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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.
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 Installation
PySpark SQL Related Assignment
PySpark Context
And More Other
Configure PySpark
!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
import os
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"
import findspark
findspark.init()
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: Input Splits: It is a fixed-size pieces called input splits Input split is a chunk of the input that is consumed by a single map Mapping 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> Shuffling 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. Reducing 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
Problem statement: 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
Examples:
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
Big Data Sample Paper
Abstract In artificial intelligence, machine learning is a method of acquiring knowledge that may be used to make intelligent judgments. There are many benefits to using big data for scientific research and value generation. This report introduces machine learning methods, Big Data technology, and machine learning applications in Big Data. There is a discussion of the challenges of machine learning applications with big data. Also highlighted are several new machine learning methodologies and technological advancements in Big Data. The Spark Python API (PySpark) exposes the Spark programming model to Python. Apache® Spark™ is an open source and is one of the most popular Big Data frameworks for scaling up your tasks in a cluster. It was developed to utilize distributed, in-memory data structures to improve data processing speeds. Apache Spark! The top technology companies like Google, Facebook, Netflix, Airbnb, Amazon, NASA, and more are all using Spark to solve their big data problems!
Introduction
Machine learning is an important area of artificial intelligence. The goal of machine learning is to discover knowledge and make intelligent decisions. Machine learning algorithms can be classified into which are classified into supervised, unsupervised, and semi-supervised. When it comes to big data, scaling machine learning algorithms are needed (Chen and Zhang, 2014; Tarwani et al., 2015), Another classification of machine learning based on the output of the machine learning system includes classification, regression, clustering, and density estimation, etc. machine-learning approaches include decision tree learning, association rule learning, artificial neural networks, support vector machines (SVM), clustering, Bayesian networks, and genetic algorithms, etc
Initiate and Configure Spark
pip install pysparkoutput:
Collecting pyspark
Downloading pyspark-3.1.2.tar.gz (212.4 MB)
|████████████████████████████████| 212.4 MB 62 kB/s
Collecting py4j==0.10.9
Downloading py4j-0.10.9-py2.py3-none-any.whl (198 kB)
|████████████████████████████████| 198 kB 51.9 MB/s
Building wheels for collected packages: pyspark
Building wheel for pyspark (setup.py) ... done
Created wheel for pyspark: filename=pyspark-3.1.2-py2.py3-none-any.whl size=212880768 sha256=430f8e4729632d5692c9fee6be2f8e4e98e454e3702a9dab478a362e4fcaa3bc
Stored in directory: /root/.cache/pip/wheels/a5/0a/c1/9561f6fecb759579a7d863dcd846daaa95f598744e71b02c77
Successfully built pyspark
Installing collected packages: py4j, pyspark
Successfully installed py4j-0.10.9 pyspark-3.1.2Load Data
from google.colab import drive
drive.mount('/content/drive')output:
Mounted at /content/drive
from pyspark.sql import SparkSession
spark = SparkSession.builder.appName('ml-bank').getOrCreate()
#reading the data and Printing the schema of main dataset
df = spark.read.csv('/content/drive/MyDrive/UNSW-NB15_features.csv', header = True, inferSchema = True)
df.printSchema()output:
root
|-- No.: integer (nullable = true)
|-- Name: string (nullable = true)
|-- Type : string (nullable = true)
|-- Description: string (nullable = true)
import pandas as pd
#reading the dataset of features
features_df=pd.DataFrame(df.take(50), columns=df.columns)
features_df.head() output:
After preprocessing the data apply classifiers:
Binary Classifier
#Splitting the dataset
train, test = df_1.randomSplit([0.7, 0.3], seed = 2018)
print("Training Dataset Count: " + str(train.count()))
print("Test Dataset Count: " + str(test.count()))output:
Training Dataset Count: 700159
Test Dataset Count: 299841
Using Decision Tree Classifier
from pyspark.ml.classification import DecisionTreeClassifier
dt = DecisionTreeClassifier(featuresCol = 'features', labelCol = 'Label', maxDepth = 3)dtModel = dt.fit(train)predictions = dtModel.transform(test)predictions.select( 'Label', 'rawPrediction', 'prediction', 'probability').show(10)output:
Multi Classifier
cat_col2 = [key for key in dict(df_2.dtypes)if dict(df_2.dtypes)[key] in ['object'] ] # Categorical Variblenum_col2 = list(df_2.select_dtypes(exclude=['object']).columns)from pyspark.ml.feature import OneHotEncoderfrom pyspark.ml.feature import StringIndexer, VectorAssembler
from pyspark.ml.feature import StringIndexer, VectorAssemblerThe procedure incorporates Category Indexing, One-Hot Encoding, and VectorAssembler, a feature converter that merges many columns into a vector column.stages = []
for categoricalCol in cat_col2:
stringIndexer = StringIndexer(inputCol = categoricalCol, outputCol = categoricalCol + 'Index')
encoder = OneHotEncoder(inputCols=[stringIndexer.getOutputCol()], outputCols=[categoricalCol + "classVec"])
stages += [stringIndexer, encoder]
label_stringIdx = StringIndexer(inputCol = 'attack_cat', outputCol = 'target')
stages += [label_stringIdx]
assemblerInputs = [c + "classVec" for c in cat_col2] + num_col2
assembler = VectorAssembler(inputCols=assemblerInputs, outputCol="features")
stages += [assembler]from pyspark.ml.feature import StringIndexer, VectorAssembler
from pyspark import SparkContext, SparkConffrom pyspark.sql import SQLContextsc = SparkContext.getOrCreate()sqlContext = SQLContext(sc)df_2 = sqlContext.createDataFrame(df_2)cols = df_2.columnsUsing Pipeline To analyse and learn from data, it is used to run a series of algorithms.
from pyspark.ml import Pipeline
pipeline = Pipeline(stages = stages)
pipelineModel = pipeline.fit(df_2)from pyspark.ml import Pipeline
pipeline = Pipeline(stages = stages)
pipelineModel = pipeline.fit(df_2)
df_2 = pipelineModel.transform(df_2)
selectedCols = ['target', 'features'] + cols
df_2 = df_2.select(selectedCols)
df_2.printSchema()output:
...
...
#spliting the dataset into Train and Test
train, test = df_2.randomSplit([0.7, 0.3], seed = 2018)
print("Training Dataset Count: " + str(train.count()))
print("Test Dataset Count: " + str(test.count()))output:
Training Dataset Count: 138130
Test Dataset Count: 58963x['target'].value_counts()output:
0.0 103446
1.0 19786
2.0 9583
3.0 7839
4.0 6457
5.0 1131
6.0 1012
7.0 669
8.0 77 Name: target, dtype: int64
Using Decision Tree Classifier for MultiClass Classification
# Machine Learning Technique, configuration, etc.:
from pyspark.ml.classification import DecisionTreeClassifier
dt2 = DecisionTreeClassifier(featuresCol = 'features', labelCol = 'target', maxDepth = 3)
dtModel2 = dt2.fit(train)
predictions = dtModel2.transform(test)output:
predictions.select( 'target', 'rawPrediction', 'prediction', 'probability').show(5)output:
Convert ipynb to HTML for Turnitin submission
# install nbconvert
!pip install nbconvert
# convert ipynb to html
# file name: "Your-Group-ID"_CN7031.ipynb
!jupyter nbconvert --to html .....ipynb To get any type of big data assignment help, homework help and project help, you can contact us at:
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