Computational Business Analytics by Subrata Das

By Subrata Das

Learn easy methods to appropriately Use the newest Analytics methods on your Organization

Computational company Analytics offers instruments and strategies for descriptive, predictive, and prescriptive analytics acceptable throughout a number of domain names. via many examples and difficult case stories from quite a few fields, practitioners simply see the connections to their very own difficulties and will then formulate their very own answer strategies.

The ebook first covers middle descriptive and inferential data for analytics. the writer then complements numerical statistical thoughts with symbolic man made intelligence (AI) and computing device studying (ML) suggestions for richer predictive and prescriptive analytics. With a unique emphasis on tools that deal with time and textual info, the text:

  • Enriches primary 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 options with the Kalman clear out, hidden Markov types, and dynamic Bayesian networks
  • Embeds choice timber inside of effect diagrams
  • Augments nearest-neighbor and k-means clustering options with aid vector machines and neural networks

These techniques aren't replacements of conventional statistics-based analytics; quite, in general, a generalized method could be lowered to the underlying conventional base process less than very restrictive stipulations. The e-book exhibits how those enriched ideas provide effective ideas in parts, together with shopper segmentation, churn prediction, credits chance evaluation, fraud detection, and advertisements campaigns.

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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.

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