iKintosh's Stars
Asabeneh/30-Days-Of-Python
30 days of Python programming challenge is a step-by-step guide to learn the Python programming language in 30 days. This challenge may take more than100 days, follow your own pace. These videos may help too: https://www.youtube.com/channel/UC7PNRuno1rzYPb1xLa4yktw
blue-yonder/tsfresh
Automatic extraction of relevant features from time series:
uber/causalml
Uplift modeling and causal inference with machine learning algorithms
online-ml/river
🌊 Online machine learning in Python
awslabs/gluonts
Probabilistic time series modeling in Python
DistrictDataLabs/yellowbrick
Visual analysis and diagnostic tools to facilitate machine learning model selection.
Nixtla/neuralforecast
Scalable and user friendly neural :brain: forecasting algorithms.
microsoft/EconML
ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.
Alro10/deep-learning-time-series
List of papers, code and experiments using deep learning for time series forecasting
scikit-learn-contrib/lightning
Large-scale linear classification, regression and ranking in Python
deepcharles/ruptures
ruptures: change point detection in Python
winedarksea/AutoTS
Automated Time Series Forecasting
khundman/telemanom
A framework for using LSTMs to detect anomalies in multivariate time series data. Includes spacecraft anomaly data and experiments from the Mars Science Laboratory and SMAP missions.
Chanda-Abdul/Grokking-the-Coding-Interview-Patterns
This course categorizes coding interview problems into a set of 16 patterns. Each pattern will be a complete tool - consisting of data structures, algorithms, and analysis techniques - to solve a specific category of problems. The goal is to develop an understanding of the underlying pattern, so that, we can apply that pattern to solve other problems.
business-science/tidyquant
Bringing financial analysis to the tidyverse
psincraian/pepy
pepy is a site to get statistics information about any Python package.
AutoViML/Auto_TS
Automatically build ARIMA, SARIMAX, VAR, FB Prophet and XGBoost Models on Time Series data sets with a Single Line of Code. Created by Ram Seshadri. Collaborators welcome.
BiomedSciAI/causallib
A Python package for modular causal inference analysis and model evaluations
hildensia/bayesian_changepoint_detection
Methods to get the probability of a changepoint in a time series.
business-science/timetk
Time series analysis in the `tidyverse`
KDD-OpenSource/DeepADoTS
Repository of the paper "A Systematic Evaluation of Deep Anomaly Detection Methods for Time Series".
earthgecko/skyline
Anomaly detection
business-science/modeltime
Modeltime unlocks time series forecast models and machine learning in one framework
guilatrova/tryceratops
A linter to prevent exception handling antipatterns in Python (limited only for those who like dinosaurs).
microprediction/timemachines
Predict time-series with one line of code.
AnotherSamWilson/miceforest
Multiple Imputation with LightGBM in Python
waico/SKAB
SKAB - Skoltech Anomaly Benchmark. Time-series data for evaluating Anomaly Detection algorithms.
microprediction/microprediction
If you can measure it, consider it predicted
WenjieZ/TSCV
Time Series Cross-Validation -- an extension for scikit-learn
microprediction/m6
M6-Forecasting competition