Car_Price_Prediction_with_Machine_Learning

Overview This project is an interactive web application designed to predict car prices using machine learning techniques. The application leverages a Linear Regression model to estimate prices based on various features of cars, such as brand goodwill, horsepower, mileage, and more.
Key Features Machine Learning Model: Utilizes Linear Regression for accurate price predictions based on input features. Data Analysis: Developed using Python, the project involves data analysis and preprocessing to prepare the dataset for modeling. Interactive User Interface: Built with Streamlit, the application provides a user-friendly interface that allows users to input car specifications and receive predicted prices. Important Note Due to compatibility issues, the Jupyter Notebook (.ipynb) files cannot be used directly in Streamlit. As a result, the Streamlit application code is written separately in a Python (.py) file.rs.