Description
carprice is my project.
The project I have developed combines Jupiter Notebook and web development to create a powerful tool for predicting car prices based on various data points. By leveraging machine learning algorithms and a user-friendly interface, this project assists users in estimating the value of a car given inputs such as the company name, car model, year of purchase, fuel type, and number of kilometers driven.
The project utilizes Jupyter Notebook, an interactive coding environment, which allows for the seamless integration of data manipulation, analysis, and machine learning techniques. By employing libraries such as Pandas, NumPy, and Scikit-learn, the data provided by the user is processed and transformed into a format suitable for predictive modeling.
The web development aspect of the project involves creating a user interface that enables individuals to input their car's details and obtain an estimated price. Leveraging web development technologies like HTML, CSS, and JavaScript, I have built an intuitive web page where users can easily input the relevant information and obtain accurate predictions.
Behind the scenes, the machine learning model trained on a vast dataset of car sales information learns the patterns and correlations between the input data and the corresponding prices. This allows the model to generate reliable predictions for car prices based on the provided parameters.
By combining the power of Jupyter Notebook and web development, this project offers a seamless and user-friendly experience for predicting car prices. Whether you are a car buyer, seller, or simply curious about the market value of a particular vehicle, this tool can provide valuable insights and assist you in making informed decisions.
