This repository contains a Machine Learning model and a Streamlit web application for predicting taxi fare prices in New York City. The model is trained on the New York City Taxi Fare Prediction competition dataset from Kaggle.
The goal of this project is to develop a predictive model that estimates the fare amount for taxi rides in New York City based on various input features such as pickup and drop-off locations, passenger count, date, and time.
The repository consists of the following components:
- A trained Machine Learning model
- A Streamlit web application for interacting with the model
- Kaggle analysis and insights
- Predict taxi fare prices using the trained Machine Learning model.
- Visualize and interact with the model through the Streamlit web application.
- Explore insights and analysis of the New York City Taxi Fare Prediction competition on Kaggle.
Kaggle Analysis Explore the detailed analysis and insights of the New York City Taxi Fare Prediction competition in my Kaggle Notebook
To run the Streamlit web application: New York City Taxi Fare Predictor
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