This project focuses on predicting the prices of second-hand cars using data science techniques and deploying it using Django for the backend and HTML5, Bootstrap5, and CSS for the frontend.

1. Overview
2. Dataset
3. Data Preprocessing
4. Machine Learning Model
5. Frontend Development
6. Deployment

The primary objective of this project is to assist both buyers and sellers in determining a fair market price for second-hand cars. By utilizing machine learning algorithms, potential buyers can make informed decisions when purchasing a used car, while sellers can accurately price their vehicles for sale. The project also includes a user-friendly frontend interface developed using Django, HTML5, Bootstrap5, and CSS.

The dataset used for this project comprises information about various second-hand cars, including features like car model, year of manufacture, mileage, fuel type, transmission type, owner type, and more. It is sourced from reliable sources such as online car marketplaces and classified ads.

Before training the machine learning model, the dataset undergoes preprocessing steps such as handling missing values, encoding categorical variables, scaling numerical features, and addressing outliers. This ensures the data is clean and suitable for training a predictive model.

Several regression algorithms are explored and compared to develop an effective price prediction model. Commonly used regression techniques such as Linear Regression, Decision Tree Regression, Random Forest Regression, and Gradient Boosting Regression are considered. Hyperparameter tuning is performed to optimize the performance of the selected model.

The frontend of the application is developed using Django, HTML5, Bootstrap5, and CSS. The interface provides users with a form where they can input the features of a second-hand car, such as car model, year of manufacture, mileage, fuel type, transmission type, etc. Upon submission, the predicted price is displayed to the user.

The project is deployed using Django for the backend and HTML5, Bootstrap5, and CSS for the frontend. It can be accessed through a web browser, allowing users to utilize the price prediction functionality seamlessly.

The Second Hand Car Price Prediction project, with its frontend developed using Django, HTML5, Bootstrap5, and CSS, aims to provide valuable insights into the pricing of used cars, benefiting both buyers and sellers in the automotive market. By employing data science techniques and machine learning algorithms, this project contributes to making the process of buying and selling second-hand cars more transparent and efficient.