There are 1 repository under statsmodels topic.
Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and Python functions.
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.
Waiting hours for a future prediction is unacceptable. Hyperlearn makes AI and ML algorithms 50% faster, use 90% less memory and doesn't require you to use new hardware! ML Algorithms like PCA, Linear Regression, NMF are all faster!
General statistics, mathematical programming, and numerical/scientific computing scripts and notebooks in Python
:scroll: :tada: Automated reporting of objects in R
Hierarchical Time Series Forecasting with a familiar API
Nyoka is a Python library that helps to export ML models into PMML (PMML 4.4.1 Standard).
A library that unifies the API for most commonly used libraries and modeling techniques for time-series forecasting in the Python ecosystem.
Input Output Hidden Markov Model (IOHMM) in Python
Python port of "Common statistical tests are linear models" by Jonas Kristoffer Lindeløv.
Time Series Decomposition techniques and random forest algorithm on sales data
Implemented an A/B Testing solution with the help of machine learning
E-Commerce Website A/B testing: Recommend which of two landing pages to keep based on A/B testing
Material for the tutorial, "Time series analysis with pandas" at T-Academy
Udacity FWD2.0 advanced data analysis nano degree connect sessions
Naive Bayesian, SVM, Random Forest Classifier, and Deeplearing (LSTM) on top of Keras and wod2vec TF-IDF were used respectively in SMS classification
Demonstration of alternatives to lme4
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.
End To End Tutorial on Time Series Analysis and Forcasting
Awesome cheatsheets for Data Science
Data Science Portfolio
A small repository explaining how you can validate your linear regression model based on assumptions
A sample of Quantative Analysis on Markowitz Model, CAPM, Black Scholes, and VaR. Includes ML for stock price prediction.
Time Series forecasting using Seasonal ARIMA & Prophet. Applied statistical tests like Augmented Dickey–Fuller test to check stationary of series. Checked ACF ,PACF plots to identify Moving average and Auto-regressive order of series. Transformed series to make it stationary.
The collection of exercises I did during Ironhack's Data Science bootcamp.
output the results of multiple models with stars and export them as a excel/csv file.
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.
A Repo of Time-series analysis techniques. Holt-Winter methods, ACF/PACF, MA, AR, ARMA, ARIMA, SARIMA, SARIMAX, VAR, VARMA, RNN Keras, Facebook- Prophet etc.
Algo trading strategy, entrance task to CMF, Quantitative Analytics program, 2021
Análisis de series temporales: optativa de #DiploDatos
Automating Assumption Checks for Regression Models (Work in Progress)
Bitcoin Price Prediction Modeling and Dashboard
Financial and Investment Data Science - python code and examples for statistical and machine learning on structured and unstructured financial data sets
Assignment-04-Simple-Linear-Regression-2. Q2) Salary_hike -> Build a prediction model for Salary_hike Build a simple linear regression model by performing EDA and do necessary transformations and select the best model using R or Python. EDA and Data Visualization. Correlation Analysis. Model Building. Model Testing. Model Predictions.