Maddie and Greg go live with computers!

This Saturday morning, our friends Maddie Moate and Greg Foot will be live at The Centre for Computing History for a computing- and retro gaming-inspired episode of their show Let’s Go Live, and you can tune in from 10am to join the fun.

Retro gaming and computer funtimes

Saturday’s show will be a retro feast of vintage video games, and will answer questions such as ‘What is a computer?’ and ‘How do computers work?’. As always, Maddie and Greg have a number of activities planned, including designing pixel art and going on a tech safari! They’re also extremely excited to step inside a giant computer and try to put it back together!

#uncategorized #maddie moate #greg foot #lets go live #go

What is GEEK

Buddha Community

Maddie and Greg go live with computers!

This Saturday morning, our friends Maddie Moate and Greg Foot will be live at The Centre for Computing History for a computing- and retro gaming-inspired episode of their show Let’s Go Live, and you can tune in from 10am to join the fun.

Retro gaming and computer funtimes

Saturday’s show will be a retro feast of vintage video games, and will answer questions such as ‘What is a computer?’ and ‘How do computers work?’. As always, Maddie and Greg have a number of activities planned, including designing pixel art and going on a tech safari! They’re also extremely excited to step inside a giant computer and try to put it back together!

#uncategorized #maddie moate #greg foot #lets go live #go

What's new in the go 1.15

Go announced Go 1.15 version on 11 Aug 2020. Highlighted updates and features include Substantial improvements to the Go linker, Improved allocation for small objects at high core counts, X.509 CommonName deprecation, GOPROXY supports skipping proxies that return errors, New embedded tzdata package, Several Core Library improvements and more.

As Go promise for maintaining backward compatibility. After upgrading to the latest Go 1.15 version, almost all existing Golang applications or programs continue to compile and run as older Golang version.

#go #golang #go 1.15 #go features #go improvement #go package #go new features

Top Chat Software for Live Streaming & Broadcasting Web & Mobile Apps

Do you Increase your Website Engagment?

I analysed, ranked and reviewed best live video streaming chat APIs and SDKs for your web & mobile app based on client reviews and ratings. portfolio, usecases, cost, secure streaming, live chat features, cost, support, etc.

Turn your viewers into participatients with Live Streaming Chat Solutions. There are lot of Real-time chat apis & SDks Providers have in online market now. You can easily integrte and customize real time chat solutions into your new or existing live video streaming web and iOS & android applications. Below have mentioned best real time chat api & SDk Proivders.

Here are The Most Popular Live Video Streaming Chat APIs & SDKs to be Considered for your Mobile App

1. CONTUS Fly - Real-time Messaging Platform for Live Streaming Apps & Webs

CONTUS Fly is one of the leading real time messaging software providers in the market for a decade. Their messaging platforms are completely customizable since they provide Chat APIs and SDKs to integrate real time chat feasibility on your live streaming applications irrespective of audience base. Engage your audience like a live concert, stadium like experience through digitally. Create channels for every live streaming event, sports or anything that would create buzz. Enable audience to interact with each other over voice, video chats and real-time text chats with engaging emojis. CONTUS Fly enables users to add emojis and stickers to captivate each audience and create fun.

Highlight Features of CONTUS Fly Live Video Streaming Platform Includes:

1. Chat for Live Video Streaming
2. Video & Audio Recording
3. Video Calling
4. Drawing whitebord
5. Screen Sharing
6. End to End Encryption

2. Apphitect -Instant chat for Live Streaming Platforms

To make every live streaming and broadcasting videos more engaging and entertaining, Apphitect’s instant messaging comes with exciting Instant messaging chat APIs to add chat into streaming applications. Apphitect is built with multiple real time communication features like video chat, voice chat and real-time chat to your streaming apps. Their solution surprisingly has a wide range of features to communicate, engage and increase subscription benefits.

Highlight Features of Apphitect Live Insterative Broadcasting Software Includes:

1. Live Video Streaming Chat
2. Cross Platform Support
3. Audio & Video Recording
4. Live Video Calling
5. Emoji & Stickers

3. MirrorFly - Enterprise Real Time Chat for Streaming Websites

One of the enterprise-grade real-time chat solutions built to create virtual chat experience for live streaming events and websites for big brands and startups. Irrespective of audience base, category, MirrorFly provides customizable real time chat APIs to add virtual communication mediums on live streaming and broadcasting applications. Their solution comes with absolute moderation tools and open channels to talk and listen with your audience. MirrorFly’s server infrastructure has the potential to handle concurrent messages and users and to achieve maximum sales conversion.

Highlight Features of MirrorFly Live Streaming Chat API Includes:

1. Face to Face Video Calling
2. Live Interactive Broadcasting
3. Call Recording
4. Digital Whiteboard
5. Group Video Calling

4. Applozic - Real-time Chat Plugin for Live Broadcasting & Video Streaming apps

When it comes to building a live streaming chat app software that covers the entire platforms and demand All-in-One package (features, Customization to any extent) with a one-time payment for lifetime performance, then undoubtedly Contus Fly makes the right choice to partner with. The company offers live broadcasting SDK for Android/iOS and chat APIs for customization.

Highlight Features of Applozic Chat Live Streaming Platform Includes:

1. Real time Communication
2. Cross Platform Support
3. Live Audio Broadcasting
4. Push Notifications
5. Secure Image Sharing

5. Sendbird - Top Real time Chat for Live Video Streams

Being a leading real time chat platform provider in the market, Sendbird has its own hallmark of communication features to the world’s most prominent live streaming applications. Their real time chat solution enables broadcasting and streaming platform’ owners to create a physical equivalent digital chat experience for the audience during any live event streaming to interact, collaborate and cheer together within the same streaming screen. By creating open channels and groups, you can enable the audience to interact with each other during any streaming, engage them with polls, stickers, multiple communication channels and more.

Highlight Features of Sendbird Live Streaming Chat API Includes:

1. Chat for Streaming website
2. Messaging Data
3. Multi Platforms
4. Push Notifications
5. End to End Encryption

6. Agora - Interactive Live Chat for Live Video Streaming

Agora, a deep integratable API available in the market to deliver live interactive streaming experience for workplace, enterprises, gaming, retail, telehealth and social live streaming websites. With easy-to-embed SDKs, Agora empowers businesses to add HD and low latency video and voice chat features into any streaming platforms and channels. Their easy-to-embed real time chat features encourage higher levels of user engagement and opportunity to drive more audience.

7. Enablex - A Redefined Communication APIs for In-app Chat

Their smart and secure chat APIs deliver real-time chat feasibility for live and on-demand video streaming websites. The real time chat features provides users to communicate and engage within the same streaming platform irrespective of interaction medium and audience count. Enablex offers platform-as-a-service communication solutions for real time messaging integration with APIs hosting possibility on public, private and cloud deployment. Their APIs are enriched with multiple communication features and engagement tools like live-polls, stickers and more.

8. Pubnub - In-app Chat Platforms for Live Event Streaming Websites

In order to increase user engagement with live and remote audiences, Pubnub offers real time messaging chat functionality with interactive features to drive event-based engagement with mass chat. Their in-app chat feature enhances live programs, event streaming and blogging content with live polling, multiple chats and more. It also enables live streaming websites to build community, channels and super groups during live streaming to bring the entire audience base to one place.

9. Vonage - Communication APIs for In-app Messagings

Vonage is a prime provider of communication APIs for major industrial sectors and enterprise workplaces. With its API, businesses such as live streaming applications can integrate in-app messaging features into any streaming platforms on Android, iOS and Web to empower user engagement. Their APIs are powered with scalable infrastructure and provide multiple communication mediums such as in-app voice, video and chat proactively engaging the audience.

10. Firekast - Live Chat Widget for Video Streaming Player

Firekast provides a customizable live chat widget with HTML code for streaming players to enable chat within any streaming or on-demand videos. The chat widget gives the ability for brands and content owners to make the audience to interact with each other for better engagement and proactivity during streaming. The Firekast Live chat comes with moderator tools that will allow administrators to delete or ban abusive content and users from the channel or groups. Firekast’s live chat comes with a private chat widget to create public or private chat rooms to make effective collaboration and discussions.
 

Conclusion
And this is all the real time chat providers in the market to implement chat functionality in any live streaming or broadcasting platforms. More than delivering entertaining live content, creating a massive engagement and buzz for every live event is the smarter way to turn every audience into a protiable subscriber. Picking up the right software provider is more important than just handling the integration process.

#live #live-streaming-solutions #live-streaming-chat-api #live-streaming-chat-sdk #chat-api-for-live-broadcasting

How to Predict Housing Prices with Linear Regression?

The final objective is to estimate the cost of a certain house in a Boston suburb. In 1970, the Boston Standard Metropolitan Statistical Area provided the information. To examine and modify the data, we will use several techniques such as data pre-processing and feature engineering. After that, we'll apply a statistical model like regression model to anticipate and monitor the real estate market.

Project Outline:

• EDA
• Feature Engineering
• Pick and Train a Model
• Interpret
• Conclusion

EDA

Before using a statistical model, the EDA is a good step to go through in order to:

• Recognize the data set
• Check to see if any information is missing.
• Find some outliers.
• To get more out of the data, add, alter, or eliminate some features.

Importing the Libraries

• Recognize the data set
• Check to see if any information is missing.
• Find some outliers.
• To get more out of the data, add, alter, or eliminate some features.

# Import the libraries #Dataframe/Numerical libraries import pandas as pd import numpy as np #Data visualization import plotly.express as px import matplotlib import matplotlib.pyplot as plt import seaborn as sns #Machine learning model from sklearn.linear_model import LinearRegression

Reading the Dataset with Pandas

#Reading the data path='./housing.csv' housing_df=pd.read_csv(path,header=None,delim_whitespace=True)

	CRIM	ZN	INDUS	CHAS	NOX	RM	AGE	DIS	RAD	TAX	PTRATIO	B	LSTAT	MEDV
0	0.00632	18.0	2.31	0	0.538	6.575	65.2	4.0900	1	296.0	15.3	396.90	4.98	24.0
1	0.02731	0.0	7.07	0	0.469	6.421	78.9	4.9671	2	242.0	17.8	396.90	9.14	21.6
2	0.02729	0.0	7.07	0	0.469	7.185	61.1	4.9671	2	242.0	17.8	392.83	4.03	34.7
3	0.03237	0.0	2.18	0	0.458	6.998	45.8	6.0622	3	222.0	18.7	394.63	2.94	33.4
4	0.06905	0.0	2.18	0	0.458	7.147	54.2	6.0622	3	222.0	18.7	396.90	5.33	36.2
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
501	0.06263	0.0	11.93	0	0.573	6.593	69.1	2.4786	1	273.0	21.0	391.99	9.67	22.4
502	0.04527	0.0	11.93	0	0.573	6.120	76.7	2.2875	1	273.0	21.0	396.90	9.08	20.6
503	0.06076	0.0	11.93	0	0.573	6.976	91.0	2.1675	1	273.0	21.0	396.90	5.64	23.9
504	0.10959	0.0	11.93	0	0.573	6.794	89.3	2.3889	1	273.0	21.0	393.45	6.48	22.0
505	0.04741	0.0	11.93	0	0.573	6.030	80.8	2.5050	1	273.0	21.0	396.90	7.88	11.9

Have a Look at the Columns

Crime: It refers to a town's per capita crime rate.

ZN: It is the percentage of residential land allocated for 25,000 square feet.

Indus: The amount of non-retail business lands per town is referred to as the indus.

CHAS: CHAS denotes whether or not the land is surrounded by a river.

NOX: The NOX stands for nitric oxide content (part per 10m)

RM: The average number of rooms per home is referred to as RM.

AGE: The percentage of owner-occupied housing built before 1940 is referred to as AGE.

DIS: Weighted distance to five Boston employment centers are referred to as dis.

RAD: Accessibility to radial highways index

TAX: The TAX columns denote the rate of full-value property taxes per $10,000 dollars.

B: B=1000(Bk — 0.63)2 is the outcome of the equation, where Bk is the proportion of blacks in each town.

PTRATIO: It refers to the student-to-teacher ratio in each community.

LSTAT: It refers to the population's lower socioeconomic status.

MEDV: It refers to the 1000-dollar median value of owner-occupied residences.

Data Preprocessing

# Check if there is any missing values. housing_df.isna().sum() CRIM       0 ZN         0 INDUS      0 CHAS       0 NOX        0 RM         0 AGE        0 DIS        0 RAD        0 TAX        0 PTRATIO    0 B          0 LSTAT      0 MEDV       0 dtype: int64

No missing values are found

We examine our data's mean, standard deviation, and percentiles.

housing_df.describe()

	CRIM	ZN	INDUS	CHAS	NOX	RM	AGE	DIS	RAD	TAX	PTRATIO	B	LSTAT	MEDV
count	506.000000	506.000000	506.000000	506.000000	506.000000	506.000000	506.000000	506.000000	506.000000	506.000000	506.000000	506.000000	506.000000	506.000000
mean	3.613524	11.363636	11.136779	0.069170	0.554695	6.284634	68.574901	3.795043	9.549407	408.237154	18.455534	356.674032	12.653063	22.532806
std	8.601545	23.322453	6.860353	0.253994	0.115878	0.702617	28.148861	2.105710	8.707259	168.537116	2.164946	91.294864	7.141062	9.197104
min	0.006320	0.000000	0.460000	0.000000	0.385000	3.561000	2.900000	1.129600	1.000000	187.000000	12.600000	0.320000	1.730000	5.000000
25%	0.082045	0.000000	5.190000	0.000000	0.449000	5.885500	45.025000	2.100175	4.000000	279.000000	17.400000	375.377500	6.950000	17.025000
50%	0.256510	0.000000	9.690000	0.000000	0.538000	6.208500	77.500000	3.207450	5.000000	330.000000	19.050000	391.440000	11.360000	21.200000
75%	3.677083	12.500000	18.100000	0.000000	0.624000	6.623500	94.075000	5.188425	24.000000	666.000000	20.200000	396.225000	16.955000	25.000000
max	88.976200	100.000000	27.740000	1.000000	0.871000	8.780000	100.000000	12.126500	24.000000	711.000000	22.000000	396.900000	37.970000	50.000000

The crime, area, sector, nitric oxides, 'B' appear to have multiple outliers at first look because the minimum and maximum values are so far apart. In the Age columns, the mean and the Q2(50 percentile) do not match.

We might double-check it by examining the distribution of each column.

Inferences

1. The rate of crime is rather low. The majority of values are in the range of 0 to 25. With a huge value and a value of zero.
2. The majority of residential land is zoned for less than 25,000 square feet. Land zones larger than 25,000 square feet represent a small portion of the dataset.
3. The percentage of non-retial commercial acres is mostly split between two ranges: 0-13 and 13-23.
4. The majority of the properties are bordered by the river, although a tiny portion of the data is not.
5. The content of nitrite dioxide has been trending lower from.3 to.7, with a little bump towards.8. It is permissible to leave a value in the range of 0.1–1.
6. The number of rooms tends to cluster around the average.
7. With time, the proportion of owner-occupied units rises.
8. As the number of weights grows, the weight distance between 5 employment centers reduces. It could indicate that individuals choose to live in new high-employment areas.
9. People choose to live in places with limited access to roadways (0-10). We have a 30th percentile outlier.
10. The majority of dwelling taxes are in the range of $200-450, with large outliers around $700,000.
11. The percentage of people with lower status tends to cluster around the median. The majority of persons are of lower social standing.

Because the model is overly generic, removing all outliers will underfit it. Keeping all outliers causes the model to overfit and become excessively accurate. The data's noise will be learned.

The approach is to establish a happy medium that prevents the model from becoming overly precise. When faced with a new set of data, however, they generalise well.

We'll keep numbers below 600 because there's a huge anomaly in the TAX column around 600.

new_df=housing_df[housing_df['TAX']<600]

Looking at the Distribution

The overall distribution, particularly the TAX, PTRATIO, and RAD, has improved slightly.

Correlation

Perfect correlation is denoted by the clear values. The medium correlation between the columns is represented by the reds, while the negative correlation is represented by the black.

With a value of 0.89, we can see that 'MEDV', which is the medium price we wish to anticipate, is substantially connected with the number of rooms 'RM'. The proportion of black people in area 'B' with a value of 0.19 is followed by the residential land 'ZN' with a value of 0.32 and the percentage of black people in area 'ZN' with a value of 0.32.

The metrics that are most connected with price will be plotted.

Feature Engineering

Feature Scaling

Gradient descent is aided by feature scaling, which ensures that all features are on the same scale. It makes locating the local optimum much easier.

Mean standardization is one strategy to employ. It substitutes (target-mean) for the target to ensure that the feature has a mean of nearly zero.

def standard(X):    '''Standard makes the feature 'X' have a zero mean'''    mu=np.mean(X) #mean    std=np.std(X) #standard deviation    sta=(X-mu)/std # mean normalization    return mu,std,sta     mu,std,sta=standard(X) X=sta X

	CRIM	ZN	INDUS	CHAS	NOX	RM	AGE	DIS	RAD	TAX	PTRATIO	B	LSTAT
0	-0.609129	0.092792	-1.019125	-0.280976	0.258670	0.279135	0.162095	-0.167660	-2.105767	-0.235130	-1.136863	0.401318	-0.933659
1	-0.575698	-0.598153	-0.225291	-0.280976	-0.423795	0.049252	0.648266	0.250975	-1.496334	-1.032339	-0.004175	0.401318	-0.219350
2	-0.575730	-0.598153	-0.225291	-0.280976	-0.423795	1.189708	0.016599	0.250975	-1.496334	-1.032339	-0.004175	0.298315	-1.096782
3	-0.567639	-0.598153	-1.040806	-0.280976	-0.532594	0.910565	-0.526350	0.773661	-0.886900	-1.327601	0.403593	0.343869	-1.283945
4	-0.509220	-0.598153	-1.040806	-0.280976	-0.532594	1.132984	-0.228261	0.773661	-0.886900	-1.327601	0.403593	0.401318	-0.873561
...	...	...	...	...	...	...	...	...	...	...	...	...	...
501	-0.519445	-0.598153	0.585220	-0.280976	0.604848	0.306004	0.300494	-0.936773	-2.105767	-0.574682	1.445666	0.277056	-0.128344
502	-0.547094	-0.598153	0.585220	-0.280976	0.604848	-0.400063	0.570195	-1.027984	-2.105767	-0.574682	1.445666	0.401318	-0.229652
503	-0.522423	-0.598153	0.585220	-0.280976	0.604848	0.877725	1.077657	-1.085260	-2.105767	-0.574682	1.445666	0.401318	-0.820331
504	-0.444652	-0.598153	0.585220	-0.280976	0.604848	0.606046	1.017329	-0.979587	-2.105767	-0.574682	1.445666	0.314006	-0.676095
505	-0.543685	-0.598153	0.585220	-0.280976	0.604848	-0.534410	0.715691	-0.924173	-2.105767	-0.574682	1.445666	0.401318	-0.435703

Choose and Train the Model

For the sake of the project, we'll apply linear regression.

Typically, we run numerous models and select the best one based on a particular criterion.

Linear regression is a sort of supervised learning model in which the response is continuous, as it relates to machine learning.

Form of Linear Regression

y= θX+θ1 or y= θ1+X1θ2 +X2θ3 + X3θ4

y is the target you will be predicting

0 is the coefficient

x is the input

We will Sklearn to develop and train the model

#Import the libraries to train the model from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression

Allow us to utilise the train/test method to learn a part of the data on one set and predict using another set using the train/test approach.

X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.4) #Create and Train the model model=LinearRegression().fit(X_train,y_train) #Generate prediction predictions_test=model.predict(X_test) #Compute loss to evaluate the model coefficient= model.coef_ intercept=model.intercept_ print(coefficient,intercept) [7.22218258] 24.66379606613584

In this example, you will learn the model using below hypothesis:

Price= 24.85 + 7.18* Room

It is interpreted as:

For a decided price of a house:

A 7.18-unit increase in the price is connected with a growth in the number of rooms.

As a side note, this is an association, not a cause!

Interpretation

You will need a metric to determine whether our hypothesis was right. The RMSE approach will be used.

Root Means Square Error (RMSE) is defined as the square root of the mean of square error. The difference between the true and anticipated numbers called the error. It's popular because it can be expressed in y-units, which is the median price of a home in our scenario.

def rmse(predict,actual):    return np.sqrt(np.mean(np.square(predict - actual))) # Split the Data into train and test set X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.4) #Create and Train the model model=LinearRegression().fit(X_train,y_train) #Generate prediction predictions_test=model.predict(X_test) #Compute loss to evaluate the model coefficient= model.coef_ intercept=model.intercept_ print(coefficient,intercept) loss=rmse(predictions_test,y_test) print('loss: ',loss) print(model.score(X_test,y_test)) #accuracy [7.43327725] 24.912055881970886 loss: 3.9673165450580714 0.7552661033654667 Loss will be 3.96

This means that y-units refer to the median value of occupied homes with 1000 dollars.

This will be less by 3960 dollars.

While learning the model you will have a high variance when you divide the data. Coefficient and intercept will vary. It's because when we utilized the train/test approach, we choose a set of data at random to place in either the train or test set. As a result, our theory will change each time the dataset is divided.

This problem can be solved using a technique called cross-validation.

Improvisation in the Model

With 'Forward Selection,' we'll iterate through each parameter to assist us choose the numbers characteristics to include in our model.

Forward Selection

1. Choose the most appropriate variable (in our case based on high correlation)
2. Add the next best variable to the model
3. Some predetermined conditions must meet.

We'll use a random state of 1 so that each iteration yields the same outcome.

cols=[] los=[] los_train=[] scor=[] i=0 while i < len(high_corr_var):    cols.append(high_corr_var[i])        # Select inputs variables    X=new_df[cols]        #mean normalization    mu,std,sta=standard(X)    X=sta        # Split the data into training and testing    X_train,X_test,y_train,y_test= train_test_split(X,y,random_state=1)        #fit the model to the training    lnreg=LinearRegression().fit(X_train,y_train)        #make prediction on the training test    prediction_train=lnreg.predict(X_train)        #make prediction on the testing test    prediction=lnreg.predict(X_test)        #compute the loss on train test    loss=rmse(prediction,y_test)    loss_train=rmse(prediction_train,y_train)    los_train.append(loss_train)    los.append(loss)        #compute the score    score=lnreg.score(X_test,y_test)    scor.append(score)        i+=1

We have a big 'loss' with a smaller collection of variables, yet our system will overgeneralize in this scenario. Although we have a reduced 'loss,' we have a large number of variables. However, if the model grows too precise, it may not generalize well to new data.

In order for our model to generalize well with another set of data, we might use 6 or 7 features. The characteristic chosen is descending based on how strong the price correlation is.

high_corr_var ['RM', 'ZN', 'B', 'CHAS', 'RAD', 'DIS', 'CRIM', 'NOX', 'AGE', 'TAX', 'INDUS', 'PTRATIO', 'LSTAT']

With 'RM' having a high price correlation and LSTAT having a negative price correlation.

# Create a list of features names feature_cols=['RM','ZN','B','CHAS','RAD','CRIM','DIS','NOX'] #Select inputs variables X=new_df[feature_cols] # Split the data into training and testing sets X_train,X_test,y_train,y_test= train_test_split(X,y, random_state=1) # feature engineering mu,std,sta=standard(X) X=sta # fit the model to the trainning data lnreg=LinearRegression().fit(X_train,y_train) # make prediction on the testing test prediction=lnreg.predict(X_test) # compute the loss loss=rmse(prediction,y_test) print('loss: ',loss) lnreg.score(X_test,y_test) loss: 3.212659865936143 0.8582338376696363

The test set yielded a loss of 3.21 and an accuracy of 85%.

Other factors, such as alpha, the learning rate at which our model learns, could still be tweaked to improve our model. Alternatively, return to the preprocessing section and working to increase the parameter distribution.

For more details regarding scraping real estate data you can contact Scraping Intelligence today

https://www.websitescraper.com/how-to-predict-housing-prices-with-linear-regression.php

7 Scripts To Develop Live Video Streaming Apps - Streambiz

7 Scripts That Will Help You Build A Live Video Streaming Application. Live Video Streaming App can Be for Live Events or Individuals to Use it for Fun and also Earn Money Online.

StreamBiz is one of the most exclusive offers for you who are looking for the best live stream video app builder. Are you looking for a video host app for your company, educator Institute, or events? Download Now. Click here! StreamBiz is a unique app presented by BSETEC to fulfill your requirement for any time of video streaming creative business plan online. Maybe you are the best marketer in your company who has got the opportunity to present your brand more lively on social media apps. However, if you want to excel now in these times with a personal video app for your business idea, then you should start without any worry. Take the free live streaming script app called StreamBiz available on Google playstore or the Apple store. Get the latest features and technology support for free! 

Now Here are the 7 Best Scripts for Live Video Streaming Apps 

1. StreamBiz Live Video Script

Build a Bigo live clone or a periscope clone, all you need is Streambiz free video app script to allow the creator to build an exclusive unique video app that can be used for a particular crowd or can be used by anyone to present online. The script is available on Google Play or Apple Store easily without any technology or sign-up interaction. Use this in media, technology, education, sports, corporate, or the government. The app itself is a proud technology provided by one of the excellent and leading technology companies BSETEC. 

2. Zoom Clone Script

Live video streaming script apps have become more important than any other application, Zoom app is the most suitable for all just like StreamBiz high technology suite for video app development. Zoom video app, mostly used for live conferences, the scream script is now available on our website. It has some amazing features that you can apply while creating a video stream app for your company or business. The feature includes a user can start a video session on zoom and share with others, also anyone can have a personal chat room, group chat, recording of the meeting, and set an extra miles sample for others in the video app script clone business. 

3. Periscope Clone Script Live Streaming App

Periscope is a broadcast live video sharing platform for the targeted audience who are logged in already and for everyone who wants to join and watch hosted videos on periscope. Also, it allows streaming a live moment that can be shared with anyone on any social media app. Periscope is a good app for speakers and educators, business professionals who cover a huge audience at one moment can easily use this, and similarly if you want to clone this app, we have a free live streaming script for video making apps. 

Benefits of PERISCOPE CLONE SCRIPT FOR ALL INDUSTRIES – BSETEC
 

4. Live TV Stream Script

Interestingly this app script is famous and empowers the user to create a live stream video app. It Is compatible with any desktop and mobile app. The user can easily watch the stream and share it with others on social media. One can have a full panel control to edit, filter, and do lots more. The script can be cloned easily with a live stream script. It has transaction options for all the viewers. And this helps the businessman to earn money through the paid version of the video stream for various events. 

5. Castasy Video Streaming App 

The App is famous and can be used for multiple purposes, we can clone this type of template, and yes. It is very easy and convenient. Anyone can use cactasy so bring a huge new set of video streaming apps in the market with the best features that can be very unique and different for everyone. To clone this app contact our experts now BSETEC

6. Gentle Ninja Meerkat Turnkey App Script

This app script is making a special noise. Yet very interesting, it can be cloned with our free open source code online. The app has features that allow the user to log in via social media, live re-streaming, scheduling, follow up, get the entire management admin panel, and much more. One can just start using our free live streaming script and build the most exclusive video app in less time. 

How Live Streaming has Helped Small Businesses to Grow?

7. Bigo Live App 

The Bigo live app can be cloned easily with the help of a Streambiz, a very useful and smart option that allows the end-user to log in, like, follow and share. Play interesting games and get the best options to earn money online. This is way different than other applications like zoom or periscope. Contact our experts for more details. 

Live Video Streaming App can Be for Live Events or Individuals to Use it for Fun and also Earn Money Online.

Conclusion:

Ask us all about live video streaming app cloning, free advice with 100% technical support, free download at https://www.bsetec.com/periscope-clone/ 

 

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