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Assignment 4 - Predictive Data Analytics with Python

Explain association rules with example.

Association Rules: Summary

Definition:

  • Association rules are used in data mining to discover relationships between variables in databases, expressed as "if-then" statements.

Components:

  1. Antecedent: Condition that must be satisfied.
  2. Consequent: Outcome if the antecedent is met.

Metrics:

  • Support: Frequency of the itemset in the dataset.
  • Confidence: Likelihood of the consequent given the antecedent.
  • Lift: Indicates strength of association (greater than 1 suggests a strong relationship).

Example:

  • Rule: Diapers → Milk
  • Support: 60% (3 out of 5 transactions).
  • Confidence: 75% (Milk bought in 3 out of 4 Diaper transactions).
  • Lift: Measures how much more often Diapers and Milk co-occur than if independent.

These elements highlight how association rules help in understanding item relationships within transactional datasets, crucial for retail insights.

Explain predictive, descriptive, prescriptive data analysis. And also mention their difference.

Certainly! Here’s a concise, pointwise summary for each type of data analysis:

Descriptive Data Analysis

  • Purpose: Summarize historical data to understand what has happened.
  • Techniques:
    • Statistical summaries: Mean, median, mode.
    • Visualizations: Charts and graphs.
    • Data aggregation: Combine data from multiple sources.
  • Focus: Provide insights into past performances and trends.

Predictive Data Analysis

  • Purpose: Forecast future events using historical data.
  • Techniques:
    • Regression models: Predict continuous outcomes.
    • Classification models: Predict categorical outcomes.
  • Focus: Make informed predictions to enable proactive decisions.

Prescriptive Data Analysis

  • Purpose: Recommend actions based on predictive insights.
  • Techniques:
    • Optimization: Find best outcomes using mathematical models.
    • Simulation: Explore potential outcomes under various scenarios.
  • Focus: Suggest actionable strategies to achieve optimal results.

This format efficiently captures the essence and applications of each type of data analysis, tailored for quick understanding and point-wise exam responses.

Explain use of logistic function in logistic regression in detail. List and explain types of logistic regression.

Logistic Function in Logistic Regression

  • Definition: The logistic function, $\sigma(z) = \frac{1}{1 + e^{-z}}$, maps any real-valued number to the (0, 1) interval, ideal for modeling probabilities.
  • Characteristics: It has an S-shaped curve, outputs between 0 and 1, and its derivative, useful in optimization, peaks at ( z = 0 ).

Types of Logistic Regression

  1. Binary Logistic Regression:
    • Predicts one of two classes, using the logistic function to model probabilities of the default class.
  2. Multinomial Logistic Regression:
    • Extends to multiple classes, using a generalization of the logistic function.
  3. Ordinal Logistic Regression:
    • Handles naturally ordered categories, modeling thresholds to reflect varying intervals.