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Predictive Analytics - An Overview
The document provides an overview of predictive analytics, discussing its definition, techniques, and business applications. It outlines the predictive analytics process, common methods such as regression and classification, and evaluates model performance. Additionally, it highlights various sectors where predictive analytics can be employed, including healthcare, finance, retail, and manufacturing.
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Predictive Analytics - An Overview
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- 2. Introduction toBig Data. What is Analytics? Overview of Predictive Analytics Techniques. Business Applications of Predictive Analytics. Predictive Analytics Tools in Market. Agenda
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- 4. Things That HappenOn Internet Every Sixty Seconds
- 5. Things That HappenEvery Sixty Seconds
- 6. The 5 V'sof 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 BigData Adoption Stages
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- 9. OSEMN is anacronym that rhymes with “awesome” Data Analysis: OSEMN Process Obtain Data Scrub Data Explore Data Model Data iNterpret Results
- 10. Predictive analytics isthe 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?
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- 12. • Regression: Predicting outputvariable 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 rulemining: 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 tocheck 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 ofPredictive Analytics Factory Failures FinanceSmarter Healthcare Multi-channel sales Telecom Manufacturing Traffic Control Trading Analytics Fraud and Risk Renewable Energy Spam Filters Retail: Churn
- 16. • Supply Chain: Simulateand 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: Energyforecasting, 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 QualityControl: 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 Toolsin Market
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