Data analysis and further

Geospatial Analysis, Regression, and ML/DL

This page provides some basic knowledge to help you understand geospatial analysis, regression algorithms, and machine learning/deep learning.
Not an expert, just some guiding information for complete beginners!

Python Libraries

  • Geopandas – for geospatial data handling
  • Pandas & NumPy – for general data manipulation and numerical computing

Regression

  • Logistic Regression – for classification problems
  • Linear Regression – for predicting continuous values

Machine Learning / Deep Learning

  • Neural Networks – basic feedforward networks
  • Random Forest – ensemble method for classification and regression
  • Convolutional Neural Networks (CNN) – for image or spatial data
  • Regularisation – techniques to prevent overfitting
  • Workflow & Management – organizing projects and experiments
  • Large language Model - Chatgpt and how it works
  • Multimodal learning
Pelicanflow X DataLinear Regression and Gradient Descent