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aryashah2k / Operations-Research-And-Optimization-With-Pyomo

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Star 13

A Repository Consisting Of Code And Theory For Operations Research And Optimization Problems Being Solved While I Learn The Python Package - Pyomo.

optimization transportation curve-fitting nqueens-problem pyomo operations-research pareto-front optimization-algorithms maxflow heron steiner-tree-problem travelling-salesman-problem pareto-optimality fastest-routes graphcoloring
  • Updated Mar 17, 2021

rajesvariparasa / spatial-routing-libraries-and-services

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Using OSMnx, OSRM, and Google Maps Directions API with Python to calculate shortest, fastest, and traffic-based most-efficient routes for a set of origin and destination points

python osrm routes road-network cities shortest-paths travel-times google-maps-api directions-api networkx-graph origin-destination fastest-routes osmnx travel-distances geopandas-dataframes origin-destination-routing python-routing
  • Updated Apr 24, 2021
  • Jupyter Notebook

ankitAMD / NYTaxi_XG_Boost_Challenge-kaggle_challenge

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I'm attempting the NYC Taxi Duration prediction Kaggle challenge. I'll by using a combination of Pandas, Matplotlib, and XGBoost as python libraries to help me understand and analyze the taxi dataset that Kaggle provides. The goal will be to build a predictive model for taxi duration time. I'll also be using Google Colab as my jupyter notebook. i will also predict without Google colab on normal system.

python machine-learning deep-learning random-forest numpy linear-regression scikit-learn pandas python3 kaggle-competition xgboost matplotlib feature-engineering knn-regression xgboost-model nyc-taxi-dataset fastest-routes visualization-image google-colab remote-server
  • Updated Sep 21, 2018
  • Jupyter Notebook

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