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statsmodels
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Master the essential skills needed to recognize and solve complex real-world problems with Machine Learning and Deep Learning by leveraging the highly popular Python Machine Learning Eco-system.
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natural-language-processing
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jupyter
notebook
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keras
jupyter-notebook
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spacy
nltk
classification
convolutional-neural-networks
prophet
statsmodels
time-series-analysis
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Oct 1, 2020 - Jupyter Notebook
50% faster, 50% less RAM Machine Learning. Numba rewritten Sklearn. SVD, NNMF, PCA, LinearReg, RidgeReg, Randomized, Truncated SVD/PCA, CSR Matrices all 50+% faster
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research
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gpu
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scikit-learn
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econometrics
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tensor
regression-models
statsmodels
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Sep 18, 2020 - Jupyter Notebook
General statistics, mathematical programming, and numerical/scientific computing scripts and notebooks in Python
python
data-science
machine-learning
statistics
analytics
clustering
numpy
probability
mathematics
pandas
scipy
matplotlib
inferential-statistics
hypothesis-testing
anova
statsmodels
bayesian-statistics
numerical-analysis
normal-distribution
mathematical-programming
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Oct 1, 2020 - Jupyter Notebook
r
apa
reporting
models
reports
rstats
bayesian
manuscript
statsmodels
automated-report-generation
easystats
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Sep 22, 2020 - R
Input Output Hidden Markov Model (IOHMM) in Python
python
machine-learning
time-series
scikit-learn
supervised-learning
semi-supervised-learning
sequence-to-sequence
graphical-models
unsupervised-learning
hidden-markov-model
statsmodels
linear-models
sequence-labeling
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Apr 1, 2020 - Python
A library that unifies the API for most commonly used libraries and modeling techniques for time-series forecasting in the Python ecosystem.
wrapper
data-science
time-series
sklearn
parallel
cross-validation
transformer
model-selection
statsmodels
wrapper-library
sklearn-compatible
fbprophet
sarimax
time-series-forecasting
sklearn-library
sklearn-api
pmdarima
tbats
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Sep 15, 2020 - Python
carlomazzaferro
commented
Mar 28, 2020
Is your proposed enhancement related to a problem? Please describe.
Add support and automated tests for python 3.5+, and for MacOS, Windows
Describe the solution you'd like
Add test matrix with tox
Describe alternatives you've considered
Tox, or any other solution that would run tests on travis ci for dfferent versions/platforms
Python port of "Common statistical tests are linear models" by Jonas Kristoffer Lindeløv.
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Jun 28, 2019 - HTML
Time Series Decomposition techniques and random forest algorithm on sales data
sales
sklearn
seaborn
machinelearning
statsmodels
datamining
time-series-analysis
regression-trees
sales-forecasting
time-series-decomposition
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Jun 6, 2019 - Jupyter Notebook
E-Commerce Website A/B testing: Recommend which of two landing pages to keep based on A/B testing
landing-page
p-value
e-commerce
logistic-regression
ab-testing
conversion-tracking
hypothesis-testing
statsmodels
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Dec 21, 2017 - HTML
Material for the tutorial, "Time series analysis with pandas" at T-Academy
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Mar 13, 2019 - Jupyter Notebook
Implemented an A/B Testing solution with the help of machine learning
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Oct 1, 2020 - Jupyter Notebook
Demonstration of alternatives to lme4
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Aug 12, 2019 - R
Python package for Scailable uploads
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Jun 19, 2020 - Python
Naive Bayesian, SVM, Random Forest Classifier, and Deeplearing (LSTM) on top of Keras and wod2vec TF-IDF were used respectively in SMS classification
nlp
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deep-learning
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svm
word2vec
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sms
keras
ml
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classification
gensim
tf-idf
statsmodels
spam-classification
lstm-neural-networks
gridsearchcv
sms-classification
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Nov 24, 2018 - Jupyter Notebook
In this course, teachers with different experiences in programming get an overview of the most relevant packages and tools available for Python, and learn how they can be applied in teaching and research.
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Aug 16, 2020 - Jupyter Notebook
This repo contains various data science strategy and machine learning models to deal with structure as well as unstructured data. It contains module on feature-preprocessing, feature-engineering, machine-learning-models, bayesian-parameter-tuning, etc, built using libraries such as scikit-learn, keras, h2o, xgboost, lightgbm, catboost, etc.
data-science
machine-learning
text-mining
h2o
feature-engineering
bayesian-optimization
statsmodels
ensemble-model
nan
entity-embedding
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Jul 1, 2020 - Python
Awesome cheatsheets for Data Science
python
machine-learning
time
deep-neural-networks
timeseries
deep-learning
time-series
scikit-learn
sklearn
cheatsheet
machinelearning
arima
prophet
statsmodels
sarimax
series-temporales
facebook-prophet
sarima
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Sep 16, 2019 - Jupyter Notebook
Análisis de series temporales: optativa de #DiploDatos
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Sep 13, 2019 - Jupyter Notebook
Using Python Statsmodel arima method to model time series data.
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Jul 25, 2017
A small repository explaining how you can validate your linear regression model based on assumptions
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Jul 14, 2020 - Jupyter Notebook
Solutions to the labs and exercises in ISL.
python
machine-learning
numpy
sklearn
statistical-learning
pandas
seaborn
matplotlib
statsmodels
patsy
tibshirani
hastie
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Jan 21, 2019 - Jupyter Notebook
Python web application for exploring and forecasting crime rates in NYC
python
docker
data-science
pandas
flask-application
statsmodels
time-series-analysis
geospatial-analysis
forecasting-crime-rates
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Feb 26, 2019 - HTML
ML-training
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Aug 18, 2017
Supervised Machine Learning Using Regression Analysis
seaborn
ols-regression
statsmodels
multivariate-regression
pyplot
house-price-prediction
supervised-machine-learning
regression-analysis
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Jan 21, 2018 - Jupyter Notebook
Open source data science tutorials for MFlux.ai
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machine-learning
tutorial
scikit-learn
keras
tutorials
artificial-intelligence
deep-learning-tutorial
statsmodels
mlflow
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Jan 21, 2020
END TO END IMPLEMENTATION OF MACHINE LEARNING USING PYTHON
python
flask
machine-learning
numpy
sklearn
machine-learning-algorithms
pandas
seaborn
matplotlib
statsmodels
hands-on-machine-learning
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Sep 5, 2020 - Jupyter Notebook
Forecasting monthly armed robberies in Boston with an ARIMA model.
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Feb 8, 2017 - Jupyter Notebook
Evaluations and experiments with time series models
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Feb 15, 2019 - Python
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I'm sorry if I missed this functionality, but
CLIversion hasn't it for sure (I saw the related code only ingenerate_code_examples.py). I guess it will be very useful to eliminate copy-paste phase, especially for large models.Of course, piping is a solution, but not for development in Jupyter Notebook, for example.