iyashk/Car-Price-Prediction
A Machine Learning Project that uses Random Forest Regressor model to predict used cars price based on some attributes such as kilometers driven, age, number of previous owners etc.
The model pipeline includes data preprocessing to handle mixed categorical and numeric features, followed by training a serialized pickle artifact for deployment. A Flask web application provides real-time inference through an HTML interface, accepting user inputs for car attributes and returning predicted prices. The project uses scikit-learn for model implementation and relies on Conda for dependency management across a Python 3.6 environment.
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Last pushed
Feb 08, 2023
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