Transform ML models into a native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart, Haskell, Ruby) with zero dependencies

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.

50% faster, 50% less RAM Machine Learning. Numba rewritten Sklearn. SVD, NNMF, PCA, LinearReg, RidgeReg, Randomized, Truncated SVD/PCA, CSR Matrices all 50+% faster

General statistics, mathematical programming, and numerical/scientific computing scripts and notebooks in Python

📜 🎉 Automated reporting of objects in R

Input Output Hidden Markov Model (IOHMM) in Python

A library that unifies the API for most commonly used libraries and modeling techniques for time-series forecasting in the Python ecosystem.

Hierarchical Time Series Forecasting with a familiar API

Python port of "Common statistical tests are linear models" by Jonas Kristoffer Lindeløv.

Time Series Decomposition techniques and random forest algorithm on sales data

E-Commerce Website A/B testing: Recommend which of two landing pages to keep based on A/B testing

Implemented an A/B Testing solution with the help of machine learning

Material for the tutorial, "Time series analysis with pandas" at T-Academy

Demonstration of alternatives to lme4

Naive Bayesian, SVM, Random Forest Classifier, and Deeplearing (LSTM) on top of Keras and wod2vec TF-IDF were used respectively in SMS classification

Python package for Scailable uploads

Awesome cheatsheets for Data Science

Análisis de series temporales: optativa de #DiploDatos

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.

Using Python Statsmodel arima method to model time series data.

Solutions to the labs and exercises in ISL.

Python web application for exploring and forecasting crime rates in NYC

ML-training

Evaluations and experiments with time series models

Predict Insurance charges using feature bmi, sex, smoker, region, have children and age

output the results of multiple models with stars and export them as a excel/csv file.

Consider a real estate company that has a dataset containing the prices of properties in the Delhi region. It wishes to use the data to optimize the sale prices of the properties based on important factors such as area, bedrooms, parking, etc.

Forecasting monthly armed robberies in Boston with an ARIMA model.

A small repository explaining how you can validate your linear regression model based on assumptions