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iads_summerschool_2019

Decision Trees, Random Forests, and Gradient Boosting

juanbazo.github.io

Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes

machine-learning

Regression and classification Problem. Used Logistic Regression, Linear Regression and Random Forest

machine-learning-models

0) Data Preprocessing 1) Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial 2) Regression, SVR, Decision Tree Regression, Random Forest Regression 3) Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification 4) Clustering 5) Association Rule Learning 6) Reinforcement Learning 7) Natural Language Processing 8) Deep Learning 9) Dimensionality Reduction 10) Model Selection & BoostingSearch, XGBoost

machine-learning-project

Logistic Regression, decision tree , Linear regression , Random forest algorithms

machinelearning-az

Repositorio del Curso de Machine Learning de la A a la Z con R y Python

melbourne-housing-prediction-with-random-forest-regression-

metocean

Python for ocean - atmosphere science and engineering

ml-combined-assignment

Application of Linear Regression, Decision Tree, Random Forest, Xgboost

ml4floods

An ecosystem of data, models and code pipelines to tackle flooding with ML

ncep-gfs

Visualizing NCEP/GDAS' FNL Meteorological Dataset, core of GFS Model, at 0.25 x 0.25 degree (These notebooks can be used, accordingly, based on your preference).

notebook-examples

Example notebooks showing how to work with ECMWF services and data

oasispiwind

Toy UK windstorm model

oasisui

User Interface for the Oasis platform.

pisco_clase

unalm 12.10.2019

plot-uv-wind

Plotting wind components with xarray and cartopy libraries.

predicting-flood-in-malawi

in this project we will use machine learning techniques to predict floods in Malawi

python_for_geosciences

Introduction to python use in geosciences.

pythonista

random-forest-linear-regression

A model that could predict the individual medical charges incurred by a health insurance provider based on a handful of categorical and numeric attributes.

regressions-models-selection

Plotting the behavior of different kinds of regressions including: Linear Regression, Polynomial Regression, Support Vector regression (SVR), Decision Tree Regression and Random Forest Regressor.

rentalbikeprediction

Used the “Seoul Bike Data” available on UCI Machine Learning Repository. Applied Regression analysis to predict the bike count needs to be available to meet customer demand. Compared different model accuracy and got 90% as the best. Working to publish this project as well.

scikit-downscale

Statistical climate downscaling in Python

south_sudan_conflict_migration_project

A technical analysis of population displacement during conflict.

spatial-analysis---drought-sensitivity

The Palmer Drought Severity Index dataset provides high spatial resolution (~4-km) thrice-monthly estimates of this widely used measure of integrated water supply and demand anomalies across the contiguous United States from 1979-present. The PDSI is calculated using precipitation and potential evapotranspiration derived from the gridded meteorological dataset of Abatzoglou (2013). Potential evapotranspiration is computed using the Penman-Montieth equation for a reference grass surface. Available soil water holding capacity in the top 2.5m of the soil was derived from the STATSGO soils database and used in the computations. Whereas PDSI has typically been computed on monthly timescales, we compute these data three-times a month to provide more timely updates. Due to the spin-up of PDSI calculations, data for the first year of record should be used sparingly. This dataset contains provisional products that are replaced with updated versions when the complete source data become available. Products can be distinguished by the value of the 'status' property. At first, assets are ingested with status='early'. After several days, they are replaced by assets with status='provisional'. After about 2 months, they are replaced by the final assets with status='permanent'.

spatial-model-malaria

staeiou.github.io

Github pages

storm_forecast

ramp kit for storm intensity forecasting

subseasonal_toolkit

Subseasonal forecasting models

temperature_prediction_randomforests

Predicting Transient Temperatures during Heat Conduction using Ensemble Regression Models