Sentiment Analysis In Retail Domain

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Data ScienceSentiment Analysis in Retail

Domain

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Objectives

What is data mining

Stages of data mining??

What is R

What is data science??

What is need of data scientist??

Roles and Responsibilities of a Data Scientist.

Sentiment analysis on zomato reviews

At the end of this session, you will be able to

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How about this?

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Data Science Applications??

According to Wikipedia: Data science is the study of the generalizable extraction of knowledge from data, yet the key word is science.

These scenarios involve:

Storing, organizing and integrating huge amount of unstructured data Processing and analyzing the data Extracting knowledge, insights and predict future from the data

Storage of big data is done in Hadoop. For more details on Hadoop please refer Big data and Hadoop blog http://www.edureka.in/blog/category/big-data-and-hadoop/

Processing, Analyzing, extracting knowledge and insights are done through Machine Learning

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Knowledge discovery and data mining ( KDD)

Stages of Analytics / Data Mining

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What is R

R is Programming Language

R is Environment for Statistical Analysis

R is Data Analysis Software

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Data Visualization in RThis plot represents the

locations of all the traffic signals in the city.

It is recognizable as Toronto without any other geographic data being plotted - the structure of the city comes out in the data alone.

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What is data science??

“More data usually beats better algorithms,” Such as: Recommending movies or music based on past preferences

No matter how extremely unpleasant your algorithm is, they can often be beaten simply by having more data (and a less sophisticated algorithm).

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Components data science??

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Data Science: Demand Supply Gap

Big Data Analyst

Big Data Architect

Big Data Engineer

Big Data Research Analyst

Big Data Visualizer

Data Scientist

50

43

44

31

23

18

50

57

56

69

77

82

Filled job vs unfilled jobs in big data

Filled Unfilled

Vacancy/Filled(%)

Gartner Says Big Data Creates Big Jobs: 4.4 Million IT Jobs Globally to Support Big Data By 2015http://www.gartner.com/newsroom/id/2207915

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Data Science: Job Trends

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Hadoop and R together

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Machine LearningWe have so many algorithms for data mining which can be used to build systems that can read past data and can

generate a system that can accommodate any future data and derive useful insight from it

Such set of algorithms comes under machine learning

Machine learning focuses on the development of computer programs that can teach themselves to grow and change

when exposed to new data

Train data

ML

model

Algorithms

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Types of Learning

Supervised Learning Unsupervised Learning

1. Uses a known dataset to make predictions.

2. The training dataset includes input data and response values.

3. From it, the supervised learning algorithm builds a model to make predictions of the response values for a new dataset.

1. Draw inferences from datasets consisting of input data without labeled responses.

2. Used for exploratory data analysis to find hidden patterns or grouping in data

3. The most common unsupervised learning method is cluster analysis.

Machine Learning

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• Common Machine Learning Algorithms

Types of Learning

Supervised Learning

Unsupervised Learning

Algorithms

Naïve Bayes Support Vector Machines Random Forests Decision Trees

Algorithms

K-means

Fuzzy Clustering

Hierarchical Clustering

Gaussian mixture models

Self-organizing maps

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Use Case : Zomato Ratings Review

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Module 1

» Introduction to Data Science

Module 2

» Basic Data Manipulation using R

Module 3

» Machine Learning Techniques using R Part -1

- Clustering

- TF-IDF and Cosine Similarity

- Association Rule Mining

Module 4

» Machine Learning Techniques using R Part -2

- Supervised and Unsupervised Learning

- Decision Tree Classifier

Course Topics

Module 5

» Machine Learning Techniques using R Part -3

- Random Forest Classifier

- Naïve Bayer’s Classifier

Module 6

» Introduction to Hadoop Architecture

Module 7

» Integrating R with Hadoop

Module 8

» Mahout Introduction and Algorithm Implementation

Module 9

» Additional Mahout Algorithms and Parallel Processing in R

Module 10

» Project

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Questions?Enroll for the Complete Course at : www.edureka.in/data_science

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Please Don’t forget to fill in the survey report

Class Recording and Presentation will be available in 24 hours at:http://www.edureka.in/blog/application-of-clustering-in-data-science-using-real-life-examples/