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Explainable-AI
  • Explainable AI
  • Preface
  • How to use this book?
  • Contents
  • What is Explainable AI?
  • Why do we need Explainablity?
  • Explainable systems and Black box systems
  • Types of Explainability Techniques
  • Explainable Models
    • Linear Regression
      • Assumptions
      • Model
      • Statistical Interpretation
    • Decision Trees
      • How Do They Work?
      • Creating the model
      • Interpretation
  • Explainability Techniques for Classical ML
    • SHAP (SHapley Additive exPlanations)
    • Surrogate model
    • LIME (Local Interpretable Model-Agnostic Explanations)
    • PDP (Partial Dependence Plot)
    • ICE (Individual Conditional Expectation Plots)
    • ALE (Accumulated Local Effects Plot)
  • Datasets
    • Medical Cost Personal Dataset
    • Telecom Churn Dataset
    • Sales Opportunity Size Dataset
    • Pima Indians Diabetes Dataset
  • Implementation of these techniques on different models
    • Logistic Regression - SHAP
    • Random Forest - LIME
    • GBM - PDP
    • GBM - ICE
    • Deep Learning - Surrogate
  • Future scope
  • Contributors
  • Citing this Book
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  1. Datasets

Sales Opportunity Size Dataset

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Last updated 4 years ago

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Description

The Sales Opportunity gives the probability of becoming a customer. This probability is very important to target them based on how big the deal will be and what value they add. The sales data contains information about customers and their transactions and the deal size.

Data Set Characteristics: Classification - Multivariate

Number of Instances: 2623

Features

  • ORDER NUMBER

  • QUANTITY ORDERED

  • PRICE EACH

  • ORDER LINE NUMBER

  • SALES

  • ORDER DATE

  • STATUS

  • QTR_ID

  • MONTH_ID

  • YEAR_ID

  • PRODUCT LINE

  • MSRP

  • PRODUCT CODE

  • CUSTOMER NAME

  • PHONE

  • ADDRESS LINE1

  • ADDRESS LINE2

  • CITY

  • STATE

  • POSTAL CODE

  • COUNTRY

  • TERRITORY

  • CONTACT LAST NAME

  • CONTACTFIRSTNAME

  • DEAL SIZE

Acknowledgments

The dataset was provided by and it is available .

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