Dynamic Ride Pricing App

This is a Streamlit web application designed to implement a dynamic pricing model for ride-sharing platforms. Users can enter different parameters, and the app will predict the ride fare using a trained Random Forest Regressor model. The application also features visualizations that compare the predicted prices with the actual fares, offering insights into the model's accuracy.

-Predict ride prices based on user inputs such as the number of riders, number of drivers, vehicle type, and expected ride duration.

-Visualize predicted ride prices vs. actual values using interactive Plotly graphs.

-Analyze the distribution of profitable and loss-making rides for valuable insights.

-Handle missing data through effective preprocessing techniques.

-Use a Random Forest Regressor model to generate accurate price predictions.