From 42f2182bd44d18fd7720b9050a397c929a327dd1 Mon Sep 17 00:00:00 2001 From: Arpan Pramanik Date: Fri, 15 Nov 2024 15:42:21 +0530 Subject: [PATCH] Update README.md --- README.md | 15 ++++++++------- 1 file changed, 8 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index 9449ea9..d992d77 100644 --- a/README.md +++ b/README.md @@ -1,10 +1,11 @@ # 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.