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 # Car-Price-Prediction-Streamli
-Car Price Prediction with Machine Learning and Streamlit
-This project uses a Random Forest model to predict car prices based on various features. The predictions are deployed in an interactive Streamlit web app, making it simple for users to input details and view estimated prices.
 
-Features
-Data Preprocessing: The project employs ColumnTransformer and Pipeline for efficient feature encoding and scaling.
-Random Forest Model: A Random Forest model is trained to ensure accurate and reliable price predictions.
-Streamlit App: An easy-to-use web interface for real-time predictions based on user-provided inputs.
-Pickle for Model Storage: The model and preprocessing pipeline are saved using pickle for quick access and deployment.
+# Car Price Prediction with Machine Learning and Streamlit
 
+This project uses a **Random Forest** model to predict car prices based on various features. The predictions are deployed in an interactive **Streamlit** web app, making it simple for users to input details and view estimated prices.
+
+## Features
+- **Data Preprocessing**: The project employs `ColumnTransformer` and `Pipeline` for efficient feature encoding and scaling.
+- **Random Forest Model**: A Random Forest model is trained to ensure accurate and reliable price predictions.
+- **Streamlit App**: An easy-to-use web interface for real-time predictions based on user-provided inputs.
+- **Pickle for Model Storage**: The model and preprocessing pipeline are saved using `pickle` for quick access and deployment.