https://github.com/gokulnpc/car-price-prediction

This is a simple web app to predict the price of a car based on the user inputs. The model used in this web app is a Random Forest Regressor model. The model was trained on a dataset containing information about used cars. The dataset was collected from the Kaggle website.
https://github.com/gokulnpc/car-price-prediction

machine-learning random-forest regression-models streamlit

Last synced: 12 days ago
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This is a simple web app to predict the price of a car based on the user inputs. The model used in this web app is a Random Forest Regressor model. The model was trained on a dataset containing information about used cars. The dataset was collected from the Kaggle website.

• Host: GitHub
• URL: https://github.com/gokulnpc/car-price-prediction
• Owner: gokulnpc
• Created: 2024-03-15T11:23:03.000Z (11 months ago)
• Default Branch: main
• Last Pushed: 2024-03-15T11:35:11.000Z (11 months ago)
• Last Synced: 2024-12-07T04:12:17.224Z (2 months ago)
• Topics: machine-learning, random-forest, regression-models, streamlit
• Language: Jupyter Notebook
• Homepage: https://car-price-prediction-gokulnpc.streamlit.app/
• Size: 2.59 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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# Car Price Prediction

This is a simple web app to predict the price of a car based on the user inputs. The model used in this web app is a Random Forest Regressor model. The model was trained on a dataset containing information about used cars. The dataset was collected from the Kaggle website. The dataset contains the following columns:

- Name: The name of the car

- Year: The year in which the car was manufactured

- Selling Price: The price at which the car was sold

- Present Price: The current price of the car

- Kms Driven: The number of kilometers the car has been driven

- Fuel Type: The type of fuel used by the car

- Seller Type: The type of seller (Individual or Dealer)

- Transmission: The type of transmission (Manual or Automatic)

- Owner: The number of previous owners of the car

- Mileage: The mileage of the car

- Engine: The engine capacity of the car

- Power: The power of the car

- Seats: The seating capacity of the car

- Brand: The brand of the car

- Model: The model of the car

- Color: The color of the car

- Fuel Tank Capacity: The capacity of the fuel tank