Model Example

• Data Preprocessing
  • Data Schema
  • Loading Data
  • World Map Projection
  • Statistical Summary
  • Missing Value
  • Feature Engineering
• Classification
  • Table of Contents
  • 1. Train-Test Data Preparation
  • 2. Missing Value Processing
  • 3. Data Processing
  • 4. Model Selection
• Regression
  • 1. Introduction to Regression
  • 2. Introduction to Regression function of Geochemistry π
    • 2.1 Enter the sub-menu of Regression
    • 2.2 Generate a map projection
    • 2.3 Enter the range of data and check the output
    • 2.4 Use imputation techniques to deal with the missing values
    • 2.5 Feature Engineering
  • 3. Model Selection
• Clustering
  • Table of Contents
  • Data Preparation
    • Data Selection
    • Feature Engineering
  • Model Selection
    • Hyper-Parameters Specification
  • Data Results
    • 2 dimensions graphs of data
    • 3 dimensions graphs of data
• Decomposition
  • Table of Contents
  • 1. T-distributed Stochastic Neighbor Embedding (T-SNE)
  • 2. Preparation
  • 3. NAN value process
  • 4. Feature engineering
  • 5. Model Selection
  • 6. T-SNE
• Anomaly Detection
  • Table of Contents
  • 1. Train-Test Data Preparation
  • 2. Missing Value Processing
  • 3. Data Processing
  • 4. Model Selection
  • 5.Hyper-Parameters Specification
  • 6.Results
• Network Analysis