https://github.com/akarshankapoor7/dynamic_pricing_model_in-python
Dynamic Ride Pricing App This is a Streamlit web application designed to implement a dynamic pricing model for ride-sharing platforms.
https://github.com/akarshankapoor7/dynamic_pricing_model_in-python
data-science dynamic-pricing plotly-python random-forest-regressor streamlit-application supply-chain
Last synced: 3 months ago
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Dynamic Ride Pricing App This is a Streamlit web application designed to implement a dynamic pricing model for ride-sharing platforms.
- Host: GitHub
- URL: https://github.com/akarshankapoor7/dynamic_pricing_model_in-python
- Owner: akarshankapoor7
- Created: 2024-12-10T14:18:47.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2024-12-10T14:27:43.000Z (5 months ago)
- Last Synced: 2024-12-27T06:12:59.265Z (5 months ago)
- Topics: data-science, dynamic-pricing, plotly-python, random-forest-regressor, streamlit-application, supply-chain
- Language: Jupyter Notebook
- Homepage:
- Size: 1.71 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
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.