This project is focused on utilizing Machine Learning techniques, such as linear and logistic regression, on the NHTS (National Household Travel Survey) dataset.
The project directory is organized as follows:
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Codes/: Python codes that are used for developing and implementing machine learning models.
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Dataset/: Datasets that are used for training and testing machine learning models.
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Instructions/: Communications and instructions from the project advisor.
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Presentations/: Presentation slides and project updates.
The goal of this project is to explore the NHTS dataset and develop Machine Learning models that can accurately predict household travel behavior. To achieve this, we will be using techniques such as linear regression and logistic regression.
The NHTS dataset is a national survey conducted by the Federal Highway Administration in the United States. It contains information about household travel behavior, including trip purpose, mode of transportation, and distance traveled. The dataset is publicly available and can be accessed from the National Household Travel Survey website.
To get started with the project, clone the repository to your local machine and navigate to the Codes/ directory. From there, you can run the Python codes and explore the results.
- Eduardo Lopez
- Waquar Kaleem
We would like to thank our project advisor, Dr. Anirudh Subramanyam, for his guidance and support throughout this project.