BigData Assignment Help
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Big Data Assignment Help
‘Big Data’ is a term that defines the huge volume of data that is growing exponentially. In machine Learning BigData analytics help to manage big data, sometimes data is huge number of records then big data use to analyze the data, the speed to big data analytics techniques is more than normal techniques
Big Data Assignment Help | Big Data Homework Help
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Our cohesive team of Big Data assignment experts consists of:
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Big Data Assignment Help
Our Machine Learning Expert Provide Big Data Assignment help & Big Data homework help. Our expert are able to do your Big Data homework assignments at bachelors , masters & the research level. Here you can get top quality code and report at any basic to advanced level. We are solve lots of projects and papers related to Big Data and Machine Learning research paper so you can get code with more experienced expert.
Machine Learning Assignment Help from the Best-Qualified Experts & Professionals
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Characteristics Of Big Data
There are different types of characteristics of Big Data which is listed below:
Volume: Data is collected from a large number of sources, online and offline. The more the volume of good quality data the better the analysis
Velocity: Velocity means the rate at which data is generated. It deals with determining how fast the data is generated from various sources through the real world, online and offline
Variety: Data is available in many formats like text, images, videos, emails, offline document records and online sources.
Importance Of Big Data
Find the reason of failure in policies of businesses and eliminating the causes in future.
Time-to-time offers for the customers based on their purchases.
Detecting any fraudulent activity.
Get Help In Big Data PySpark
Apache Spark is written in Scala programming language. PySpark has been released in order to support the collaboration of Apache Spark and Python, it actually is a Python API for Spark.
Our expert provide all PySpark related help; Big Data PySpark Coding Help, Big Data PySpark Assignment Help, Big Data PySpark Homework Help, Big Data PySpark Coursework Help, etc.
Here you can get help in:
PySpark SQL Related Assignment
And More Other
!apt-get install openjdk-8-jdk-headless -qq > /dev/null
!wget -q http://www-us.apache.org/dist/spark/spark-2.4.0/spark-2.4.0-bin-hadoop2.7.tgz
!tar xf spark-2.4.0-bin-hadoop2.7.tgz
!pip install -q findspark
os.environ["JAVA_HOME"] = "C:\work\Java\jre1.8.0_301"
os.environ["SPARK_HOME"] = "C:\work\Spark\spark-3.1.2-bin-hadoop2.7"
os.environ["HADOOP_HOME"] = "C:\work\Spark\spark-3.1.2-bin-hadoop2.7"
from pyspark import SparkContext
from pyspark.sql import SQLContext
sc = SparkContext.getOrCreate()
sqlContext = SQLContext(sc)
Get Help In Big Data Map-Reduce
In a MapReduce program, Map() and Reduce() are two functions. The Map function performs actions like filtering, grouping and sorting. While Reduce function aggregates and summarizes the result produced by map function. The result generated by the Map function is a key value pair (K, V) which acts as the input for Reduce function.
Basic Steps To Perform Map Reduce:
It is a fixed-size pieces called input splits Input split is a chunk of the input that is consumed by a single map
In this phase data in each split is passed to a mapping function to produce output values. In our example, a job of mapping phase is to count a number of occurrences of each word from input splits (more details about input-split is given below) and prepare a list in the form of <word, frequency>
This phase consumes the output of Mapping phase. Its task is to consolidate the relevant records from Mapping phase output. In our example, the same words are clubed together along with their respective frequency.
In this phase, output values from the Shuffling phase are aggregated. This phase combines values from Shuffling phase and returns a single output value. In short, this phase summarizes the complete dataset.
Perform Map Reduce For Integer Number
You are given a large number of files containing positive integers. Design the MapReduce process to compute the number of even integers across all files.
Before answering the question we will creating MapReduce job using positive integers with, <key, value>
Using MapReduce, determine how many odd and even numbers of jobs in positive integer jobs.
Get Help In Big Data Hadoop/HDFS
The Hadoop Distributed File System (HDFS) is the primary data storage system used by Hadoop applications. HDFS employs a NameNode and DataNode architecture to implement a distributed file system that provides high-performance access to data across highly scalable Hadoop clusters.
Here you can get good quality code from our expert. Our expert focus to deliver code without any Plagiarism and within your due.
Big Data can be characterized by:
Volume - the amount and scale of data
Velocity - the speed at which data travels in and out
Variety - the range and complexity of data types, structures, and sources
Financial markets - 7 billion shares change hands every day in the U.S. markets
Google, Twitter, GPS data, Facebook, YouTube, etc.
MapReduce is a framework for processing large-scale data using parallel and distributed computing technologies with a large number of computers. Apache Hadoop is an open-source implementation of MapReduce.
Big Data Analytics using Spark SQL
PySpark SQL is a module in Spark which integrates relational processing with Spark's functional programming API. We can extract the data by using an SQL query language. We can use the queries same as the SQL language. If you have basic understanding related to RDBMS, SQL, and Data Analytics then you can easily work with this.
Feature of PySpark SQL: Consistence Data Access, Incorporation with Spark, Standard Connectivity, User-Defined Functions, Hive Compatibility
Visualization of big data
Big data visualization is the process of displaying data in charts, graphs, maps, and other visual forms. It is used to help people easily understand and interpret their data at a glance, and to clearly show trends and patterns that arise from this data.
Realcode4you also covers all big data visualization help which is related to any programming languages; Python, R, Java, C# etc. If you are looking to hire big data visualization expert then we are the right choice for you. Our expert coverts all types of visualizations like: Bar Plot, Line Plot, Pie Plot, Scatter Plot , Histogram, Heat Map, Dashboards, And more others.
Why Big Data Necessary For Industries
It is necessary for large number of industries which makes it better for business:
Banking and Securities
Communications, Media and Entertainment
Manufacturing and Natural Resources
Retail and Wholesale trade
Energy and Utilities
Ensemble Learning Assignment Help
A group of predictors is called an ensemble; thus, this technique is called Ensemble Learning, and an Ensemble Learning algorithm is called an Ensemble method. Ensemble methods, including bagging, boosting, and stacking. We will also explore Random Forests.
Bagging and Pasting: One way to get a diverse set of classifiers is to use very different training algorithms, as just discussed. Another approach is to use the same training algorithm for every predictor and train them on different random subsets of the training set. When sampling is performed with replacement, this method is called bagging (short for boot-strap aggregating). When sampling is performed without replacement, it is called pasting.
Bagging and Pasting in Scikit-Learn: Scikit-Learn offers a simple API for both bagging and pasting with the BaggingClassifier class (or BaggingRegressor for regression). The following code trains an ensemble of 500 Decision Tree classifiers: each is trained on 100 training instances randomly sampled from the training set with replacement (this is an example of bagging, but if you want to use pasting instead, just set bootstrap=False). The n_jobs parameter tells Scikit-Learn the number of CPU cores to use for training and predictions (–1 tells Scikit-Learn to use all available cores):
Boosting: Boosting refers to any Ensemble method that can combine several weak learners into a strong learner. The general idea of most boosting methods is to train predictors sequentially, each trying to correct its predecessor. There are many boosting methods available, but by far the most popular are AdaBoost (short for Adaptive Boosting) and Gradient Boosting. Let’s start with AdaBoost.
AdaBoost: One way for a new predictor to correct its predecessor is to pay a bit more attention to the training instances that the predecessor underfit. This results in new predictors focusing more and more on the hard cases. This is the technique used by AdaBoost. For example, when training an AdaBoost classifier, the algorithm first trains a base classifier (such as a Decision Tree) and uses it to make predictions on the training set. The algorithm then increases the relative weight of misclassified training instances. Then it trains a second classifier, using the updated weights, and again makes predictions on the training set, updates the instance weights, and so on (see the following figure).
Gradient Boosting: Another very popular boosting algorithm is Gradient Boosting. Just like AdaBoost, Gradient Boosting works by sequentially adding predictors to an ensemble, each one correcting its predecessor. However, instead of tweaking the instance weights at every iteration like AdaBoost does, this method tries to fit the new predictor to the residual errors made by the previous predictor.
Stacking: The last Ensemble method we will discuss in this chapter is called stacking (short for stacked generalization). It is based on a simple idea: instead of using trivial functions (such as hard voting) to aggregate the predictions of all predictors in an ensemble, why don’t we train a model to perform this aggregation? The following figure shows such an ensemble performing a regression task on a new instance.
Docker, DataBricks, AWS and GCP Assignment Help
Many modern-day datasets are huge and truly exemplify “big data”. For example, the Facebook social graph is petabytes large (over 1M GB); every day, Twitter users generate over 12 terabytes of messages; and the NASA Terra and Aqua satellites each produce over 300 GB of MODIS satellite imagery per day. These raw data are far too large to even fit on the hard drive of an average computer, let alone to process and analyze. Luckily, there are a variety of modern technologies that allow us to process and analyze such large datasets in a reasonable amount of time.
A main goal of this assignment is to help students gain exposure to a variety of tools that will be useful in the future (e.g., future project, research, career). The reasoning behind intentionally including AWS, Azure and GCP (most courses use only one), because we want students to be able to try and compare these platforms as they evolve rapidly. This will help the students in the future. Should they need to select a cloud platform to use, they can make more informed decisions and be able to get started right away.
Realcode4you Expert has the good knowledge in all above machine learning cloud technology. We are deliver many successful project task using different programming languages like Python, R and MATLAB.
Hire Data Bricks Expert
Databricks has excellent documentation and we defer to their guidance instead of reproducing it here. Follow these steps to get started:
1. Create a Community Edition (https://community.cloud.databricks.com/) account on Databricks. Do NOT select Databricks Platform - Free Trial; if you do, you will encounter many problems in the subsequent sections. More info: https://docs.databricks.com/getting-started/try-databricks.html
Hire AWS & PySpark Expert
You will try out PySpark for processing data on Amazon Web Services (AWS). Here you can learn more about PySpark and how it can be used for data analysis. You will be completing a task that may be accomplished using a commodity computer (e.g., consumer-grade laptops or desktops). However, we would like you to use this exercise as an opportunity to learn distributed computing on Amazon EC2, and to gain experience that will help you tackle more complex problems.
Hire GCP & PySpark Expert
Helpful tips/FAQs for special scenarios:
If GCP service is disabled for your google account, try the steps in this google support link
If you have any issues with GCP free credits, please fill out this form
Dimensionality Reduction Assignment help
It is the process of reducing the number of independent variable, if your problem related to dimensionality reduction then we will help you to do your task and fit dimensionality reduction algorithms easily in your code. Below the some features which makes it better to improve machine learning accuracy and for which you use this in machine learning algorithms.
Reducing Dimensionality of independent variables helps in many ways.
Remove multi-collinearity to improve ML model performance
Helps Reduce Over fitting
Decreases Computational time for fitting models
Makes Visualization easier
Decreases Storage requirements
Avoid Curse of dimensionality
Here we can say that dimensionality reduction plays a significant role in analyzing data.
It use different Dimensionality Reduction techniques:
Feature Extraction(PCA, t-SNE)
Hire Online Big Data Tutor
Realcode4you online tutors are ready to help you with the Big Data topics in which you are struggling. You can choose or hire our one-on-one tutoring or homework assistance to get the academic support or any industrial support. You can request live or through mail directly for online tutoring session to get personalized academic support. Our tutors provide one-to-one live session in which all doubts are clear easily. We are also provide coursework or document which help you to practice related topics in Data Analysis.
There are many doubts in your mind when you start machine learning as a programming expert. Our Tutors easily explain your all doubts in live session and provide better help in your coursework and homework.
Join our training program to Improve coding and technical skills .
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Big Data Assignments Help
The ensemble methods in machine learning combine the insights obtained from multiple learning models to facilitate accurate and improved decision. In other way we can say, a group of models or classifier are called ensemble.
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Important Data Analysis Topics
Here you can get help in : Introduction to Descriptive Analytics, Descriptive Analytics through visualization using Tableau, Introduction to tableau, Install tableau, Connecting tableau with data file, Familiarizing with user interface in tableau, etc.
introduction to predictive Analytics, Approaches to predictive analytics, Get help in predictive analytics tools, Application of predictive analytics, Techniques of predictive analytics, Steps to predictive analytics models, forecasting using tableau
introduction to prescriptive Analytics, Approaches to prescriptive analytics, Get help in prescriptive analytics tools, Application of prescriptive analytics, Techniques of prescriptive analytics, Steps to prescriptive analytics models.
Why Student Need Big Data Assignment Help
Shortage of Time
A number of students already have other work at the same time or which has same due date then to manage all task is difficult.
Lake of Skills
If you have basic programming skills or you work as a beginners then face problems to do your task then don't worry about it
Lake of Resources
If you not have proper resource which is related to your task then here you can get all support and also get resources which is related to your programming skills.
Lake of Interest
Many of the students have enough knowledge and skills but still, they are struggling only because of their interest factor.
- We Provide -
There are many problems if you are new in programming languages like: Software Installation, to running the code, write code in proper syntax, fix issues if face when run the code, and more others. Realcode4you python expert provide full guide to run the code which is related to your project task, assignment and homework.
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Programming Languages Used For Data Scientist & Machine Learning
There are many other programming languages and Tools which used to do Data Science and Machine Learning Task. Here we can discussing these programming languages in which our expert also providing the Data Science and Machine Learning programming Help:
Python: Python is the import programming language which start career as a Data Science and Machine Learning. This is first choice of professionals which learn data science. Now a day it became most popular programming language in the field of data science.
Java: Java is also used for machine learning projects but it is difficult to implement compare to Python programming language. Syntax of this programming language is complex compare to python so developer not choose this as a first choice.
R: R programming is also became most familiar with Data Science and Machine Learning expert. This is also choose by Data Science expert like a python. It is also simple like python and it provide lots of in-built libraries which make it easy to implement.
MATLAB: MATLAB is also used to predict the Data Science and Machine Learning models. Basically it used to advance level scientific calculations which is related to machine learning.
SCALA: Scala is used in Data processing, distributed computing, and web development. It powers the data engineering infrastructure of many companies. It also used by Data Scientist Developer.
PySpark: It used to handle the Big Data related task. When data is too long then right choice to implement PySpark. It also support the SQL so it make easy to execute the query.
Hive/HADOOP: It also used by Big Data expert to implement big data related task. Now a day most of industries which is working over past decades then data of these industries is too long. To handle these data Hive/HADOOP is the right choice of developer.
Tkinter: This is the python tool which used to create GUI applications. It also used to create Data Science and Machine Learning Application.
Django/Flask: These are the python Framework which is used to create GUI Applications. If you are looking to hire expert which can do your machine learning web application then you can choose these frameworks.
Big Data PySpark Sample
Write a Spark program to find the top 10 products based on the number of user reviews and report their average ratings, product price.
Dataset: Use the Amazon Instant Video review file (reviews_Amazon_Instant_Video. json) and metadata (meta_Amazon_Instant_Video.json) from the Amazon product dataset (http://jmcauley.ucsd.edu/data/amazon/links.html). Download both files from the “Per-category files” section.
Big Data Query & Analysis using Spark SQL
This task is using Spark SQL for converting big sized raw data into useful information. Each member of a group should implement 2 complex SQL queries (refer to the marking scheme). Apply appropriate visualization tools to present your findings numerically and graphically. Interpret shortly your findings.
You can use https://spark.apache.org/docs/3.0.0/sql-ref.html for more information.
What do you need to put in the HTML report per student?
At least two Spark SQL queries.
A short explanation of the queries.
The working solution, i.e., plot or table.
Tip: The mark for this section depends on the level of your queries complexity, for
instance using the simple select query is not supposed for a full mark.
Advanced Analytics using PySpark
3.1. Analyze and Interpret Big Data using PySpark
Every member of a group should analyze data through 3 analytical methods (e.g., advanced descriptive statistics, correlation, hypothesis testing, density estimation, etc.). You need to present your work numerically and graphically. Apply tooltip text, legend, title, X-Y labels etc. accordingly.
Note: we need a working solution without system or logical error for the good/full mark.
3.2. Design and Build a Machine Learning (ML) technique
Every member of a group should go over https://spark.apache.org/docs/3.0.0/ml-guide.html and apply one ML technique. You can apply one the following approaches: Classification, Regression, Clustering, Dimensionality Reduction, Feature Extraction, Frequent Pattern or Optimization. Explain and evaluate your model and its results into the numerical
and/or graphical representations.
Note: If you are 4 students in a group, you should develop 4 different models. If you have a similar model, the mark would be zero.
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