PhyLab Statistics 📊📈

This is a set of algorithms for data analysis written mostly in Python, but also including R and C. The purpose of this compendium is to provide the GitHub scientific community, be they physicists, chemists, engineers, etc., code that can facilitate their work in laboratories and even simple investigations. Little by little the volume of the content of this repository will be increased, starting with pure regression and distributions for data measured or obtained in a laboratory.

NOTE: It is planned to add deep learning algorithms and ANN or RNN, as well as code for deep symbolic regression for physics research.

Tech 💻

Algorithms 🔬

• Statistical Regression
  1. Simple Linear Regression
  2. Multiple Linear Regression
  3. Polynomial Linear Regression
  4. Support Vector for Regression (SVR)
  5. Decision Tree
  6. Random Forest
• Data Classification
  1. Logistic Regression
  2. K Nearest Neighbors (K-NN)
  3. Support Vector Machine (SVM)
  4. Kernel SVM
  5. Naive Bayes
  6. Decision Tree Classification
  7. Random Forest Classification
• Statistical Data Distribution
  1. Gaussian or Normal Distribution
  2. Poisson Distribution
  3. Binomial Distribution
• Statistical Analysis
  1. R Squared Factor
  2. Reduced R Squared Factor
  3. Variance
  4. Standard Deviation
• Deep Learning
• Artificial Neural Networks
• Deep Symbolic Regression for Physics [Tenachi et all 2023]

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