15+ Machine/Deep Learning Projects in Ipython Notebooks

pwtools is a Python package for pre- and postprocessing of atomistic calculations, mostly targeted to Quantum Espresso, CPMD, CP2K and LAMMPS. It is almost, but not quite, entirely unlike ASE, with some tools extending numpy/scipy. It has a set of powerful parsers and data types for storing calculation data.

A repository to explore the concepts of applied econometrics in the context of financial time-series.

This repo demonstrates how to build a surrogate (proxy) model by multivariate regressing building energy consumption data (univariate and multivariate) and use (1) Bayesian framework, (2) Pyomo package, (3) Genetic algorithm with local search, and (4) Pymoo package to find optimum design parameters and minimum energy consumption.

No description provided.

SKBEL - Bayesian Evidential Learning framework built on top of scikit-learn.

Iterative hard thresholding for l0 penalized regression

Equities Pair Trading/Statistical Arbitrage and Multi-Variable Index Regression

Code to perform multivariate linear regression using Gibbs sampling

Machine Learning Project to Predict House Prices in Bangalore.

Several examples of multivariate techniques implemented in R, Python, and SAS. Multivariate concrete dataset retrieved from https://archive.ics.uci.edu/ml/datasets/Concrete+Slump+Test. Credit to Professor I-Cheng Yeh.

MATLAB implementation of Gradient Descent algorithm for Multivariate Linear Regression

The project aims to perform various visualizations and provide various insights from the considered Indian automobile dataset by performing data analysis that utilizing machine learning algorithms in R programming language.

python implementation of process mining and machine learning algorithm

A small tutorial on MARS: Multivariate Adaptive Regression Splines in Python

Multivariate Markov-Switching Models Regressions Framework

A graphical multivariate lesion-symptom mapping toolbox

A cluster of Machine Learning algorithms

Helper R scripts for multiple PERMANOVA tests, AICc script for PERMANOVA, etc.

Robust transfer function analysis code for magnetotellurics

Forecasting exchange rates by using commodities prices

Predicting Crop Yield.

To know internal working of machine learning algorithms, I have implemented types of regression through scratch.

Answers of exercises on "Introduction to Multivariate Analysis; from Linear to Nonlinear" (Iwanami Shoten, 2010) by Sadanori Konishi.

Predicting solar generation based on weather forecast - a project which was part of Machine Learning course at BITS Pilani

Bayesian spatial regression with Meshed Gaussian Process

🌍Flood risk modelling with logistic regression (multivariate)

Spatial Multivariate Trees for Big Data Bayesian Regression

Deep Neural Network for Weather Time Series Forecasting

[42 curriculum] One week to learn basics in Machine Learning! 🤖 (well, as one of the authors, It was just to get the XP because the pedago of 42Paris was too slow to give it to me). This project is a Machine Learning bootcamp created by 42 AI.