MM - Introduction to Scikit-learn, Installations, Dataset

Sure, creating a mind map with keywords and short sentences can help you recall the key concepts of Unit 4 effectively. Here’s a structured list of keywords and short sentences for each topic within the unit:

Predictive Data Analytics with Python

Introduction

• Predictive Analytics: Uses historical data to predict future events.
• Importance: Key in decision-making, risk management, and strategic planning.

Essential Python Libraries

• NumPy: Numerical computing, arrays, matrices.
• Pandas: Data manipulation, DataFrame.
• Matplotlib & Seaborn: Data visualization.
• Scikit-learn: Machine learning, data mining, data analysis.

Basic Examples

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

Data Preprocessing

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

Types of Data Analytics

• Predictive: Predict future outcomes.
• Descriptive: Summarize past data.
• Prescriptive: Recommend actions.

Key Algorithms

• Association Rule Learning:
  • Apriori: Frequent itemsets, association rules.
  • FP-Growth: Efficient frequent itemsets.
• Regression Analysis:
  • Linear Regression: Relationship between variables.
  • Logistic Regression: Binary classification.
• Classification Algorithms:
  • Naive Bayes: Based on Bayes' theorem, independence assumption.
  • Decision Trees: Tree-like model of decisions.

Introduction to Scikit-learn

• Installation: pip install scikit-learn.
• Dataset: Built-in datasets, e.g., Iris, Digits.
• Math Library: NumPy integration.
• Filling Missing Values: SimpleImputer.
• Regression & Classification: Use Scikit-learn API.

Example Keywords and Short Sentences for a Mind Map:

Central Node: Predictive Data Analytics with Python

1. Introduction

   • Predictive Analytics: Predict future
   • Importance: Decision-making, risk management

2. Essential Python Libraries

   • NumPy: Numerical arrays, matrices
   • Pandas: DataFrame, data manipulation
   • Matplotlib & Seaborn: Visualization
   • Scikit-learn: ML, data analysis

3. Basic Examples

   • Reading Data: Pandas CSV
   • Data Operations: Filter, sort, group

4. Data Preprocessing

   • Remove Duplicates: Unique data
   • Transformation: Functions, mapping
   • Replace Values: Substitute values
   • Handle Missing Values: Fill, drop

5. Types of Data Analytics

   • Predictive: Future outcomes
   • Descriptive: Summarize past
   • Prescriptive: Recommend actions

6. Key Algorithms

   • Association Rules: Apriori, FP-Growth
   • Regression: Linear, Logistic
   • Classification: Naive Bayes, Decision Trees

7. Scikit-learn

   • Installation: pip install
   • Dataset: Iris, Digits
   • Math Library: NumPy
   • Fill Missing Values: SimpleImputer
   • Regression & Classification: API

Visualizing the Mind Map

Here's a textual representation of how you could structure your mind map:

                                      [Predictive Data Analytics with Python]
                                              /               |                \
                  [Introduction]       [Essential Libraries]       [Basic Examples]
                /           \              /              \             /                   \
        Predictive   Importance  NumPy   Pandas  Matplotlib & Seaborn  Reading Data      Data Operations
       Analytics     Decision-making, Numerical  DataFrame,    Visualization           Filter, sort, group
                          risk mgmt   arrays      manipulation

       [Data Preprocessing]     [Types of Data Analytics]      [Key Algorithms]      [Scikit-learn]
              /     |     \                      /        |         \                       /       |       \            \
  Remove   Transformation  Replace      Predictive  Descriptive  Prescriptive      Installation  Dataset  Math Library  Fill Missing    Regression
Duplicates   Functions,   Substitute    Future       Summarize      Actions        `pip install`   Iris     NumPy      Values          Linear
Unique data  mapping        values        outcomes       past data                               Digits                   `SimpleImputer` Logistic
                                                                                                                                                  Classification
                                                                                                                                                  Naive Bayes
                                                                                                                                                  Decision Trees

This structure helps break down the unit into key components and subcomponents, making it easier to study and recall each part. You can draw this mind map using various mind mapping tools or even on paper for better visualization.