Sentiment Analysis In Retail Domain

Sentiment Analysis In Retail Domain

• 1.
  www.edureka.in/data-science Data Science Sentiment Analysisin Retail Domain
• 2.
  www.edureka.co/r-for-analyticsSlide 2 Twitter@edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions 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
• 3.
  www.edureka.in/data-scienceSlide 3 How aboutthis? Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions
• 4.
  www.edureka.in/data-scienceSlide 4 Data ScienceApplications?? 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 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions
• 5.
  Slide 5Slide 5www.edureka.co/r-for-analyticsTwitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Knowledge discovery and data mining ( KDD) Stages of Analytics / Data Mining
• 6.
  Slide 6Slide 6www.edureka.co/r-for-analyticsTwitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions What is R R is Programming Language R is Environment for Statistical Analysis R is Data Analysis Software
• 7.
  Slide 7Slide 7www.edureka.co/r-for-analyticsTwitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Data Visualization in R This 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.
• 8.
  Slide 8Slide 8www.edureka.co/r-for-analyticsTwitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions 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).
• 9.
  Slide 9Slide 9www.edureka.co/r-for-analyticsTwitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Components data science??
• 10.
  Slide 10 www.edureka.in/data-science DataScience: 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
• 11.
  Slide 11 www.edureka.in/data-science DataScience: Job Trends
• 12.
  Slide 12 www.edureka.in/data-science
• 13.
  Slide 13 www.edureka.in/data-science Hadoopand R together
• 14.
  Slide 14 www.edureka.in/data-science MachineLearning We 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
• 15.
  Slide 15 www.edureka.in/data-science Typesof 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
• 16.
  Slide 16 Twitter@edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions • 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
• 17.
  Slide 17 www.edureka.in/data-science UseCase : Zomato Ratings Review
• 18.
  Slide 18 www.edureka.in/data-science 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 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions
• 19.
  Slide 19 Questions? Enroll forthe Complete Course at : www.edureka.in/data_science Twitter @edurekaIN, Facebook /edurekaIN, use #askEdureka for Questions www.edureka.in/data_science 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/