Statsmodels: statistical modeling and econometrics in Python
- python
- statistics
- econometrics
- data-analysis
Repository Index
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Lightning ⚡️ fast forecasting with statistical and econometric models.
- time-series
- statistics
- forecasting
- arima
Python Toolkit for Causal and Probabilistic Reasoning
- python
- bayesian-networks
- causal-inference
- causal-discovery
A library for debugging/inspecting machine learning classifiers and explaining their predictions
Python library for time series forecasting using scikit-learn compatible models, statistical methods, and foundation models
- forecasting
- scikit-learn
- machine-learning
- python
moDel Agnostic Language for Exploration and eXplanation
- machine-learning
- interpretability
- data-science
- xai
Visualize large time series data with plotly.py
🍇 GRAPE is a Rust/Python Graph Representation Learning library for Predictions and Evaluations
- graph
- high-performance
- machine-learning
- knowledge-graph
ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission (CHIL 2020 Workshop)
- clinicalbert
- hospital-readmission
- prediction
TranslatedTensorFlow in practice: using LSTM to predict lottery results
Genomic Pretrained Network - GPN, GPN-MSA, PhyloGPN, GPN-Star
- dna
- language-model
- variant-effect-prediction
- genomics
A unified framework for tabular probabilistic regression, time-to-event prediction, and probability distributions in python
- python
- sklearn
- data-science
- framework
MolTrans: Molecular Interaction Transformer for Drug Target Interaction Prediction (Bioinformatics)
This is the code for "Bitcoin Prediction" by Siraj Raval on Youtube
Cancer Imaging Phenomics Toolkit (CaPTk) is a software platform to perform image analysis and predictive modeling tasks. Documentation: https://cbica.github.io/CaPTk
- cancer-imaging-research
- radiomics
- radiomics-features
- machine-learning
Code for "Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution", NeurIPS 2022.
- disentanglement
- drug-discovery
- genomics
- perturbation
A collection of tasks to probe the effectiveness of protein sequence representations in modeling aspects of protein design
- protein
- protein-sequences
- protein-design
- representation-learning
PRnet is a flexible and scalable perturbation-conditioned generative model predicting transcriptional responses to unseen complex perturbations at bulk and single-cell levels.
Data and code for "When (ish) is My Bus? User-centered Visualizations of Uncertainty in Everyday, Mobile Predictive Systems" (CHI 2016)
Stock price prediction using a deterministic algorithm inspired by LSTM, focusing on pattern recognition in historical data.
Interpretable drug response prediction with graph neural networks over drugs, cell lines, and genes.
Comparing the performance of Stacked Ensemble Learning & machine learning algorithms like Random Forest, Decision Tree, Adaboost, Gradient Boost and XGBoost Classifier in Python for Stock Market Trend Prediction.