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View Code? Open in Web Editor NEWClustering Analysis is used to analyze the travel behaviors, preferences, and attitudes of New York City citizens.
Clustering Analysis is used to analyze the travel behaviors, preferences, and attitudes of New York City citizens.
Group the data based on the household feature to better understand the data.
Having a well-organized project structure makes it easy to understand and make changes.
In general, these are the basic folders we will work on:
data, models, notebooks, utils and src folders.
The Citywide Mobility Survey (CMS) is a survey conducted by the New York City Department of Transportation (DOT) to gather information about the travel behavior, preferences, and attitudes of New York City residents. The survey is conducted periodically, and the data collected is used to inform transportation planning and policy decisions.
The primary objectives of the CMS data collection are:
The objective of this project is to analyze the CMS dataset to gain insights into the travel behavior, preferences, and attitudes of New York City residents. Specifically, we aim to:
By achieving these objectives, we hope to contribute to the ongoing efforts to improve transportation in New York City and enhance the mobility of its residents.
Do Feature Engineering includes:
Based on the dataset and our sumption and understanding:
Output expected:
Dataset with specific collected data method and with only persons who fill out the servay.
Provide the basic information about the dataset in the notebook file. It must contain:
This task is for everyone.
We will meet to identify the purpose of our project and to study the features and their potential values (data) in order to determine how to deal with them.
understand then give a summary of the fourth link in the data website.
Meet to discuss the clustering results for various algorithms and our progress.
Cleaning the data by removing columns that are unnecessary, or contain a large amount of missing data. in addition to dealing with noisy or inconsistent data.
understand then give a summary of the first link in the data website.
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