Business Analytics using R.ppt
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Descriptive and diagnostic analytics describe and analyze organizational data through measures like mean, median, standard deviation and charts/tables to identify patterns and insights. Predictive analytics use statistical techniques like regression and clustering to predict future outcomes and behaviors. Prescriptive analytics provide recommendations for decisions and actions based on optimization models. The document then focuses on classification techniques in predictive analytics, covering methods like decision trees, rules, Naive Bayes and nearest neighbor algorithms.
Business Analytics using R.ppt
- 3. Types of Analytics Contd…
- 4. Descriptive and Diagnostic Analytics Function: describe the main features of organizational data Common tools: sampling, mean, mode, median, standard deviation, range, variance, stem and leaf diagram, histogram, interquartile range, quartiles, and frequency distributions Displaying results: graphics/charts, tables, and summary statistics such as single numbers
- 5. Function: draw conclusions and predict future behavior Common tools: cluster analysis, association analysis, multiple regression, logistic regression, decision tree methods, neural networks, text mining and forecasting tools (such as time series and causal relationships) Predictive Analytics
- 6. Function: make decisions based on data Common models: linear programming sensitivity analysis integer programming goal programming nonlinear programming simulation modeling Prescriptive Analytics
- 8. Major Techniques Classification Clustering Association Mining Regression
- 10. Classification Defined The classification problem may be formalized using a-posteriori probabilities: P(C|X) = prob. that the sample tuple X=<x1,…,xk> is of class C. E.g. P(class=N | outlook=sunny,windy=true,…) Idea: assign to sample X the class label C such that P(C|X) is maximal
- 11. Classification: predicts categorical class labels classifies data (constructs a model) based on the training set and the values (class labels) in a classifying attribute and uses it in classifying new data Typical Applications credit approval target marketing medical diagnosis treatment effectiveness analysis Classification
- 12. Classification—A Two-Step Process Model construction: describing a set of predetermined classes Each tuple/sample is assumed to belong to a predefined class, as determined by the class label attribute The set of tuples used for model construction: training set The model is represented as classification rules, decision trees, or mathematical formulae Model usage: for classifying future or unknown objects Estimate accuracy of the model The known label of test sample is compared with the classified result from the model Accuracy rate is the percentage of test set samples that are correctly classified by the model Test set is independent of training set, otherwise over-fitting will occur
- 13. Classification Process (1): Model Construction Training Data NAME RANK YEARS TENURED Mike Assistant Prof 3 no Mary Assistant Prof 7 yes Bill Professor 2 yes Jim Associate Prof 7 yes Dave Assistant Prof 6 no Anne Associate Prof 3 no Classification Algorithms IF rank = ‘professor’ OR years > 6 THEN tenured = ‘yes’ Classifier (Model)
- 14. Classification Process (2): Use the Model in Prediction Classifier Testing Data NAME RANK YEARS TENURED Tom Assistant Prof 2 no Merlisa Associate Prof 7 no George Professor 5 yes Joseph Assistant Prof 7 yes Unseen Data (Jeff, Professor, 4) Tenured?
- 15. Issues (1): Data Preparation Data cleaning Preprocess data in order to reduce noise and handle missing values Relevance analysis (feature selection) Remove the irrelevant or redundant attributes Data transformation Generalize and/or normalize data
- 16. Issues (2): Evaluating Classification Methods
- 17. Measurement The Accuracy (AC) is the proportion of the total number of predictions that were correct. Sensitivity or true positive rate (TP) is the proportion of positive cases that were correctly identified Specificity : the proportion of actual negative cases which are correctly identified(TN).
- 20. Classification by Decision Tree Induction Decision tree A flow-chart-like tree structure Internal node denotes a test on an attribute Branch represents an outcome of the test Leaf nodes represent class labels or class distribution Decision tree generation consists of two phases Tree construction At start, all the training examples are at the root Partition examples recursively based on selected attributes Tree pruning Identify and remove branches that reflect noise or outliers Use of decision tree: Classifying an unknown sample Test the attribute values of the sample against the decision tree
- 21. Training Dataset age income student credit_rating <=30 high no fair <=30 high no excellent 31…40 high no fair >40 medium no fair >40 low yes fair >40 low yes excellent 31…40 low yes excellent <=30 medium no fair <=30 low yes fair >40 medium yes fair <=30 medium yes excellent 31…40 medium no excellent 31…40 high yes fair >40 medium no excellent This follows an example from
- 22. Output: A Decision Tree for “buys_computer” age? overcast student? credit rating? no yes fair excellent <=30 >40 no no yes yes yes 30..40
- 23. Extracting Classification Rules from Trees Represent the knowledge in the form of IF-THEN rules One rule is created for each path from the root to a leaf Each attribute-value pair along a path forms a conjunction The leaf node holds the class prediction Rules are easier for humans to understand Example IF age = “<=30” AND student = “no” THEN buys_computer = “no” IF age = “<=30” AND student = “yes” THEN buys_computer = “yes” IF age = “31…40” THEN buys_computer = “yes” IF age = “>40” AND credit_rating = “excellent” THEN buys_computer = “yes” IF age = “>40” AND credit_rating = “fair” THEN buys_computer = “no”
- 25. Bayesian Theorem Given training data D, posteriori probability of a hypothesis h, P(h|D) follows the Bayes theorem MAP (maximum posteriori) hypothesis Practical difficulty: require initial knowledge of many probabilities, significant computational cost ) ( ) ( ) | ( ) | ( D P h P h D P D h P . ) ( ) | ( max arg ) | ( max arg h P h D P H h D h P H h MAP h
- 26. Bayesian classification The classification problem may be formalized using a-posteriori probabilities: P(C|X) = prob. that the sample tuple X=<x1,…,xk> is of class C. Idea: assign to sample X the class label C such that P(C|X) is maximal
- 27. Naïve Bayesian Classification Naïve assumption: attribute independence P(x1,…,xk|C) = P(x1|C)·…·P(xk|C) If i-th attribute is categorical: P(xi|C) is estimated as the relative freq of samples having value xi as i-th attribute in class C If i-th attribute is continuous: P(xi|C) is estimated thru a Gaussian density function Computationally easy in both cases
- 28. Play-tennis example Outlook Temperature Humidity Windy Class sunny hot high false N sunny hot high true N overcast hot high false P rain mild high false P rain cool normal false P rain cool normal true N overcast cool normal true P sunny mild high false N sunny cool normal false P rain mild normal false P sunny mild normal true P overcast mild high true P overcast hot normal false P rain mild high true N outlook P(sunny|p) = 2/9 P(sunny|n) = 3/5 P(overcast|p) = 4/9 P(overcast|n) = 0 P(rain|p) = 3/9 P(rain|n) = 2/5 temperature P(hot|p) = 2/9 P(hot|n) = 2/5 P(mild|p) = 4/9 P(mild|n) = 2/5 P(cool|p) = 3/9 P(cool|n) = 1/5 humidity P(high|p) = 3/9 P(high|n) = 4/5 P(normal|p) = 6/9 P(normal|n) = 2/5 windy P(true|p) = 3/9 P(true|n) = 3/5 P(false|p) = 6/9 P(false|n) = 2/5 P(p) = 9/14 P(n) = 5/14
- 29. Play-tennis example: classifying X An unseen sample X = <rain, hot, high, false> P(X|p)·P(p) = P(rain|p)·P(hot|p)·P(high|p)·P(false|p)·P(p) = 3/9·2/9·3/9·6/9·9/14 = 0.010582 P(X|n)·P(n) = P(rain|n)·P(hot|n)·P(high|n)·P(false|n)·P(n) = 2/5·2/5·4/5·2/5·5/14 = 0.018286 Sample X is classified in class n (don’t play)
- 30. Other Classification Methods k-nearest neighbor classifier case-based reasoning Genetic algorithm Rough set approach Fuzzy set approaches Support Vector Machine (SVM) Logistic Regression