Computational Business Analytics by Subrata Das

By Subrata Das

Learn the best way to competently Use the most recent Analytics methods on your Organization

Computational company Analytics provides instruments and methods for descriptive, predictive, and prescriptive analytics appropriate throughout a number of domain names. via many examples and not easy case stories from various fields, practitioners simply see the connections to their very own difficulties and will then formulate their very own answer strategies.

The e-book first covers center descriptive and inferential records for analytics. the writer then complements numerical statistical suggestions with symbolic synthetic intelligence (AI) and computer studying (ML) strategies for richer predictive and prescriptive analytics. With a different emphasis on tools that deal with time and textual facts, the text:

  • Enriches critical part and issue analyses with subspace equipment, equivalent to latent semantic analyses
  • Combines regression analyses with probabilistic graphical modeling, similar to Bayesian networks
  • Extends autoregression and survival research thoughts with the Kalman clear out, hidden Markov types, and dynamic Bayesian networks
  • Embeds determination bushes inside of effect diagrams
  • Augments nearest-neighbor and k-means clustering innovations with help vector machines and neural networks

These methods will not be replacements of conventional statistics-based analytics; really, ordinarily, a generalized method might be diminished to the underlying conventional base approach less than very restrictive stipulations. The e-book indicates how those enriched options supply effective ideas in components, together with client segmentation, churn prediction, credits danger overview, fraud detection, and ads campaigns.

Show description

Read or Download Computational Business Analytics PDF

Similar data mining books

Mining Imperfect Data: Dealing with Contamination and Incomplete Records

Information mining is worried with the research of databases big enough that numerous anomalies, together with outliers, incomplete info files, and extra sophisticated phenomena corresponding to misalignment mistakes, are almost bound to be current. Mining Imperfect info: facing illness and Incomplete documents describes intimately a couple of those difficulties, in addition to their assets, their results, their detection, and their therapy.

Unsupervised Information Extraction by Text Segmentation

A brand new unsupervised method of the matter of data Extraction by means of textual content Segmentation (IETS) is proposed, applied and evaluated herein. The authors’ method is determined by details to be had on pre-existing facts to benefit the way to affiliate segments within the enter string with attributes of a given area hoping on a truly powerful set of content-based good points.

Computational Science and Its Applications – ICCSA 2014: 14th International Conference, Guimarães, Portugal, June 30 – July 3, 2014, Proceedings, Part VI

The six-volume set LNCS 8579-8584 constitutes the refereed lawsuits of the 14th overseas convention on Computational technology and Its functions, ICCSA 2014, held in Guimarães, Portugal, in June/July 2014. The 347 revised papers awarded in 30 workshops and a distinct song have been rigorously reviewed and chosen from 1167.

Handbook of Educational Data Mining

Cristobal Romero, Sebastian Ventura, Mykola Pechenizkiy and Ryan S. J. d. Baker, «Handbook of academic facts Mining» . instruction manual of academic information Mining (EDM) offers an intensive evaluation of the present kingdom of data during this zone. the 1st a part of the publication contains 9 surveys and tutorials at the relevant info mining innovations which have been utilized in schooling.

Extra resources for Computational Business Analytics

Sample text

6, 6)}✱ {sunny, rain, snow}✱ ❛♥❞ {t : t ∈ [0♦ ❈, 100♦ ❈]} ❛r❡✱ r❡s♣❡❝t✐✈❡❧②✱ ❡①❛♠♣❧❡s ♦❢ s❛♠♣❧❡ s♣❛❝❡s ❢♦r t❤❡s❡ ❡①♣❡r✐♠❡♥ts✳ ❆ ♣r♦❜❛❜✐❧✐t② ♣r♦✈✐❞❡s ❛ q✉❛♥t✐t❛t✐✈❡ ❞❡s❝r✐♣t✐♦♥ ♦❢ t❤❡ ❧✐❦❡❧② ♦❝❝✉rr❡♥❝❡ ♦❢ ❛ ♣❛rt✐❝✉❧❛r ❡✈❡♥t✳ ❚❤❡ ♣r♦❜❛❜✐❧✐t② ♦❢ ❛♥ ❡✈❡♥t x✱ ❞❡♥♦t❡❞ ❛s p (x)✱ ✐s ❝♦♥✲ ✈❡♥t✐♦♥❛❧❧② ❡①♣r❡ss❡❞ ♦♥ ❛ s❝❛❧❡ ❢r♦♠ ✵ t♦ ✶✱ ✐♥❝❧✉s✐✈❡✳ ❊①❛♠♣❧❡ ■♥ t❤❡ s✐♥❣❧❡ ❞✐❡ ❡①♣❡r✐♠❡♥t✱ t❤❡ ♣r♦❜❛❜✐❧✐t② ♦❢ r♦❧❧✐♥❣ ❛ s✐① ✐s ✶✴✻✳ ❚❤❡r❡ ❛r❡ ✸✻ ♣♦ss✐❜❧❡ ❝♦♠❜✐♥❛t✐♦♥s ♦❢ ♥✉♠❜❡rs ✇❤❡♥ t✇♦ ❞✐❝❡ ❛r❡ r♦❧❧❡❞✳ ❚❤❡ s❛♠♣❧❡ ♣♦✐♥ts x ❛♥❞ y ❝♦♥s✐st✐♥❣ ♦❢ s✉♠s ♦❢ ✼ ❛♥❞ ✶✵ ❛r❡✱ r❡s♣❡❝t✐✈❡❧②✱ x = {(✶✱ ✻) ✱ (✷✱ ✺) ✱ (✸✱ ✹) ✱ (✹✱ ✸) ✱ (✺✱ ✷) ✱ (✻✱ ✶)} ❛♥❞ y = {(✹✱ ✻) ✱ (✺✱ ✺) ✱ (✻✱ ✹)}✳ ❍❡♥❝❡✱ ✇❡ ❤❛✈❡ p (x) = 6/36✱ p (y) = 3/36✳ ❢♦r t❤❡ t✇♦ ❡✈❡♥ts ❆s ❞❡✜♥❡❞ ❛❜♦✈❡✱ ❛♥ ❡✈❡♥t ❝♦♥s✐sts ♦❢ ❛ s✐♥❣❧❡ ♦✉t❝♦♠❡ ✐♥ t❤❡ s❛♠♣❧❡ s♣❛❝❡✳ s✐♠♣❧❡ ❡✈❡♥t ✭♦r ❡❧❡♠❡♥t❛r② ❝♦♠♣♦✉♥❞ ❡✈❡♥t ❛s ❛♥ ❡✈❡♥t t❤❛t ▲❡t ✉s ❣❡♥❡r❛❧✐③❡ t❤✐s ❞❡✜♥✐t✐♦♥ ❜② ❝❛❧❧✐♥❣ ✐t ❛ ❡✈❡♥t ♦r ❛t♦♠✐❝ ❡✈❡♥t ✮✱ ❛♥❞ ❜② ❞❡✜♥✐♥❣ ❛ ❝♦♥s✐sts ♦❢ ♠✉❧t✐♣❧❡ s✐♠♣❧❡ ❡✈❡♥ts✳ ■♥ ❣❡♥❡r❛❧✱ ❛♥ ❡✈❡♥t ✐s ❡✐t❤❡r ❛ s✐♠♣❧❡ ❡✈❡♥t ♦r ❛ ❝♦♠♣♦✉♥❞ ❡✈❡♥t✳ ❙❡t t❤❡♦r② ❝❛♥ ❜❡ ✉s❡❞ t♦ r❡♣r❡s❡♥t ✈❛r✐♦✉s r❡❧❛t✐♦♥s❤✐♣s ❛♠♦♥❣ ❡✈❡♥ts✳ ❋♦r ❡①❛♠♣❧❡✱ ✐❢ x ❛♥❞ y ❛r❡ t✇♦ ❡✈❡♥ts ✭✇❤✐❝❤ ♠❛② ❜❡ ❡✐t❤❡r s✐♠♣❧❡ ♦r ❝♦♠♣♦✉♥❞✮ ✐♥ t❤❡ s❛♠♣❧❡ s♣❛❝❡ • x∪y ♠❡❛♥s ❡✐t❤❡r • x∩y ✭♦r ①②✮ ♠❡❛♥s ❜♦t❤ • x⊆y • x ¯ x ♦r y S t❤❡♥✿ ♦❝❝✉rs ✭♦r ❜♦t❤ ♦❝❝✉r✮✳ x ❛♥❞ y ♦❝❝✉r✳ ♠❡❛♥s ✐❢ x ♦❝❝✉rs t❤❡♥ s♦ ❞♦❡s ♠❡❛♥s ❡✈❡♥t x ❞♦❡s ♥♦t ♦❝❝✉r ✭♦r ❡q✉✐✈❛❧❡♥t❧②✱ t❤❡ ❝♦♠♣❧❡♠❡♥t ♦❢ ♦❝❝✉rs✮✳ • Φ r❡♣r❡s❡♥ts ❛♥ ✐♠♣♦ss✐❜❧❡ ❡✈❡♥t✳ • S ✐s ❛♥ ❡✈❡♥t t❤❛t ✐s ❝❡rt❛✐♥ t♦ ♦❝❝✉r✳ y.

6, 6)}✱ {sunny, rain, snow}✱ ❛♥❞ {t : t ∈ [0♦ ❈, 100♦ ❈]} ❛r❡✱ r❡s♣❡❝t✐✈❡❧②✱ ❡①❛♠♣❧❡s ♦❢ s❛♠♣❧❡ s♣❛❝❡s ❢♦r t❤❡s❡ ❡①♣❡r✐♠❡♥ts✳ ❆ ♣r♦❜❛❜✐❧✐t② ♣r♦✈✐❞❡s ❛ q✉❛♥t✐t❛t✐✈❡ ❞❡s❝r✐♣t✐♦♥ ♦❢ t❤❡ ❧✐❦❡❧② ♦❝❝✉rr❡♥❝❡ ♦❢ ❛ ♣❛rt✐❝✉❧❛r ❡✈❡♥t✳ ❚❤❡ ♣r♦❜❛❜✐❧✐t② ♦❢ ❛♥ ❡✈❡♥t x✱ ❞❡♥♦t❡❞ ❛s p (x)✱ ✐s ❝♦♥✲ ✈❡♥t✐♦♥❛❧❧② ❡①♣r❡ss❡❞ ♦♥ ❛ s❝❛❧❡ ❢r♦♠ ✵ t♦ ✶✱ ✐♥❝❧✉s✐✈❡✳ ❊①❛♠♣❧❡ ■♥ t❤❡ s✐♥❣❧❡ ❞✐❡ ❡①♣❡r✐♠❡♥t✱ t❤❡ ♣r♦❜❛❜✐❧✐t② ♦❢ r♦❧❧✐♥❣ ❛ s✐① ✐s ✶✴✻✳ ❚❤❡r❡ ❛r❡ ✸✻ ♣♦ss✐❜❧❡ ❝♦♠❜✐♥❛t✐♦♥s ♦❢ ♥✉♠❜❡rs ✇❤❡♥ t✇♦ ❞✐❝❡ ❛r❡ r♦❧❧❡❞✳ ❚❤❡ s❛♠♣❧❡ ♣♦✐♥ts x ❛♥❞ y ❝♦♥s✐st✐♥❣ ♦❢ s✉♠s ♦❢ ✼ ❛♥❞ ✶✵ ❛r❡✱ r❡s♣❡❝t✐✈❡❧②✱ x = {(✶✱ ✻) ✱ (✷✱ ✺) ✱ (✸✱ ✹) ✱ (✹✱ ✸) ✱ (✺✱ ✷) ✱ (✻✱ ✶)} ❛♥❞ y = {(✹✱ ✻) ✱ (✺✱ ✺) ✱ (✻✱ ✹)}✳ ❍❡♥❝❡✱ ✇❡ ❤❛✈❡ p (x) = 6/36✱ p (y) = 3/36✳ ❢♦r t❤❡ t✇♦ ❡✈❡♥ts ❆s ❞❡✜♥❡❞ ❛❜♦✈❡✱ ❛♥ ❡✈❡♥t ❝♦♥s✐sts ♦❢ ❛ s✐♥❣❧❡ ♦✉t❝♦♠❡ ✐♥ t❤❡ s❛♠♣❧❡ s♣❛❝❡✳ s✐♠♣❧❡ ❡✈❡♥t ✭♦r ❡❧❡♠❡♥t❛r② ❝♦♠♣♦✉♥❞ ❡✈❡♥t ❛s ❛♥ ❡✈❡♥t t❤❛t ▲❡t ✉s ❣❡♥❡r❛❧✐③❡ t❤✐s ❞❡✜♥✐t✐♦♥ ❜② ❝❛❧❧✐♥❣ ✐t ❛ ❡✈❡♥t ♦r ❛t♦♠✐❝ ❡✈❡♥t ✮✱ ❛♥❞ ❜② ❞❡✜♥✐♥❣ ❛ ❝♦♥s✐sts ♦❢ ♠✉❧t✐♣❧❡ s✐♠♣❧❡ ❡✈❡♥ts✳ ■♥ ❣❡♥❡r❛❧✱ ❛♥ ❡✈❡♥t ✐s ❡✐t❤❡r ❛ s✐♠♣❧❡ ❡✈❡♥t ♦r ❛ ❝♦♠♣♦✉♥❞ ❡✈❡♥t✳ ❙❡t t❤❡♦r② ❝❛♥ ❜❡ ✉s❡❞ t♦ r❡♣r❡s❡♥t ✈❛r✐♦✉s r❡❧❛t✐♦♥s❤✐♣s ❛♠♦♥❣ ❡✈❡♥ts✳ ❋♦r ❡①❛♠♣❧❡✱ ✐❢ x ❛♥❞ y ❛r❡ t✇♦ ❡✈❡♥ts ✭✇❤✐❝❤ ♠❛② ❜❡ ❡✐t❤❡r s✐♠♣❧❡ ♦r ❝♦♠♣♦✉♥❞✮ ✐♥ t❤❡ s❛♠♣❧❡ s♣❛❝❡ • x∪y ♠❡❛♥s ❡✐t❤❡r • x∩y ✭♦r ①②✮ ♠❡❛♥s ❜♦t❤ • x⊆y • x ¯ x ♦r y S t❤❡♥✿ ♦❝❝✉rs ✭♦r ❜♦t❤ ♦❝❝✉r✮✳ x ❛♥❞ y ♦❝❝✉r✳ ♠❡❛♥s ✐❢ x ♦❝❝✉rs t❤❡♥ s♦ ❞♦❡s ♠❡❛♥s ❡✈❡♥t x ❞♦❡s ♥♦t ♦❝❝✉r ✭♦r ❡q✉✐✈❛❧❡♥t❧②✱ t❤❡ ❝♦♠♣❧❡♠❡♥t ♦❢ ♦❝❝✉rs✮✳ • Φ r❡♣r❡s❡♥ts ❛♥ ✐♠♣♦ss✐❜❧❡ ❡✈❡♥t✳ • S ✐s ❛♥ ❡✈❡♥t t❤❛t ✐s ❝❡rt❛✐♥ t♦ ♦❝❝✉r✳ y.

6, 6)}✱ {sunny, rain, snow}✱ ❛♥❞ {t : t ∈ [0♦ ❈, 100♦ ❈]} ❛r❡✱ r❡s♣❡❝t✐✈❡❧②✱ ❡①❛♠♣❧❡s ♦❢ s❛♠♣❧❡ s♣❛❝❡s ❢♦r t❤❡s❡ ❡①♣❡r✐♠❡♥ts✳ ❆ ♣r♦❜❛❜✐❧✐t② ♣r♦✈✐❞❡s ❛ q✉❛♥t✐t❛t✐✈❡ ❞❡s❝r✐♣t✐♦♥ ♦❢ t❤❡ ❧✐❦❡❧② ♦❝❝✉rr❡♥❝❡ ♦❢ ❛ ♣❛rt✐❝✉❧❛r ❡✈❡♥t✳ ❚❤❡ ♣r♦❜❛❜✐❧✐t② ♦❢ ❛♥ ❡✈❡♥t x✱ ❞❡♥♦t❡❞ ❛s p (x)✱ ✐s ❝♦♥✲ ✈❡♥t✐♦♥❛❧❧② ❡①♣r❡ss❡❞ ♦♥ ❛ s❝❛❧❡ ❢r♦♠ ✵ t♦ ✶✱ ✐♥❝❧✉s✐✈❡✳ ❊①❛♠♣❧❡ ■♥ t❤❡ s✐♥❣❧❡ ❞✐❡ ❡①♣❡r✐♠❡♥t✱ t❤❡ ♣r♦❜❛❜✐❧✐t② ♦❢ r♦❧❧✐♥❣ ❛ s✐① ✐s ✶✴✻✳ ❚❤❡r❡ ❛r❡ ✸✻ ♣♦ss✐❜❧❡ ❝♦♠❜✐♥❛t✐♦♥s ♦❢ ♥✉♠❜❡rs ✇❤❡♥ t✇♦ ❞✐❝❡ ❛r❡ r♦❧❧❡❞✳ ❚❤❡ s❛♠♣❧❡ ♣♦✐♥ts x ❛♥❞ y ❝♦♥s✐st✐♥❣ ♦❢ s✉♠s ♦❢ ✼ ❛♥❞ ✶✵ ❛r❡✱ r❡s♣❡❝t✐✈❡❧②✱ x = {(✶✱ ✻) ✱ (✷✱ ✺) ✱ (✸✱ ✹) ✱ (✹✱ ✸) ✱ (✺✱ ✷) ✱ (✻✱ ✶)} ❛♥❞ y = {(✹✱ ✻) ✱ (✺✱ ✺) ✱ (✻✱ ✹)}✳ ❍❡♥❝❡✱ ✇❡ ❤❛✈❡ p (x) = 6/36✱ p (y) = 3/36✳ ❢♦r t❤❡ t✇♦ ❡✈❡♥ts ❆s ❞❡✜♥❡❞ ❛❜♦✈❡✱ ❛♥ ❡✈❡♥t ❝♦♥s✐sts ♦❢ ❛ s✐♥❣❧❡ ♦✉t❝♦♠❡ ✐♥ t❤❡ s❛♠♣❧❡ s♣❛❝❡✳ s✐♠♣❧❡ ❡✈❡♥t ✭♦r ❡❧❡♠❡♥t❛r② ❝♦♠♣♦✉♥❞ ❡✈❡♥t ❛s ❛♥ ❡✈❡♥t t❤❛t ▲❡t ✉s ❣❡♥❡r❛❧✐③❡ t❤✐s ❞❡✜♥✐t✐♦♥ ❜② ❝❛❧❧✐♥❣ ✐t ❛ ❡✈❡♥t ♦r ❛t♦♠✐❝ ❡✈❡♥t ✮✱ ❛♥❞ ❜② ❞❡✜♥✐♥❣ ❛ ❝♦♥s✐sts ♦❢ ♠✉❧t✐♣❧❡ s✐♠♣❧❡ ❡✈❡♥ts✳ ■♥ ❣❡♥❡r❛❧✱ ❛♥ ❡✈❡♥t ✐s ❡✐t❤❡r ❛ s✐♠♣❧❡ ❡✈❡♥t ♦r ❛ ❝♦♠♣♦✉♥❞ ❡✈❡♥t✳ ❙❡t t❤❡♦r② ❝❛♥ ❜❡ ✉s❡❞ t♦ r❡♣r❡s❡♥t ✈❛r✐♦✉s r❡❧❛t✐♦♥s❤✐♣s ❛♠♦♥❣ ❡✈❡♥ts✳ ❋♦r ❡①❛♠♣❧❡✱ ✐❢ x ❛♥❞ y ❛r❡ t✇♦ ❡✈❡♥ts ✭✇❤✐❝❤ ♠❛② ❜❡ ❡✐t❤❡r s✐♠♣❧❡ ♦r ❝♦♠♣♦✉♥❞✮ ✐♥ t❤❡ s❛♠♣❧❡ s♣❛❝❡ • x∪y ♠❡❛♥s ❡✐t❤❡r • x∩y ✭♦r ①②✮ ♠❡❛♥s ❜♦t❤ • x⊆y • x ¯ x ♦r y S t❤❡♥✿ ♦❝❝✉rs ✭♦r ❜♦t❤ ♦❝❝✉r✮✳ x ❛♥❞ y ♦❝❝✉r✳ ♠❡❛♥s ✐❢ x ♦❝❝✉rs t❤❡♥ s♦ ❞♦❡s ♠❡❛♥s ❡✈❡♥t x ❞♦❡s ♥♦t ♦❝❝✉r ✭♦r ❡q✉✐✈❛❧❡♥t❧②✱ t❤❡ ❝♦♠♣❧❡♠❡♥t ♦❢ ♦❝❝✉rs✮✳ • Φ r❡♣r❡s❡♥ts ❛♥ ✐♠♣♦ss✐❜❧❡ ❡✈❡♥t✳ • S ✐s ❛♥ ❡✈❡♥t t❤❛t ✐s ❝❡rt❛✐♥ t♦ ♦❝❝✉r✳ y.

Download PDF sample

Rated 4.12 of 5 – based on 50 votes