Sadra Daneshvar's repositories
Gravity_Model
Streamline transportation planning with the Gravity Model's Python implementation. This repository offers a comprehensive README, user-friendly Python script, and practical examples, making it straightforward for users to implement and adapt the model to their specific urban planning scenarios.
Bike_Sharing_Problem
Comprehensive exploration of the Bike Sharing Problem using advanced Machine Learning for demand forecasting and Mathematical Linear Optimization for strategic bike allocation. Includes detailed Jupyter notebooks covering model development, analysis, and optimization solutions.
Decision_Theory_Project_2022
Efficient Government Strategies: Research findings on revolution, legal aspects, and societal satisfaction, supported by literature analysis and a Python-powered survey approach. Explore results and documentation in the attached files.
Econometrics_Project_2022
Dive into the nexus of income inequality and crime rates. Our repository showcases research findings, data, and code exploring this connection, revealing a general positive relationship in a multivariable regression model.
Furness_Method
Optimize transportation planning with the Furness Method's Python Implementation and predict future trip distribution in residential areas using this algorithmic approach. This repository provides a detailed README, Python script, and examples for easy implementation.
Operation_Research_Project_2021
Delve into a project optimizing electricity generation, pricing, and environmental considerations. Employ mathematical modeling with GAMS to enhance cost-effective strategies. Access results, code, and comprehensive documentation for valuable insights.
Simulation_Project_2023
Optimize car insurance assessment center operations with our SimPy-based Python simulation model. Improve efficiency, reduce delays, and enhance capacity management. Explore the code and detailed documentation for insights.
Principles_of_Programming_Project_2019
This GitHub repository offers Python-based airline ticket data analysis, including insights into monthly buying patterns, customer points, sales trends, popular routes, and airline performance, while ensuring ethical data usage and code modularity.