semvec/pypi-packages huggingface dataset:
UberRidePrediction 0.0.1
PineconeUtils is a Python module designed to handle and process data for embedding and indexing using Pinecone, Cohere, and OpenAI services. This utility module makes it easy to load, chunk, prepare, and upsert data into a Pinecone index, making it ideal for applications involving text embedding and retrieval augmented systems(RAG)
License: MIT
Requires Python: >=3.7

# 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.


![Demo Image](static/demo.png)

## 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/)