DS-U4-MM

Creating a mind map can help visually organize the topics and subtopics of Unit 4: Predictive Data Analytics with Python, making it easier to recall and understand the concepts. Here’s a detailed description of how to structure your mind map for this unit:

Central Node: Predictive Data Analytics with Python

Branch 1: Essential Python Libraries

• NumPy
  • Numerical operations
  • Array and matrix support
• Pandas
  • Data manipulation
  • DataFrame operations
• Matplotlib and Seaborn
  • Data visualization
  • Statistical graphics
• Scikit-learn
  • Machine learning algorithms
  • Built on NumPy, SciPy, Matplotlib

Branch 2: Basic Examples

• Loading Data
  • Using pd.read_csv()
• Data Operations
  • Filtering
  • Sorting
  • Grouping

Branch 3: Data Preprocessing

• Removing Duplicates
  • drop_duplicates()
• Transformation Using Functions or Mapping
  • apply()
  • Lambda functions
• Replacing Values
  • replace()
• Handling Missing Values
  • Scikit-learn SimpleImputer
    • Strategies: mean, median, most_frequent, constant

Branch 4: Types of Data Analytics

• Predictive Analytics
  • Forecasting future events
  • Techniques: regression, classification, time series analysis
• Descriptive Analytics
  • Summarizing past data
  • Techniques: data aggregation, data mining
• Prescriptive Analytics
  • Recommending actions
  • Techniques: optimization, simulation

Branch 5: Key Algorithms

• Association Rule Learning
  • Apriori Algorithm
    • Frequent itemsets, association rules
  • FP-Growth Algorithm
    • Efficient itemset mining
• Regression Analysis
  • Linear Regression
    • Relationship between variables
  • Logistic Regression
    • Binary classification
• Classification Algorithms
  • Naive Bayes
    • Based on Bayes' theorem
  • Decision Trees
    • Tree-like model for decisions

Branch 6: Introduction to Scikit-learn

• Installation
  • pip install scikit-learn
• Dataset
  • Loading Iris dataset
  • load_iris()
• Math Library
  • Utilizes NumPy
• Filling Missing Values
  • Using SimpleImputer
• Regression and Classification
  • Implementing algorithms
  • LogisticRegression, DecisionTreeClassifier

Example Mind Map Structure

1. Central Node: Predictive Data Analytics with Python
   • Essential Python Libraries
     • NumPy: Numerical operations, array/matrix support
     • Pandas: Data manipulation, DataFrame operations
     • Matplotlib/Seaborn: Data visualization, statistical graphics
     • Scikit-learn: Machine learning algorithms, built on NumPy/SciPy/Matplotlib
   • Basic Examples
     • Loading Data: Using pd.read_csv()
     • Data Operations: Filtering, sorting, grouping
   • Data Preprocessing
     • Removing Duplicates: drop_duplicates()
     • Transformation: apply(), lambda functions
     • Replacing Values: replace()
     • Handling Missing Values: SimpleImputer
   • Types of Data Analytics
     • Predictive: Forecasting, regression, classification, time series analysis
     • Descriptive: Summarizing past data, data aggregation, data mining
     • Prescriptive: Recommending actions, optimization, simulation
   • Key Algorithms
     • Association Rule Learning: Apriori, FP-Growth
     • Regression: Linear, Logistic
     • Classification: Naive Bayes, Decision Trees
   • Introduction to Scikit-learn
     • Installation: pip install scikit-learn
     • Dataset: Loading Iris dataset, load_iris()
     • Math Library: Utilizes NumPy
     • Filling Missing Values: SimpleImputer
     • Regression and Classification: Implementing algorithms

Visualization

You can visualize this mind map using mind mapping tools like XMind, MindMeister, or even on paper. Here’s a basic textual layout that you can expand into a full visual mind map:

- Predictive Data Analytics with Python
  - Essential Python Libraries
    - NumPy
    - Pandas
    - Matplotlib/Seaborn
    - Scikit-learn
  - Basic Examples
    - Loading Data
    - Data Operations
  - Data Preprocessing
    - Removing Duplicates
    - Transformation
    - Replacing Values
    - Handling Missing Values
  - Types of Data Analytics
    - Predictive
    - Descriptive
    - Prescriptive
  - Key Algorithms
    - Association Rule Learning
      - Apriori
      - FP-Growth
    - Regression
      - Linear
      - Logistic
    - Classification
      - Naive Bayes
      - Decision Trees
  - Introduction to Scikit-learn
    - Installation
    - Dataset
    - Math Library
    - Filling Missing Values
    - Regression and Classification

This mind map structure helps in recalling and understanding the interconnected concepts within Unit 4, providing a visual representation that makes it easier to memorize and comprehend the material.