DS-U5-MM

To create a structured mind map for Unit 5: Data Analytics and Model Evaluation, we will organize the topics, sub-topics, and key concepts systematically. This mind map will serve as a visual aid to recall and understand the intricate details of the unit.

Main Topic: Data Analytics and Model Evaluation

1. Clustering Algorithms

• K-Means Clustering
  • Keywords: Centroids, Euclidean Distance, Iteration, Inertia
  • Key Concepts:
    • Partitioning data into K clusters
    • Minimizing within-cluster variance
• Hierarchical Clustering
  • Keywords: Dendrogram, Agglomerative, Divisive, Linkage Methods
  • Key Concepts:
    • Creating a hierarchy of clusters
    • Merging or splitting clusters
• Time-Series Clustering
  • Keywords: Temporal Patterns, Distance Measures, Dynamic Time Warping
  • Key Concepts:
    • Grouping time-series data based on similarity
    • Identifying trends and patterns over time

2. Introduction to Text Analysis

• Text-Preprocessing
  • Keywords: Tokenization, StemmingStemmingOver stemming Under Stemming, LemmatisationLemmatisationStemming with meaning, Stop Words
  • Key Concepts:
    • Preparing raw text for analysis
    • Cleaning and normalizing text data
• Bag of Words (BoW)
  • Keywords: Word Frequency, Vectorisation, Sparsity
  • Key Concepts:
    • Representing text data as a collection of word counts
    • Simple and effective text representation
• TF-IDF (Term Frequency-Inverse Document Frequency)
  • Keywords: Weighting, Importance, Document Frequency
  • Key Concepts:
    • Measuring the importance of words in documents
    • Balancing term frequency and inverse document frequency
• Topic Modeling
  • Keywords: Latent Dirichlet Allocation (LDA), Topics, Distributions
  • Key Concepts:
    • Discovering hidden topics within a text corpus
    • Understanding themes and subjects in text data

3. Social Network Analysis

• Introduction
  • Keywords: Nodes, Edges, Graph Theory, Centrality
  • Key Concepts:
    • Analyzing the structure of social networks
    • Understanding relationships and influences
• Applications
  • Keywords: Marketing, Sociology, Information Dissemination
  • Key Concepts:
    • Practical uses in various domains
    • Identifying key influencers and communities

4. Business Analysis

• Introduction
  • Keywords: Data-Driven Decisions, Strategy, Competitive Advantage
  • Key Concepts:
    • Using data analytics for business insights
    • Enhancing decision-making and strategic planning
• Use Cases
  • Keywords: Customer Insights, Market Trends, Operational Efficiency
  • Key Concepts:
    • Real-world examples of data analytics in business
    • Leveraging data for business growth

5. Model Evaluation and Selection

• Metrics for Evaluating Classifier Performance
  • Keywords: Accuracy, Precision, Recall, F1-Score
  • Key Concepts:
    • Assessing the effectiveness of classification models
    • Understanding trade-offs between different metrics
• Holdout MethodHoldout MethodOptimising eh To divide training and test data optimisingly There will be and Random SubsamplingRandom SubsamplingIterations of Holdout Method, and then shuffle (randomise) and then shuffle Which give the minimum error will be the minimum output
  • Keywords: Training Set, Test Set, Validation
  • Key Concepts:
    • Techniques for model validation
    • Ensuring reliable model performance estimates
• Parameter Tuning and Optimization
  • Keywords: Hyperparameters, Grid Search, Cross-Validation
  • Key Concepts:
    • Fine-tuning model parameters for optimal performance
    • Systematic search for the best parameter values
• Result Interpretation
  • Keywords: Confusion Matrix, True Positives, False Negatives
  • Key Concepts:
    • Interpreting model outputs and performance metrics
    • Making informed decisions based on results

6. Practical Implementation

• Clustering and Time-Series Analysis with Scikit-learn
  • Keywords: Scikit-learn, K-MeansK-MeansK-Means Clustering is an Unsupervised Machine Learning algorithm, which groups the unlabeled dataset into different clusters. The article aims to explore the fundamentals and working of k mean clustering along with the implementation. What is K-means Clustering? Unsupervised Machine Learning is the process of teaching a computer to use unlabeled, unclassified data and enabling the algorithm to operate on that data without supervision. Without any previous data training, the machine’s job in th, Time-Series
  • Key Concepts:
    • Using Python library for practical data analysis
    • Implementing clustering algorithms
• Evaluation Tools in Sklearn.metrics
  • Keywords: Confusion Matrix, AUC-ROCAUC-ROCGoogle Developer, Elbow Plot
  • Key Concepts:
    • Tools for model evaluation and selection
    • Visualizing and interpreting evaluation metrics

Here’s a basic structure for your mind map:

Data Analytics and Model Evaluation
├── Clustering Algorithms
│   ├── K-Means Clustering
│   │   ├── Centroids
│   │   ├── Euclidean Distance
│   │   ├── Iteration
│   │   └── Inertia
│   ├── Hierarchical Clustering
│   │   ├── Dendrogram
│   │   ├── Agglomerative
│   │   ├── Divisive
│   │   └── Linkage Methods
│   └── Time-Series Clustering
│       ├── Temporal Patterns
│       ├── Distance Measures
│       └── Dynamic Time Warping
├── Introduction to Text Analysis
│   ├── Text-Preprocessing
│   │   ├── Tokenization
│   │   ├── Stemming
│   │   ├── Lemmatization
│   │   └── Stop Words
│   ├── Bag of Words (BoW)
│   │   ├── Word Frequency
│   │   ├── Vectorization
│   │   └── Sparsity
│   ├── TF-IDF
│   │   ├── Weighting
│   │   ├── Importance
│   │   └── Document Frequency
│   └── Topic Modeling
│       ├── Latent Dirichlet Allocation (LDA)
│       ├── Topics
│       └── Distributions
├── Social Network Analysis
│   ├── Introduction
│   │   ├── Nodes
│   │   ├── Edges
│   │   ├── Graph Theory
│   │   └── Centrality
│   └── Applications
│       ├── Marketing
│       ├── Sociology
│       └── Information Dissemination
├── Business Analysis
│   ├── Introduction
│   │   ├── Data-Driven Decisions
│   │   ├── Strategy
│   │   └── Competitive Advantage
│   └── Use Cases
│       ├── Customer Insights
│       ├── Market Trends
│       └── Operational Efficiency
├── Model Evaluation and Selection
│   ├── Metrics for Evaluating Classifier Performance
│   │   ├── Accuracy
│   │   ├── Precision
│   │   ├── Recall
│   │   └── F1-Score
│   ├── Holdout Method and Random Subsampling
│   │   ├── Training Set
│   │   ├── Test Set
│   │   └── Validation
│   ├── Parameter Tuning and Optimization
│   │   ├── Hyperparameters
│   │   ├── Grid Search
│   │   └── Cross-Validation
│   └── Result Interpretation
│       ├── Confusion Matrix
│       ├── True Positives
│       └── False Negatives
└── Practical Implementation
    ├── Clustering and Time-Series Analysis with Scikit-learn
    │   ├── Scikit-learn
    │   ├── K-Means
    │   └── Time-Series
    └── Evaluation Tools in Sklearn.metrics
        ├── Confusion Matrix
        ├── AUC-ROC
        └── Elbow Plot

This mind map structure organizes the topics and sub-topics in a way that highlights the key concepts and keywords, making it easier to recall and understand each component of Unit 5.