kmoraza / periodic_table_analysis

Analysis of Modern Periodic Table using some Machine Learning techniques

Analysis of Modern Periodic Table using some Machine Learning techniques

• Phase Classification
• Metallicity Classification
• Chemical Family Classification
• Reactivity Classification
• Electrical Type Classification
• Hardness Classification
• Radioactivity Classification
• Space Group Classification
• Graph Properties Classification
• Lifetime Classification
• Linear Regression
• Multiple Linear Regression
• Polynomial Regression
• Ridge Regression
• Lasso Regression
• ElasticNet Regression
• Decision Tree Regression
• Random Forest Regression
• Gradient Boosting Regression
• Support Vector Regression
• K-Nearest Neighbors (KNN) Regression
• Bayesian Regression
• Neural Network Regression
• Quantile Regression
• Robust Regression
• Nonlinear Regression
• Generalized Additive Model
• Generalized Linear Model
• Tobit Regression
• Poisson Regression
• Negative Binomial Regression
• Zero-Inflated Regression
• Multilevel (or Hierarchial Regression)
• Bayesian Additive Regression Trees
• Gaussian Process Regression
• Functional Regression
• Instrumental Variables Regression
• Heteroscedasticity Regression
• Multivariate Regression
• K-Means Clustering
• Hierarchical Clustering
• Density-Based Spatial Clustering of Applications with Noise (DBSCAN)
• Mean Shift Clustering
• Gaussian Mixture Models (GMM)
• Spectral Clustering
• Affinity Propagation
• Fuzzy Clustering (Fuzzy C-Means)
• Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH)
• Self-Organizing Maps (SOM)
• Agglomerative Clustering
• Divisive Clustering
• Leader Clustering
• Clustering In Quest (CLIQUE)
• Principal Component Analysis (PCA)
• Linear Discriminant Analysis (LDA)
• Independent Component Analysis (ICA)
• Factor Analysis
• t-Distributed Stochastic Neighbor Embedding (t-SNE)
• Isomap
• Local Linear Embedding
• Hessian Eigenmaps
• Spectral Embedding
• Uniform Manifold Approximation and Projection (UMAP)
• Multidimensional Scaling (MDS)
• Sammon Mapping
• Deep Belief Network
• Restricted Boltzmann Machine
• Curvilinear Component Analysis
• Random Projection
• Quadratic Discriminant Analysis
• Deep Autoencoding Gaussian Mixture Model (DAGMM)
• Linear Autoencoder
• Nonlinear Autoencoder
• Deep Variational Information Bottleneck (DVIB)
• Principal Curves
• Self-Organizing Feature Maps (SOFM)
• Domain Adaptation
• Model Stacking
• Ensemble Learning
• Multi-task Learning
• Meta Learning
• Deep Belief Network (DBN)
• Transformer Network
• Capsule Network
• Temporal Convolutional Network (TCN)
• Deep Q-Network (DQN)
• Support Vector Machine (SVM)
• K-Nearest Neighbors (KNN)
• Gaussian Naive Bayes
• Multinomial Naive Bayes
• Bernoulli Naive Bayes
• Complement Naive Bayes
• Categorical Naive Bayes
• Hybrid Naive Bayes
• Averaged One-Dependence Estimator (AODE)
• Semi-Naive Bayes
• Weighted Naive Bayes