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Business Analytics Decision Tree in R

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The document discusses a course on business analytics using R. It covers topics like data mining, business analytics, machine learning algorithms like clustering and decision trees. It provides examples of using R for data visualization and building decision trees. The objectives are to understand concepts like data mining and analytics, learn R programming, use R for tasks like exploratory data analysis, visualization, clustering, regression and building decision tree models.

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www.edureka.co/r-for-analytics View Business Analytics with R course details at www.edureka.co/r-for-analytics Business Analytics with R
www.edureka.co/r-for-analyticsSlide 2 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Objectives What is data mining What is Business Analytics Stages of Analytics / data mining What is R overview of Machine Learning  What is Clustering What is K-means Clustering Use-case At the end of this session, you will be able to
Slide 3Slide 3 www.edureka.co/r-for-analyticsTwitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Data mining ?? Generally, data mining is the process of studying data from maximum possible dimensions and summarizing it into useful information Technically, data mining is the process of finding correlations or patterns among dozens of fields in large data generated from business Or you can say, data mining is the process finding useful information from the data and then devising knowledge out of it for improving future of our business » Data ?? Data are any facts, numbers, or text is getting produced by existing system » Information ?? The patterns, associations, or relationships among all this data can provide information » Knowledge ?? Information can be converted into knowledge about historical patterns and future trends. For example summary of sales in off season may help to start some offers in that period to increase sales
Slide 4Slide 4 www.edureka.co/r-for-analyticsTwitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Business Analytics Why Business Analytics is getting popular these days ? Cost of storing data Cost of processing data
Slide 5Slide 5 www.edureka.co/r-for-analyticsTwitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Cross Industry standard Process for data mining ( CRISP – DM ) Stages of Analytics / Data Mining
Slide 6Slide 6 www.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
Slide 7Slide 7 www.edureka.co/r-for-analyticsTwitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions R : Characteristics Effective and fast data handling and storage facility A bunch of operators for calculations on arrays, lists, vectors etc A large integrated collection of tools for data analysis, and visualization Facilities for data analysis using graphs and display either directly at the computer or paper A well implemented and effective programming language called ‘S’ on top of which R is built A complete range of packages to extend and enrich the functionality of R
Slide 8Slide 8 www.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.
Slide 9Slide 9 www.edureka.co/r-for-analyticsTwitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Who Uses R : Domains  Telecom  Pharmaceuticals  Financial Services  Life Sciences  Education, etc
Slide 10Slide 10 www.edureka.co/r-for-analyticsTwitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions 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
www.edureka.co/r-for-analyticsSlide 11 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
www.edureka.co/r-for-analyticsSlide 12 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions What is Decision Trees? Organizing data into clusters such that there is:  High intra-cluster similarity  Low inter-cluster similarity  Informally, finding natural groupings among objects http://en.wikipedia.org/wiki/Cluster_analysis
www.edureka.co/r-for-analyticsSlide 13 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Decision Trees
www.edureka.co/r-for-analyticsSlide 14 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions What is Decision Trees??
www.edureka.co/r-for-analyticsSlide 15 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions How to Build Decision Trees??
www.edureka.co/r-for-analyticsSlide 16 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Algorithm for Decision Tree Induction
www.edureka.co/r-for-analyticsSlide 17 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Decision Tree Example??
www.edureka.co/r-for-analyticsSlide 18 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Decision Tree 1: Root “student”
www.edureka.co/r-for-analyticsSlide 19 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Decision Tree 1: Root “student”
www.edureka.co/r-for-analyticsSlide 20 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Decision Tree 1: Root “student”
www.edureka.co/r-for-analyticsSlide 21 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Decision Tree 1: Root “student”
www.edureka.co/r-for-analyticsSlide 22 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Decision Tree 1: Root “student”
www.edureka.co/r-for-analyticsSlide 23 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Decision Tree 1: Root “student”
www.edureka.co/r-for-analyticsSlide 24 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Decision Tree 1: Root “student”
www.edureka.co/r-for-analyticsSlide 25 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Decision Tree 2: Root “Income”
www.edureka.co/r-for-analyticsSlide 26 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Decision Tree 2: Root “Income”
www.edureka.co/r-for-analyticsSlide 27 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Decision Tree 2: Root “Income”
www.edureka.co/r-for-analyticsSlide 28 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Decision Tree 2: Root “Income”
www.edureka.co/r-for-analyticsSlide 29 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Decision Tree 2: Root “Income”
www.edureka.co/r-for-analyticsSlide 30 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Decision Tree 2: Root “Income”
www.edureka.co/r-for-analyticsSlide 31 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Decision Tree 2: Root “Income”
Slide 32Slide 32 www.edureka.co/r-for-analyticsTwitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Decision Trees Case-study
Slide 33Slide 33 www.edureka.co/r-for-analyticsTwitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Let’s consider a Company dataset with around 10 variables and 400 records. The attributes are as follows:  Sales  Competitor Price  Income  Advertising  Population  Price  Shelf Location at stores  Age  Education  Urban  US About the data:
Slide 34Slide 34 www.edureka.co/r-for-analyticsTwitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions The company dataset looks like this: Problem Statement: A cloth manufacturing company is interested to know about the segment or attributes causes high sale. Approach - A decision tree can be built with target variable Sale (we will first convert it in categorical variable) & all other variable will be independent in the analysis.
Slide 35 www.edureka.co/r-for-analyticsTwitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Demo More Information on R setup and applications at: http://www.edureka.in/blog/category/business-analytics-with-r/
Slide 36 www.edureka.co/r-for-analytics  Module 1 » Introduction to Business Analytics  Module 2 » Introduction to R Programming  Module 3 » Data Manipulation in R  Module 4 » Data Import Techniques in R  Module 5 » Exploratory Data Analysis  Module 6 » Data Visualization in R Course Topics  Module 7 » Data mining: Clustering Techniques  Module 8 » Data Mining: Association rule mining and Sentiment analysis  Module 9 » Linear and Logistic Regression  Module 10 » Annova and Predictive Analysis  Module 11 » Data Mining: Decision Trees and Random forest  Module 12 » Final Project Business Analytics with R class – Census Data
Slide 37 www.edureka.co/r-for-analytics

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Business Analytics Decision Tree in R

  • 1. www.edureka.co/r-for-analytics View Business Analytics with R course details at www.edureka.co/r-for-analytics Business Analytics with R
  • 2. www.edureka.co/r-for-analyticsSlide 2 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Objectives What is data mining What is Business Analytics Stages of Analytics / data mining What is R overview of Machine Learning  What is Clustering What is K-means Clustering Use-case At the end of this session, you will be able to
  • 3. Slide 3Slide 3 www.edureka.co/r-for-analyticsTwitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Data mining ?? Generally, data mining is the process of studying data from maximum possible dimensions and summarizing it into useful information Technically, data mining is the process of finding correlations or patterns among dozens of fields in large data generated from business Or you can say, data mining is the process finding useful information from the data and then devising knowledge out of it for improving future of our business » Data ?? Data are any facts, numbers, or text is getting produced by existing system » Information ?? The patterns, associations, or relationships among all this data can provide information » Knowledge ?? Information can be converted into knowledge about historical patterns and future trends. For example summary of sales in off season may help to start some offers in that period to increase sales
  • 4. Slide 4Slide 4 www.edureka.co/r-for-analyticsTwitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Business Analytics Why Business Analytics is getting popular these days ? Cost of storing data Cost of processing data
  • 5. Slide 5Slide 5 www.edureka.co/r-for-analyticsTwitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Cross Industry standard Process for data mining ( CRISP – DM ) Stages of Analytics / Data Mining
  • 6. Slide 6Slide 6 www.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 7 www.edureka.co/r-for-analyticsTwitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions R : Characteristics Effective and fast data handling and storage facility A bunch of operators for calculations on arrays, lists, vectors etc A large integrated collection of tools for data analysis, and visualization Facilities for data analysis using graphs and display either directly at the computer or paper A well implemented and effective programming language called ‘S’ on top of which R is built A complete range of packages to extend and enrich the functionality of R
  • 8. Slide 8Slide 8 www.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.
  • 9. Slide 9Slide 9 www.edureka.co/r-for-analyticsTwitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Who Uses R : Domains  Telecom  Pharmaceuticals  Financial Services  Life Sciences  Education, etc
  • 10. Slide 10Slide 10 www.edureka.co/r-for-analyticsTwitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions 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
  • 11. www.edureka.co/r-for-analyticsSlide 11 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
  • 12. www.edureka.co/r-for-analyticsSlide 12 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions What is Decision Trees? Organizing data into clusters such that there is:  High intra-cluster similarity  Low inter-cluster similarity  Informally, finding natural groupings among objects http://en.wikipedia.org/wiki/Cluster_analysis
  • 13. www.edureka.co/r-for-analyticsSlide 13 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Decision Trees
  • 14. www.edureka.co/r-for-analyticsSlide 14 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions What is Decision Trees??
  • 15. www.edureka.co/r-for-analyticsSlide 15 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions How to Build Decision Trees??
  • 16. www.edureka.co/r-for-analyticsSlide 16 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Algorithm for Decision Tree Induction
  • 17. www.edureka.co/r-for-analyticsSlide 17 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Decision Tree Example??
  • 18. www.edureka.co/r-for-analyticsSlide 18 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Decision Tree 1: Root “student”
  • 19. www.edureka.co/r-for-analyticsSlide 19 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Decision Tree 1: Root “student”
  • 20. www.edureka.co/r-for-analyticsSlide 20 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Decision Tree 1: Root “student”
  • 21. www.edureka.co/r-for-analyticsSlide 21 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Decision Tree 1: Root “student”
  • 22. www.edureka.co/r-for-analyticsSlide 22 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Decision Tree 1: Root “student”
  • 23. www.edureka.co/r-for-analyticsSlide 23 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Decision Tree 1: Root “student”
  • 24. www.edureka.co/r-for-analyticsSlide 24 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Decision Tree 1: Root “student”
  • 25. www.edureka.co/r-for-analyticsSlide 25 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Decision Tree 2: Root “Income”
  • 26. www.edureka.co/r-for-analyticsSlide 26 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Decision Tree 2: Root “Income”
  • 27. www.edureka.co/r-for-analyticsSlide 27 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Decision Tree 2: Root “Income”
  • 28. www.edureka.co/r-for-analyticsSlide 28 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Decision Tree 2: Root “Income”
  • 29. www.edureka.co/r-for-analyticsSlide 29 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Decision Tree 2: Root “Income”
  • 30. www.edureka.co/r-for-analyticsSlide 30 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Decision Tree 2: Root “Income”
  • 31. www.edureka.co/r-for-analyticsSlide 31 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Decision Tree 2: Root “Income”
  • 32. Slide 32Slide 32 www.edureka.co/r-for-analyticsTwitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Decision Trees Case-study
  • 33. Slide 33Slide 33 www.edureka.co/r-for-analyticsTwitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Let’s consider a Company dataset with around 10 variables and 400 records. The attributes are as follows:  Sales  Competitor Price  Income  Advertising  Population  Price  Shelf Location at stores  Age  Education  Urban  US About the data:
  • 34. Slide 34Slide 34 www.edureka.co/r-for-analyticsTwitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions The company dataset looks like this: Problem Statement: A cloth manufacturing company is interested to know about the segment or attributes causes high sale. Approach - A decision tree can be built with target variable Sale (we will first convert it in categorical variable) & all other variable will be independent in the analysis.
  • 35. Slide 35 www.edureka.co/r-for-analyticsTwitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Demo More Information on R setup and applications at: http://www.edureka.in/blog/category/business-analytics-with-r/
  • 36. Slide 36 www.edureka.co/r-for-analytics  Module 1 » Introduction to Business Analytics  Module 2 » Introduction to R Programming  Module 3 » Data Manipulation in R  Module 4 » Data Import Techniques in R  Module 5 » Exploratory Data Analysis  Module 6 » Data Visualization in R Course Topics  Module 7 » Data mining: Clustering Techniques  Module 8 » Data Mining: Association rule mining and Sentiment analysis  Module 9 » Linear and Logistic Regression  Module 10 » Annova and Predictive Analysis  Module 11 » Data Mining: Decision Trees and Random forest  Module 12 » Final Project Business Analytics with R class – Census Data