https://github.com/LisaATaylor/Rideshare

Jupyter notebooks for Capstone 1
https://github.com/LisaATaylor/Rideshare

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Jupyter notebooks for Capstone 1

• Host: GitHub
• URL: https://github.com/LisaATaylor/Rideshare
• Owner: LisaATaylor
• Created: 2019-07-28T17:42:35.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2020-02-10T17:55:56.000Z (almost 5 years ago)
• Last Synced: 2024-08-03T13:02:33.844Z (5 months ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 23.4 MB
• Stars: 0
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome-springboard-capstones - GitHub

README

        
# Machine Learning with the Chicago Rideshare "Trips" Dataset

## Lisa Taylor

### Springboard Data Science Bootcamp - Capstone Project 1

Since November 2018, rideshare providers (Lyft, Uber, etc) operating in Chicago have been required by ordinance to submit periodic data reports reports with basic rideshare information to the City. The main Trips dataset includes information about individual rides, including starting and ending times and locations, fares and tips, and whether the rides were pooled. The Trips data are anonymized, so trip starting and ending locations are generalized to the nearest census tract, and drivers cannot be linked to particular rides they provided. The portion of the dataset used for this exercise covers November 2018 through March 2019.

To prepare for the analyses and visualizations in this notebook, the Trips data was cleaned, aggregated, and enriched with climate and census data. Inferential statistical techniques were applied to test hypotheses regarding the data.  Supervised learning with random forest regression was applied to develop a simple rideshare demand prediction model.  Finally, a clustering analysis was used to perform customer segmentation and gain insight into rideshare usage patterns in Chicago.

### Data preparation, enrichment and visualization

[Data Wrangling and Visualization](Chicago%20Rideshare%20-%20Wrangling+Visualization.ipynb)


[Chicago Rideshare Data Story](Chicago%20Rideshare%20Data%20Story.ipynb)


[Python Data Prep Functions](RideshareDataPrep.py)

### Statistical analysis

[Application of Inferential Statistics](Chicago%20Rideshare%20Dataset-%20Statistical%20Inference.ipynb)


### Machine Learning

[Rideshare Usage Prediction with Random Forest Regression](https://nbviewer.jupyter.org/github/LisaATaylor/Rideshare/blob/master/Chicago%20Rideshare%20Dataset%20-%20Usage%20Prediction.ipynb)


[Rideshare Customer Segmentation  with KMeans Clustering](https://nbviewer.jupyter.org/github/LisaATaylor/Rideshare/blob/master/Chicago%20Rideshare%20Dataset%20-%20Clustering%20Analysis.ipynb)


### Reporting

[Final Report](Capstone%201%20Reports/Capstone%201%20-%20Final%20Report.pdf)


[Final Presentation](Capstone%201%20Reports/Capstone%201%20-%20Chicago%20Rideshare%20Walkthrough.pdf)