AIPrimer.AI
  • 🚦AI Primer In Transportation
  • CHAPTER 1 - INTRODUCTION TO MACHINE LEARNING
    • Machine Learning in Transportation
    • What is Machine Learning?
    • Types of Machine Learning
      • Supervised Learning
      • Unsupervised Learning
      • Semi-supervised Learning
      • Reinforced Learning
    • Fundamental concepts of machine learning
      • Model Training and Testing
      • Evaluating the Model’s Prediction Accuracy
      • The Underfitting and Overfitting Problems
      • Bias-Variance Tradeoff in Overfitting
      • Model Validation Techniques
      • Hyperparameter Tuning
      • Model Regularization
      • The Curse of Ddimensionality
    • Machine Learning versus Statistics
  • CHAPTER 2 - SUPERVISED METHODS
    • Supervised Learning_Complete Draft
    • K-Nearest Neighbor (KNN) Algorithm
    • Tree-Based Methods
    • Boosting
    • Support Vector Machines (SVMs)
  • CHAPTER 3 - UNSUPERVISED LEARNING
    • Principal Component Analysis
      • How Does It Work?
      • Interpretation of PCA result
      • Applications in Transportation
    • CLUSTERING
      • K-MEANS
      • SPECTRAL CLUSTERING
      • Hierarchical Clustering
    • REFERENCE
  • CHAPTER 4 - NEURAL NETWORK
    • The Basic Paradigm: Multilayer Perceptron
    • Regression and Classification Problems with Neural Networks
    • Advanced Topologies
      • Modular Network
      • Coactive Neuro–Fuzzy Inference System
      • Recurrent Neural Networks
      • Jordan-Elman Network
      • Time-Lagged Feed-Forward Network
      • Deep Neural Networks
  • CHAPTER 5 - DEEP LEARNING
    • Convolutional Neural Networks
      • Introduction
      • Convolution Operation
      • Typical Layer Structure
      • Parameters and Hyperparameters
      • Summary of Key Features
      • Training of CNN
      • Transfer Learning
    • Recurrent Neural Networks
      • Introduction
      • Long Short-Term Memory Neural Network
      • Application in transportation
    • Recent Development
      • AlexNet, ZFNet, VggNet, and GoogLeNet
      • ResNet
      • U-Net: Full Convolutional Network
      • R-CNN, Fast R-CNN, and Faster R-CNN
      • Mask R-CNN
      • SSD and YOLO
      • RetinaNet
      • MobileNets
      • Deformable Convolution Networks
      • CenterNet
      • Exemplar Applications in Transportation
    • Reference
  • CHAPTER 6 - REINFORCEMENT LEARNING
    • Introduction
    • Reinforcement Learning Algorithms
    • Model-free v.s. Model-based Reinforcement Learning
    • Applications of Reinforcement Learning to Transportation and Traffic Engineering
    • REFERENCE
  • CHAPTER 7 - IMPLEMENTING ML AND COMPUTATIONAL REQUIREMENTS
    • Data Pipeline for Machine Learning
      • Introduction
      • Problem Definition
      • Data Ingestion
      • Data Preparation
      • Data Segregation
      • Model Training
      • Model Deployment
      • Performance Monitoring
    • Implementation Tools: The Machine Learning Ecosystem
      • Machine Learning Framework
      • Data Ingestion tools
      • Databases
      • Programming Languages
      • Visualization Tools
    • Cloud Computing
      • Types and Services
    • High-Performance Computing
      • Deployment on-premise vs on-cloud
      • Case Study: Data-driven approach for the implementation of Variable Speed Limit
      • Conclusion
  • CHAPTER 8 - RESOURCES
    • Mathematics and Statistics
    • Programming, languages, and software
    • Machine learning environments
    • Tools of the Trade
    • Online Learning Sites
    • Key Math Concepts
  • REFERENCES
  • IMPROVEMENT BACKLOG
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  1. CHAPTER 7 - IMPLEMENTING ML AND COMPUTATIONAL REQUIREMENTS
  2. Implementation Tools: The Machine Learning Ecosystem

Visualization Tools

PreviousProgramming LanguagesNextCloud Computing

Last updated 1 year ago

Data Visualization is the technique of fitting data into different interactive visuals to derive a better understanding. With Machine Learning, understanding big data is a huge concern. Visualizing the data is useful to infer its properties. Software such as Tableau and Power BI are equipped with several functionalities to easily visualize big data using different chart types (Figure 5-12).

Figure 5-12 Data Visualization tools

  1. Tableau: Through data exploration and discovery, Tableau software quickly extracts insights from data and presents them in understandable formats. It doesn’t require excellent programming skills and can be easily installed on all kinds of devices. While a little script must be written, most operations are done by drag and drop.

  2. Power BI: It is a web-based service by Microsoft. This tool is flexible in handling multiple data sources such as SQL, Excel spreadsheets or cloud-based storage. It provides an easy user interface with simple drag and drop options to sort through and compare data. It provides a huge variety of visualizations to test with big data. It can easily integrate with other platforms such as R, Python or Database management softwares.

  3. Python Visualization: Libraries such a Matplotlib, Seaborn, Pandas and Plotly. Matplotlib is a low-level package whereas Pandas visualization built upon it offers better tools. Matplotlib can create neat 2D plots and also provides limited 3D graphic support. Seaborn can be used to create statistical graphics. Plotly offers functions that help create interactive plots.

  4. R Visualization: The ggplot library in R is a highly useful tool for visualizing data. It offers a variety of options such as Scatter plot, Histogram, Bar charts, Box plots, Area charts, Heat maps and Correlograms. This library is simple to use with several features to visualize data effectively.