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Map Reduce In Machine Learning

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.

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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.

map reduce image integer example.JPG
Map Reduce For Reduce Class.JPG

Lets Understand It Workflow 

What Mapper Do?

Maps input key/value pairs to a set of intermediate key/value pairs.

Maps are the individual tasks which transform input records into a intermediate records. The transformed intermediate records need not be of the same type as the input records. A given input pair may map to zero or many output pairs.

What are the key/value pairs that are INPUT to the first Mapper?

We can easily say that input for mapper is the combination of key/value, where key is offset and value is particular job.

{#offset, “2 4 5”}

This is the input for mapper which has argument where key = #offset and value = “2 4 5”

What are the key/value pairs that are OUTPUT of the first Mapper?

The output of Mapper the pairs of key/value, which is generated after filtering it with given condition, in above figure we will filter pairs of job which has even numbers:

Example: (2,“2 4 5”) and (1,“9 7 6”)

Applications of Map Reduce

MapReduce is used in many applications let us have a look at some of the applications:

Entertainment 

Hadoop and MapReduce are used by Netflix to recommend the user some popular movies based on what they watched and which movie they like.

MapReduce can determine how users are watching movies, analyzing their logs and clicks.

E-commerce: 

Many E-commerce companies such as Flipkart, Amazon, and eBay use MapReduce to analyze the buying behavior of the customers based on customers’ interests or their buying behavior

 

MapReduce programming offers several benefits

Scalability. Businesses can process petabytes of data stored in the Hadoop Distributed File System (HDFS).

Flexibility. Hadoop enables easier access to multiple sources of data and multiple types of data.

Speed. With parallel processing and minimal data movement, Hadoop offers fast processing of massive amounts of data.

Simple. Developers can write code in a choice of languages, including Java, C++ and Python.

Other Big Data Topics In Which You can Get Help

  1. Big Data Revolution

  2. Hadoop Architecture and Ecosystem

  3. Setting up Hadoop

  4. Hadoop Distributed File System (HDFS) Architecture

  5. Hadoop Distributed File System (HDFS) Programming Basics

  6. Hadoop Distributed File System (HDFS) Programming Advanced

  7. YARN and MapReduce Architecture

  8. MapReduce Programming Basics

  9. MapReduce Programming Intermediate

  10. MapReduce Programming Advanced

  11. Data Analysis using Hive

  12. Data Analysis using Pig

  13. Hadoop NOSQL Database HBase

  14. Spark

  15. Miscellaneous Hadoop Topics

 

Here You Get

  • Understand the trends that is fueling the modern Big Data Revolution.

  • Gain a solid understanding of the Apache Hadoop Architecture including HDFS and MapReduce.

  • Apply the HDFS Programming model and the ability to author HDFS Programs using Apache Hadoop HDFS API for importing and exporting data into Hadoop.

  • Apply the Distributed Storage and Distributed Programming model for distributed processing.

  • Best practices for Hadoop development, debugging, and implementation of work

  • How to leverage Hive and Pig for big data processing, and a look at related Hadoop projects.

 

Required Software

Oracle VM VirtualBox

You will need to install VirtualBox Oracle VM. This is open source software. Information on how to install and configure you can get from us.

 

Ubuntu Linux

You will need to install CentOS Linux as your own VM on VirtualBox. This is open source software. Information on how to install and configure you can get from us.

 

Apache Hadoop

You will need to install Apache Hadoop inside your CentOS Linux VM. Hadoop is open source software. Get help in how to install and configure it also when start this.

 

Apache Hive

You will need to install Apache Hive inside your CentOS Linux VM. Hadoop is open source software. Get help in how to install and configure it also when start this.

 

Apache Pig

You will need to install Apache Pig inside your CentOS Linux VM. Hadoop is open source software. Get help in how to install and configure it also when start this.

 

Apache HBase

You will need to install Apache Pig inside your CentOS Linux VM. Hadoop is open source software. Get help in how to install and configure it also when start this.

 

Apache Spark

You will need to install Apache Spark inside your CentOS Linux VM. Hadoop is open source software. Get help in how to install and configure it also when start this.

 

IDE (NetBeans or Eclipse)

You will need to install NetBeans or Eclipse inside your CentOS VM. NetBeans or Eclipse is your standard open source IDE software.

Cloudera QuickStart VM Big Data Assignment Help

This is simple Cloudera’s Big Data platform.  If you need help in any big data which is related to Map-Reduce.

These are free for personal use, but do require you to register your details on the
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requirements.

Below the basic requirement to install the cloudera in machine:

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  2. RAM of 12+ GB. That is 4+ GB for the operating system and 8+ GB for Cloudera

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Complete Installation guide you can get from below link:

https://ugc.futurelearn.com/uploads/files/3c/c9/3cc92360-1155-4eee-8d59-4c7c0e3d192c/Instructions_Installing_Cloudera.pdf

Analyze Big Data Using Cloudera Quick Start VM.jpg

Best Machine Learning Sample Projects 2023

Project 1(OpenCV): Domain- Entertainment

Company X owns a movie application and repository which caters movie streaming to millions of users who on subscription basis. Company wants to automate the process of cast and crew information in each scene from a movie such that when a user pauses on the movie and clicks on cast information button, the app will show details of the actor in the scene. Company has an in-house computer vision and multimedia experts who need to detect faces from screen shots from the movie scene. The data labelling is already done. Since there higher time complexity is involved in the

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Germany’s government is interested in reducing gender inequality, especially gender wage gaps and gender gaps in employment. They are considering the introduction of a set of
policies that incentivize firms to shrink gender inequality in working conditions. First, the policy forces firms to internally publish salaries of all workers, so discrepancies in salaries can be detected by workers themselves. Second, firms are incentivized to encourage salary negotiations. Third, firms are incentivized to offer childcare where needed for their employees to fulfill their duties.
The government has been made aware that, in parts of the United States, exactly these policies have been introduced and now asks you to evaluate the effectiveness of these policies in reducing gender inequality in wages and employment. The dataset genderinequality (provided in RData and csv formats) contains data on individuals in the U.S., some working in firms to which the new policies apply (these are the treated workers) and some working at firms to which the new policies do not apply (these are the untreated workers). The policies have been introduced in 2007 and we have panel data on workers for the years 2005 and 2010, i.e. before and after the introduction of the new policies.

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Project 3: ReneWind

Renewable energy sources play an important role in the global energy mix, as the effort to reduce the environmental impact of energy production increases. Wind energy is one of the most developed technologies worldwide and the U.S Department of Energy has put together a guide to achieving operational efficiency using predictive maintenance practices. Predictive maintenance means failure patterns are predictable and if component failure can be predicted accurately and the component is replaced before it fails, the costs of operation and maintenance will be much lower. ReneWind is a company working on improving the machinery/processes involved in the production of wind energy using machine learning and has collected data of generator failure of wind turbines using sensors. ReneWind is a company working on improving the machinery/processes involved in the production of wind energy using machine learning and has collected data of generator failure of wind turbines using sensors.

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Related to Machine Learning

Below the list of python machine learning visualizations in which you can also get help to analyze the data. It makes easy to understand the data for any non technical persons. There are many types of visualizations used in data science and machine learning

Below the list of machine learning visualizations in which you can also get help to analyze the data. It makes easy to understand the data for any non technical persons. There are many types of visualizations used in data science and machine learning:

  • Parallel Coordinates chart: Parallel coordinates is a visualization technique used to plot individual data elements across many performance measures. Each of the measures corresponds to a vertical axis and each data element is displayed as a series of connected points along the measure/axes.

  • Density Plot: Density Plot is a type of data visualization tool. It is a variation of the histogram that uses ‘kernel smoothing’ while plotting the values. It is a continuous and smooth version of a histogram inferred from a data.

  • Column Chart: A column chart is a data visualization where each category is represented by a rectangle, with the height of the rectangle being proportional to the values being plotted. Column charts are also known as vertical bar charts.

  • Bar Graph: A bar plot or bar chart is a graph that represents the category of data with rectangular bars with lengths and heights that is proportional to the values which they represent. The bar plots can be plotted horizontally or vertically

  • Stacked Bar Graph: A stacked bar chart is also known as a stacked bar graph. It is a graph that is used to compare parts of a whole. In a stacked bar chart each bar represents the whole, and the segments or parts in the bar represent categories of that whole

  • Grouped Bar Chart: A grouped barplot is used when you have several groups, and subgroups of these groups. The example in this post shows how to build a grouped barplor using the bar() function of matplotlib library.

  • Area Chart: An area chart or area graph displays graphically quantitative data. It is based on the line chart. The area between axis and line are commonly emphasized with colors, textures and hatchings. Commonly one compares two or more quantities with an area chart.

  • Dual Axis Chart: A dual axis chart (also called a multiple axes chart) uses two axes to easily illustrate the relationships between two variables with different magnitudes and scales of measurement. The relationship between two variables is referred to as correlation.

  • Line Graph: A line chart or line plot or line graph or curve chart is a type of chart which displays information as a series of data points called 'markers' connected by straight line segments. It is a basic type of chart common in many fields. 

  • Candle set chart: A typical candlestick chart is composed of a series of bars, known as candles, which vary in height and color. Candlestick charts are one of the most popular chart types for day traders. Learn how to read these charts and apply them to your trading

  • Box and whisker plot: A Box and Whisker Plot (or Box Plot) is a convenient way of visually displaying the data distribution through their quartiles.

  • Mekko Chart: A Mekko chart (sometimes also called marimekko chart) is a two-dimensional stacked chart. In addition to the varying segment heights of a regular stacked chart, a Mekko chart also has varying column widths. Column widths are scaled such that the total width matches the desired chart width. 

  • Pie Chart: A pie chart, sometimes called a circle chart, is a way of summarizing a set of nominal data or displaying the different values of a given variable (e.g. percentage distribution).

  • Bubble Chart: Bubble chart displaying the relationship between poverty and violent and property crime rates by state. Larger bubbles indicate higher percentage of state residents at or below the poverty level. 

  • Scatter Plot Chart: A scatter plot (aka scatter chart, scatter graph) uses dots to represent values for two different numeric variables. The position of each dot on the horizontal and vertical axis indicates values for an individual data point.

  • Grouped Scatter Chart: Display scatter plot of two variables. Adding a grouping variable to the scatter plot is possible. In this we group more than two variable that called group scatter chart.

  • Scatter Plot Matrix: A scatter plot matrix is a grid (or matrix) of scatter plots used to visualize bivariate relationships between combinations of variables. Each scatter plot in the matrix visualizes the relationship between a pair of variables, allowing many relationships to be explored in one chart.

  • Radar Chart: A radar chart is a way of showing multiple data points and the variation between them. They are often useful for comparing the points of two or more different data sets.

  • Radial Bar Chart: A Radial/Circular bar chart is a bar chart displayed on a polar coordinate system. The difference between radial column chart is that base axis of series is y axis of a radar chart making columns circular. You can easily adjust start/end angles of a chart by setting startAngle and endAngle of your RadarChart component.

  • Donut chart: A donut chart is essentially a Pie Chart with an area of the centre cut out. A donut chart (also spelled doughnut) is functionally identical to a pie chart, with the exception of a blank center and the ability to support multiple statistics at once.

  • Bullet Graph: A bullet graph is a variation of a bar graph developed to replace dashboard gauges and meters. A bullet graph is useful for comparing the performance of a primary measure to one or more other measures

  • Funnel Chart: A funnel chart is a specialized chart type that demonstrates the flow of users through a business or sales process. Funnel charts show values across multiple stages in a process. For example, you could use a funnel chart to show the number of sales prospects at each stage.

  • TreeMap: A treemap chart provides a hierarchical view of your data and makes it easy to spot patterns, such as which items are a store's best sellers. Treemapping is a data visualization technique that is used to display hierarchical data using nested rectangles;

  • Dendo gram: A dendrogram (or tree diagram) is a network structure. It is constituted of a root node that gives birth to several nodes connected by edges or branches.

  • Heat Map: A heat map is a two-dimensional representation of data in which values are represented by colors. A simple heat map provides an immediate visual summary of information. More elaborate heat maps allow the viewer to understand complex data sets.

  • Violin Chart: A violin plot is a method of plotting numeric data. It is similar to a box plot, with the addition of a rotated kernel density plot on each side.

  • Area graph: An area chart or area graph displays graphically quantitative data. It is based on the line chart. The area between axis and line are commonly emphasized with colors, textures and hatchings. Commonly one compares two or more quantities with an area chart.

  • stacked Area graph: Stacked Area Graphs work in the same way as simple Area Graphs do, except for the use of multiple data series that start each point from the point left by the previous data series. 

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