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https://github.com/kowshik24/uber-ride-price-prediction
A web application that predicts the price of an Uber ride based on several factors, such as pickup location, dropoff location, and the number of passengers.
https://github.com/kowshik24/uber-ride-price-prediction
fastapi machine-learning render web-application
Last synced: about 1 month ago
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A web application that predicts the price of an Uber ride based on several factors, such as pickup location, dropoff location, and the number of passengers.
- Host: GitHub
- URL: https://github.com/kowshik24/uber-ride-price-prediction
- Owner: kowshik24
- Created: 2024-06-17T08:24:50.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2024-07-16T19:14:01.000Z (6 months ago)
- Last Synced: 2024-11-16T02:12:52.021Z (2 months ago)
- Topics: fastapi, machine-learning, render, web-application
- Language: Jupyter Notebook
- Homepage: https://uber-ride-price-prediction.onrender.com/
- Size: 7.29 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Uber Ride Price Prediction
A web application that predicts the price of an Uber ride based on several factors, such as pickup location, dropoff location, and the number of passengers.
## Setup and Installation
1. Clone the repository to your local machine.
2. Install the required Python packages by running `pip install -r requirements.txt`.
3. Start the FastAPI server by running `uvicorn app:app --host 0.0.0.0 --port 9696`.
## Usage
1. Open your web browser and navigate to `http://localhost:9696`.
2. Fill out the form with the details of your ride.
3. Click the "Submit" button to get a prediction of the ride price.
## Technologies Used
- FastAPI for the web server.
- jQuery for handling AJAX requests.
- Python for the prediction logic.
## Future Improvements
- Improve the accuracy of the prediction model.
- Add support for more ride types.
- Improve the user interface.
## Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
## UberRidePrediction
A Python module for Uber Ride Prediction
### Installation
To install `UberRidePrediction`, you can use pip:
```
pip install UberRidePrediction
```
### Usage:
#### Make Prediction:
```
from UberRidePrediction import PredictionPipeline
prediction_pipeline = PredictionPipeline()
prediction_pipeline.load_model()
# For example this is your data:
pickup_datetime = '2012-04-21 08:30:00'
pickup_longitude = -73.987130
pickup_latitude = 40.732029
dropoff_longitude = -73.991875
dropoff_latitude = 40.74942
passenger_count = 1
prediction = prediction_pipeline.make_single_prediction(pickup_datetime, pickup_longitude, pickup_latitude, dropoff_longitude, dropoff_latitude, passenger_count)
print(prediction)
```
#### Train Model:
```
from UberRidePrediction import TrainingPipeline
trainer_pipeline = TrainingPipeline()
file_path = 'data.csv'
trainer_pipeline.train_model(file_path)
```
## License
[MIT](https://choosealicense.com/licenses/mit/)