An Overview of Predictive Analytics - MachinePulse

1. Vijaykumar Adamapure MachinePulse. Predictive Analytics - An overview

2. Introduction to Big Data. What is Analytics? Overview of Predictive Analytics Techniques. Business Applications of Predictive Analytics. Predictive Analytics Tools in Market. Agenda

3. Gartner Hype Cycle

4. Things That Happen On Internet Every Sixty Seconds

5. Things That Happen Every Sixty Seconds

6. The 5 V's of Big Data “Big data is high-volume, high-velocity and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making.”

7. Survey on Big Data Adoption Stages

8. What is Analytics?

9. OSEMN is an acronym that rhymes with “awesome” Data Analysis: OSEMN Process Obtain Data Scrub Data Explore Data Model Data iNterpret Results

10. Predictive analytics is the practice of extracting insights from the existing data set with the help data mining, statistical modeling and machine learning techniques and using it to predict unobserved/unknown events. Identifying cause-effect relationships across the variables from the historical data. Discovering hidden insights and patterns with the help of data mining techniques. Apply observed patterns to unknowns in the Past, Present or Future. What is Predictive Analytics?

11. Predictive Analytics Process Cycle

12. Regression: Predicting output variable using its cause-effect relationship with input variables. OLS Regression, GLM, Random forests, ANN etc. Classification: Predicting the item class. Decision Tree, Logistic Regression, ANN, SVM, Naïve Bayes classifier etc. Time Series Forecasting: Predicting future time events given past history. AR, MA, ARIMA, Triple Exponential Smoothing, Holt-Winters etc. Common Predictive Analytics Methods

13. Association rule mining: Mining items occurring together. Apriori Algorithm. Clustering: Finding natural groups or clusters in the data. K-means, Hierarchical, Spectral, Density based EM algorithm Clustering etc. Text mining: Model and structure the information content of textual sources. Sentiment Analysis, NLP Common Predictive Analytics Methods (Contd.)

14. Need to check predictive model’s out of sample performance. Model Assessment: Hit Rate, Gini Coefficient, K-S Chart, Confusion Matrix, ROC Curve, Lift Chart, Gain Chart etc. Evaluating Predictive Models

15. Business Applications of Predictive Analytics Renewable Energy Multi-channel sales Smarter Healthcare Finance Factory Failures Telecom Traffic Control Spam Filters Fraud and Risk Retail: Churn Manufacturing Trading Analytics

16. Supply Chain: Simulate and optimize supply chain flows to reduce inventory. Customer Profiling: Identify high valued customers and retain their loyalty. Pricing: Identify the optimal price which will increase net profit. Human Resources: Best Employees selection for particular tasks at optimal compensation. Employee churn retention. Business Applications (Contd.)

17. Renewable Energy: Energy forecasting, electricity price forecasting, Predictive Maintenance, Operational cost minimization. Financial Services: Approval of credit cards/ loan applications based on credit scoring models, Options pricing, Risk analysis etc. E-Commerce: Identify cross-sell and upsell opportunities, increase transactions size, maximize campaign's response based CRM data. Business Applications (Contd.)

18. Product Quality Control: Detect product quality issues in advance and prevent them. Revenue Performance: Identify key drivers of revenue generation and optimization of revenue. Fraud and Crime Detection: Detect fraud , criminal activity, insurance claims, tax evasion and credit card frauds. HealthCare: Identify prevalence of particular disease to a patient based health conditions. Business Applications (Contd.)

19. Predictive Analytics Tools in Market

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