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MM - Explain association rules with example.

Creating mind maps is an excellent way to visualize and recall complex information. Here are the key concepts and short sentences for each section of your unit on predictive data analytics with Python. These can be used as nodes and sub-nodes in your mind map.

Predictive Data Analytics with Python

1. Introduction

  • Predictive Analytics: Using historical data to predict future events.
  • Importance: Informed decision-making, risk management.

2. Essential Python Libraries

  • NumPy: Numerical operations.
  • Pandas: Data manipulation and analysis.
  • Matplotlib & Seaborn: Data visualization.
  • Scikit-learn: Machine learning tools.

3. Basic Examples

  • Reading Data: Using Pandas to read CSV files.
  • Data Operations: Filtering, sorting, grouping.

4. Data Preprocessing

  • Removing Duplicates: Ensure unique data points.
  • Transformation: Apply functions to modify data.
  • Replacing Values: Substitute specific values.
  • Handling Missing Values: Fill or drop missing data.

5. Types of Data Analytics

  • Predictive: Forecasting future events.
  • Descriptive: Summarizing past data.
  • Prescriptive: Recommending actions.

6. Key Algorithms

  • Association Rule Learning
    • Apriori Algorithm: Frequent itemsets, association rules.
    • FP-Growth Algorithm: Efficient frequent itemsets.
  • Regression Analysis
    • Linear Regression: Relationship modeling.
    • Logistic Regression: Binary classification.
  • Classification Algorithms
    • Naive Bayes: Probabilistic classifier.
    • Decision Trees: Tree-like decision model.

7. Introduction to Scikit-learn

  • Installation: pip install scikit-learn.
  • Dataset: Use built-in datasets.
  • Math Library: Utilizes NumPy.
  • Handling Missing Values: SimpleImputer.
  • Model Implementation: Regression and classification.

Association Rules

Key Concepts

  • Itemset: Collection of items.
  • Support: Frequency of itemset.
  • Confidence: Likelihood of Y given X.
  • Lift: Strength of association.

Example

  • Dataset: Transactions (bread, milk, etc.).
  • Support Calculation: Frequency in dataset.
  • Generate Rules: {bread} → {milk}.

Applications

  • Market Basket Analysis: Retail insights.
  • Recommender Systems: Product recommendations.
  • Fraud Detection: Identify unusual patterns.

Algorithms

  • Apriori: Bottom-up approach.
  • FP-Growth: Efficient frequent itemsets.

Python Implementation

  • Libraries: Pandas, mlxtend.
  • Steps: Generate frequent itemsets, association rules.

Use these keywords and short sentences as the main branches and sub-branches of your mind map. This structured format will help you recall the information more effectively.