sharing their experiences and their own knowledge or discussing current topics of interest in the industry.

Over time, you will become deeply familiar with conducting media interviews. You may feel stress or uncertainty at first, but with frequent practice and interactions of networks, you will soon become a network professional.

We now hope you feel equipped with the knowledge and confidence to successfully conduct a media interview from start to finish.

Good luck, God bless you!

Prepare interview questions

After you have prepared an informational interview, prepare yourself to ensure that you make the most of your learning opportunity. We recommend doing a preliminary research on both the company and the individual, drafting a sample of questions, and practicing in advance.

Here are some guidelines:

Come prepared with smart and relevant questions. If you can find * an answer to your question on a person’s LinkedIn profile, on Google Search, or on the company’s website, you can be absolutely * certain that it won’t be the best question you can ask.
Choose open questions with possible answers.
Before the interview, create a sample question list.
During the interview, take notes on the information you learn and be careful with your goals.
Ask clarifying questions as needed.

Objectives

Be goal-oriented throughout the interview. Think about some of the results you want to achieve at the end of the interview. For example, your goal might be to build your network, find more contacts, or access internal information about the company and culture.

We also recommend approaching the interview as an opportunity to get recommendations on the resources (blogs, books, etc.) related to your industry.

Finally, make your goal make a positive impression. Even if the new contact is not able to help you directly in the search for a job right now, you never know when they might think of you in a future opportunity.

Don't ask: It's natural to be curious about such a topic, especially as you will have your own expectations about the job salary you are interested in. However, this can be a particularly inappropriate question, especially the first time you meet.

The question that should be asked instead: If you want to collect information on this topic, try asking a more general question to measure the industry's expected salary range for a position. Moreover, don't forget to do some research on websites like Paysa, which provide market salary data, to see if you can find a reliable answer on your own.
Can i add you on facebook

Why not ask: You may find yourself developing a great relationship with the interviewee, but remember that you must maintain the professionalism of the stock exchange. Moreover, they may be reluctant to communicate on a social platform with a person who has contacted them to inquire about their professional experience and career advice.

What to ask instead: If you want to keep friendly but professional contact with them, politely ask if you can add them on LinkedIn (if you haven't already done so).
What other roles have you worked in?

Why not ask: This sounds like a good question, but it should actually be avoided because you should already know the answer! As part of preparing for the interview, you will definitely search for information about the work history of the person interviewed via their LinkedIn profile. Remember that you want to spend your valuable time in this interview to find answers to questions that you will not be able to find online.

How to keep the conversation going

Now that you've made a personal connection, keep going! Send a thank you message within 24 hours; Email is ok! If anything especially memorable happens during your conversation, you may want to mention it in order to reinforce that you have been actively listening and interacting.

When you send your thank-you email, send your CV so they can introduce you to others and remember who you are. Over time, keep in touch with holiday greetings, share information on topics of interest to you, and keep them informed of the progress of your job search. People love to help others and listen to their positive impact!

The most common questions about media interviews stem from entering a new industry and uncertainty about how to make new connections.

To address these concerns, remember:

People generally want help! It is an opportunity to return the favor, communicate and share the excitement and information about the job. You will likely be surprised by the willingness of people to participate.
Start building relationships as soon as possible; These are long term relationships that require some care.
Take advantage of your Udacity community. Classmates, teachers, and support personnel are part of your network.
Get out of your comfort zone and learn to be proactive in the question. The worst that can happen is that someone refuses or does not respond to your request. But there will be people who will not refuse and will respond!
Be optimistic and resourceful. Informational interviews are great educational experiences and practices for future job interviews.

deep learning to discovered a group of heart conditions through Echocardiography

usamnet000 — Sun, 02 Aug 2020 13:02:25 +0000

This project introduces echocardiography to discover a group of patients with a variety of heart conditions including lower fibrillation, coronary artery disease, cardiomyopathy, aortic valve stenosis and amyloidosis
This program outperforms human experts in diagnosing patients with serious disease
It uses the left ventricular cardiac functions which is used to diagnose cardiomyopathy through a wide range of videos 10030 Anatomical Echocardiography by EchoNet

Normal	Low Ejection Fraction	Arrhythmia

The results were conducted by three patients who had normal cardiac functions in the left column and three who had low expulsion fractures as you see in the middle column where the color blue is greater than normal and three of them in the right column suffer from arrhythmias (as you see in the photo once, it beats properly and again strongly and once Varying)

The project discovers and predicts three heart conditions:

Divide the left ventricle
Predicting a fracture of premature ejaculation
And cardiomyopathy by predictions that each beats

This project introduces the EchoNet-Dynamic Data Set, a publicly available data set from unspecified Echocardiogram videos, available at https://echonet.github.io/dynamic/.

The code is available on
https://github.com/echonet/dynamic

Dog Breed Classifier Using CNN Algorithms

usamnet000 — Sat, 04 Jul 2020 15:44:25 +0000

Over thousands of years, dogs helped humans hunt and manage livestock, guarded home and farm, and played critical roles in major wars. The contrast of talent and apparent patterns along with emotional contact between dogs and humans created more than 350 distinct breeds, each of which is A closed reproductive clan that reflects a set of specific characteristics. In this project, I build a convolutional neural network (CNN) that can classify the breed of dog from any user-supplied image. If the image is of a human and not a dog, the algorithm will provide an estimate of the dog breed that is most resembling.

Principal Objectives

Classify dog chains correctly and accurately
Provide time for the user to determine the type and variety, whether for humans or dogs
Choose the best classification algorithms to achieve the two previous goals

Your city is hosting a citywide dog show and you have volunteered to help the organizing committee with contestant registration. Every participant that registers must submit an image of their dog along with biographical information about their dog. The registration system tags the images based upon the biographical information.

Some people are planning on registering pets that aren’t actual dogs.

You need to use an already developed Python classifier to make sure the participants are dogs or human.

Using your Python skills, you will determine which image classification algorithm works the "best" on classifying images as "dogs" or "not dogs".

Determine how well the "best" classification algorithm works on correctly identifying a dog's breed.

If you are confused by the term image classifier look at it simply as a tool that has an input and an output. The Input is an image. The output determines what the image depicts. (for example: a dog). Be mindful of the fact that image classifiers do not always categorize the images correctly.

I take many algorithm takes to solve the classification problem .With computational tasks, there is often a trade-off between accuracy and runtime. The more accurate an algorithm, the higher the likelihood that it will take more time to run and use more computational resources to run.
For this image classification task you will be using an image classification application using a deep learning model called a convolutional neural network (often abbreviated as CNN). CNNs work particularly well for detecting features in images like colors, textures, and edges; then using these features to identify objects in the images. You'll use a CNN that has already learned the features from a giant dataset of 1.2 million images called ImageNet. There are different types of CNNs that have different structures (architectures) that work better or worse depending on your criteria. With this project you'll explore the three different architectures (VGG19, Resnet50, InceptionV3, or Xception) and determine which is best for your application.

Certain breeds of dog look very similar. The more images of two similar looking dog breeds that the algorithm has learned from, the more likely the algorithm will be able to distinguish between those two breeds. We have found the following breeds to look very similar: Great Pyrenees and Kuvasz, German Shepherd and Malinois, Beagle and Walker Hound, amongst others.

The rare and widespread origins of the breed of dogs

What is CNN (Convolutional Neural Networks) ?

Convolutional neural networks are deep artificial neural networks that are used primarily to classify images, cluster them by similarity (photo search), and perform object recognition within scenes. They are algorithms that can identify faces, individuals, street signs, tumors, platypuses and many other aspects of visual data.
Convolutional networks perform optical character recognition (OCR) to digitize text and make natural-language processing possible on analog and hand-written documents, where the images are symbols to be transcribed. CNNs can also be applied to sound when it is represented visually as a spectrogram. More recently, convolutional networks have been applied directly to text analytics as well as graph data with graph convolutional networks.

The Convolution operation

The model learns by applying the small convonet or window by sliding over the full image and tries to learn the specific patterns as per our filter matches. With each convolutional layer, our model first learns small details of the image such as lines, curves, object edges etc. And as it traverse deeper into layers, model learns for more complex figures and parts of images.

How the layers work?

A convolutional neural network consists of an input and an output layer, as well as multiple hidden layers. The hidden layers of a CNN typically consist of convolutional layers, RELU layer i.e. activation function, pooling layers, fully connected layers and normalization layers.

Convolutional: Convolutional layers consist of a rectangular grid of neurons. It requires that the previous layer also be a rectangular grid of neurons. Each neuron takes inputs from a rectangular section of the previous layer; the weights for this rectangular section are the same for each neuron in the convolutional layer
Max-Pooling: After each convolutional layer, there may be a pooling layer. The pooling layer takes small rectangular blocks from the convolutional layer and subsamples it to produce a single output from that block. There are several ways to do this pooling, such as taking the average or the maximum, or a learned linear combination of the neurons in the block.
Fully-Connected: Finally, after several convolutional and max pooling layers, the high-level reasoning in the neural network is done via fully connected layers. A fully connected layer takes all neurons in the previous layer (be it fully connected, pooling, or convolutional) and connects it to every single neuron it has.

Building the CNN algorithm’s architecture

In notebook, you will make the first steps towards developing an algorithm that could be used as part of a mobile or web app. At the end of this project, your code will accept any user-supplied image as input. If a dog is detected in the image, it will provide an estimate of the dog's breed. If a human is detected, it will provide an estimate of the dog breed that is most resembling. The image below displays potential sample output of your finished project (... but we expect that each student's algorithm will behave differently!).

In this real-world setting, you will need to piece together a series of models to perform different tasks; for instance, the algorithm that detects humans in an image will be different from the CNN that infers dog breed. There are many points of possible failure, and no perfect algorithm exists. Your imperfect solution will nonetheless create a fun user experience!
The Road Ahead
We break the notebook into separate steps. Feel free to use the links below to navigate the notebook.

Step 0: Import Datasets
Step 1: Detect Humans
Step 2: Detect Dogs
Step 3: Create a CNN to Classify Dog Breeds (from Scratch)
Step 4: Use a CNN to Classify Dog Breeds (using Transfer Learning)
Step 5: Create a CNN to Classify Dog Breeds (using Transfer Learning)
Step 6: Write your Algorithm
Step 7: Test Your Algorithm

You can return to the code via the my github

Conclusion

Furthermore, for increasing the accuracy of our model- we can use Data Augmentation during the train data. As in the images, the dog image can be vertically or horizontally positioned anywhere in the frame. Also the image can be at an angle with partial visibility. So, augmentation can also add to increase our model’s accuracy.

Reasons for success and failure in the movie industry

usamnet000 — Sat, 20 Jun 2020 02:38:03 +0000

Modern filmmaking, which is worth nearly 10 billion dolar a year, is a noisy business and highly profitable There was an important theoretical relationship between the number of revenues and the amount of money the film studio spent in producing the film

The variable was recorded as analyzes indicate a large variation in movie revenue, with approximately 80% -85% of total movie revenue coming from the best 20% of movies. The film that is a supplement or belongs to a well-established property will have an impact on competition in the release year. As the release year affects films evaluation

Introduction

Success in any film production business requires great potential, especially in light of competition from major companies with long experience.Choosing the content for the audience’s desire remains the first of the basics, as diversity in films, whether Funny, social, historical, etc. has its own audience.

Perhaps the success of the drama is due to many factors, including excitement, photography and the content of the story, in addition to employing the talents required. Therefore, we see films at the top that generate revenues and films at the bottom that do not achieve anything. In this study, we analyze data on the revenues of films most interested and compare production success in the film industry.

According to industry statistics, six or seven out of ten films are unprofitable, which makes business risky at best? Given this inherent danger, how do movie studios decide which films to place their bets on? Are there common factors, such as the duration of the show, gender, staffing, social style of the audience, or production budget, that explain the financial success of a movie in relation to another? And based on this is determined the desire of the public? Are there common factors, such as revenue (views), voting, gender, or year? This question forms the basis of this research project. This question forms the basis of this research project. To answer that

Data Analytics of movie industry

Most people can understand the visualizations, as 40% of the people can answer basic questions about the information provided on the record visualizations. Therefore, when providing information in the form of linear charts, people show a good understanding of the plotsand provide accurate forecasts in this project.

Our study group that contains 10,866 movies that were released worldwide in the years (1966-2015) Given the large number of data samples from movie releases and in order to determine the variables that determine the success of the most popular movies, we chose the dataset for years instead of movies every year where five years of forty-nine years (2011-2015) were chosen to represent modern films and five years of forty-nine years (2005-2010) were chosen to represent traditional movies and were combined into one large unorganized dataset. This method proved an effective way to answer the research question as it focused on the most profitable films and tried to explain their success, rather than finding similarities between random films that are too small and too big, something that might happen if films were chosen each year randomly and variable data was obtained.

Part I: Comparing modern and traditional movies in the last ten years?

Through the results it was found that traditional films achieved higher revenues than modern films as a result of the following: Through an average study where the best movies achieved the highest revenue is 2.781506e+09 while the lowest real revenue is 3.000000e+00 and on that the data set of the traditional films was chosen to know the factors That contributed to success

Part II: How do movie studios decide which films to place their bets on?

Are there common factors, such as the duration of the show, gender, staffing, social style of the audience, or production budget, that explain the financial success of a movie in relation to another.

factor budget relating revenue

The big companies that have big capital for making films make big revenues, while the companies that don't have a big budget make small revenues.

factor runtime relating revenue

Among the success factors for the industry, films are the show duration, that is, the longer the show, the more revenue, and the less the offer, the less revenue.

factor vote count relating revenue

Likewise, the voting component increases the more votes, the more revenue, and the lower the percentage of voting, the less revenue.

Part III: What is the best so I have not achieved revenue in all years?

the best so I have not achieved revenue in all years is 2781505847$

The best movies that have earned revenue for each year

release_year    revenue

1960    60000000

1961    215880014

1962    70000000

1963    78898765

1964    124900000

1965    163214286

1966    33736689

1967    205843612

1968    56715371

1969    102308889

1970    136400000

1971    116000000

1972    245066411

1973    441306145

1974    119500000

1975    470654000

1976    161000000

1977    775398007

1978    300218018

1979    210300000

1980    538400000

1981    389925971

1982    792910554

1983    572700000

1984    333000000

1985    381109762

1986    356830601

1987    320145693

1988    354825435

1989    474171806

1990    505000000

1991    520000000

1992    504050219

1993    920100000

1994    788241776

1995    1106279658

1996    816969268

1997    1845034188

1998    553799566

1999    924317558

2000    546388105

2001    976475550

2002    926287400

2003    1118888979

2004    919838758

2005    895921036

2006    1065659812

2007    961000000

2008    1001921825

2009    2781505847

2010    1063171911

2011    1327817822

2012    1519557910

2013    1274219009

2014    955119788

2015    2068178225

Conclusions

By identifying the big companies that have modern equipment and have big capital for the film industry, they achieve great revenues, while the average companies achieve small revenues and accordingly we can find out the reasons for the success of these companies or the failure of other companies.

In the event that success is achieved for a previous movie in the series, the company will strive to produce successful and profitable films in the coming days, because success will be followed by other successes and whoever succeeds in one of the works does not accept failure in other works, and all of this will result in increasing the different audiences. There is no specific work for success, as we found in the analysis that it is difficult to divide success according to the type of film in the study: action (ACTION), science fiction (SCIFI), comedy (COM), documentary (DOC), foreign (FOREIGN), romance (ROM), adventure (ADVENT) and horror (HORROR). Therefore, it is difficult to evaluate the database according to gender, and there are other factors that affect the popularity of films, such as music, photography, award nominations, and the strength of stars, which were important positive determinants of success.

Voting clearly plays an important role in determining movie revenues, as some votes can say something about the nature of the movie and can restrict the film market.

Another variable whose importance was questioned in the analysis but worthy of inclusion was a measure of the strength of the director and actor associated with a film project. It indicates that the analysis believes that the strength of the director and the star is important, which supports the assumption of rent picking that the actor has a market value through large salaries and does little to influence the profitability of films. And successful films may make the stars. Due to the ambiguity of the effect of this variable and the inconsistency of our qualifications

Most of the time, we found that the strength of the directors, production budgets and sequences contributed positively to the film's revenue.

Special effects and computer technology have come a long way in the past ten years, and may have contributed to changing consumers' tastes and preferences for certain types of movies.

Better quality films will be more successful.

If a movie is released in the holiday season, it is expected to see an increase in revenue, while the summer release will bring an expected increase in views.

Comedies tend to experience positive success in the supply market, although the influence of other genres is inconclusive.

test

usamnet000 — Sat, 20 Jun 2020 01:37:36 +0000

this is test