Chicago Taxi Fare Prediction Application using Vertex AI AutoML

• Application Demo

The "Chicago Taxi Fare Prediction" project aims to develop a machine learning model using Vertex AI AutoML that can be used for taxi fare prediction in the Chicago area. The location was obtained using the Google Places API, while the distance and duration were obtained using the Google Distance Matrix API.

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File Explanation

This repository consists of several files :

    ┌── .gitignore
    ├── README.md
    ├── app.py
    ├── dockerfile
    ├── requirements.txt
    └── taxi-fare.csv

• README.md: This is a Markdown file that typically contains documentation for the project. It include information on how to set up and run your application, dependencies, and any other relevant details.

• app.py: This file is the main script for the frontend of the application and is developed using the Streamlit framework.

• dockerfile: Dockerfile is used to build a Docker image for frontend application. It includes instructions on how to set up the environment and dependencies needed for frontend.

• requirements.txt: This file lists the Python dependencies required for frontend application. These dependencies can be installed using a package manager like pip.

• README.md: This is a Markdown file that typically contains documentation for the project. It include information on how to set up and run your application, dependencies, and any other relevant details.

• taxi-fare.csv: This is the CSV file used as the dataset in this project. Dataset obtained from Google Cloud Platform - BigQuery database : chicago_taxi_trips, table: taxi_trips.

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Application

How to use

Users can use this application by entering the desired location on the widget. You can also see the Exploratory Data Analysis and Model Evaluation.

Preview

streamlit-app-2024-02-21-09-02-61.mp4