ARAVINDAN SRINIVASAN (@aravindan81222)
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aravindan81222

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1

HYPOTHESES-TESTING-R-MARKDOWN-

“This document provides an information to houses sold in City of Melbourne . It includes all the details of the different types of houses sold in different years . We have used the variable name as ‘housing.dataset’ in order to read our csv file and store in the dataframe . We will use different variables for Hypotheses Testing.”
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2

VECTOR-SPACE-MODEL-OF-INFORMATION-RETRIEVAL-USING-JAVA

You are required to implement the Vector Space Model of Information Retrieval. You must create a program (using Java) that, using the files in the archive Assignment_Files.zip
Java
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CORE-LAMBDA-ARCHITECTURE-

Lambda architecture is a data-processing architecture designed to handle massive quantities of data by taking advantage of both batch and stream-processing methods.
Jupyter Notebook
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PREDICTIVE-ANALYTICS-AND-VISUALISATION

Attributes are very important to actionable insights. In this report we going to use four different sets of variables to find the relationship between them and give a useful information for decision making. These decisions will be useful for movie makers, audiences and reviewers to improve them and make use of it efficiently.
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5

CONCURRENT-PROGRAMMING-WITH-JAVA

concurrency is the ability to run several programs or several parts of a program in parallel. Concurrency enable a program to achieve high performance and throughput by utilizing the untapped capabilities of underlying operating system and machine hardware.
Java
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6

CLOUD-COMPUTING-PAAS-GPU

In this pdf, I'm going to depict and explain the methods, I have used in this project. Overview of the project is Creating and processing GPU. Totally, there are 7 brackets we are covering in this pdf. • Bracket 1: Providing login and logout service for users. • Bracket 2: Allowing user to create a model with GPU information and set of features. Input GPU name is made into key name. • Bracket 3: Building User Interface form user to get information and store those information in our datastore • Bracket 4: No same GPU key name is selected and listing all the available GPU names in the main page. • Bracket 5: Making a hyperlink to view and edit the GPU information for users. • Bracket 6: Allowing user to find the desired GPU by selecting their features. • Bracket 7: Comparison of Two GPU's in separate page.
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7

SENTIMENT-ANALYSIS-TEXT-MINING-USING-LAMBDA-ARCHITECTURE

Sentiment analysis on amazon product reviews using machine learning techniques and extracting the strongest positive, negative and neutral reviews using lambda architecture. Sentiment analysis fetches customer's feelings, emotions and attitudes towards the product. The need for this type of analysis has increased to a greater extent. Product manufacturers realized that sentiment analysis is the key to achieve success. The moment the company receives valuable information after sentiment analysis, they would dig deep about the issue. Companies can improve their product weakness and respond to the voice of the customer. Big data systems collect information from a variety of sources. Therefore, the need for Architecture that should deal with the huge amount of data at high velocity is a major requirement. Extracted data should be gathered in an effective database. Using Cassandra makes our data available to do visualization technique. Hence, lambda architecture helps in data-processing to manage massive quantities of data by utilizing both stream and batch processing. This big data system provides some solutions for solving sentiment analysis over massive amounts of data. The final data is gathered and forwarded to visualization in word cloud and text blob by displaying the strongest positive, negative and neutral words.
Jupyter Notebook
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