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)
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.
pip install -r requirements.txt.uvicorn app:app --host 0.0.0.0 --port 9696.http://localhost:9696.Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
A Python module for Uber Ride Prediction
To install UberRidePrediction, you can use pip:
pip install UberRidePrediction
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)
from UberRidePrediction import TrainingPipeline
trainer_pipeline = TrainingPipeline()
file_path = 'data.csv'
trainer_pipeline.train_model(file_path)
FAQs
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)
We found that UberRidePrediction demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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