dhruwsunita/used-car-price-predictor

Used cars price prediction using Python, Machine Learning, HTML and CSS.

Used Car Price Predictor

Overview:

This data science project will help users to predict the prices of used cars based on various factors such as kilometers driven, age, car model, year of purchase etc. It uses Linear Regression model to estimate fair market value of pre-owned vehicles.The performance of the model is evaluated using the R2 score metric.

Project Snapshots:

Features

• Data Collection: Data is gathered from various sources, including online marketplaces like Kaggle and quiker.
• Data Preprocessing: The dataset undergoes preprocessing steps such as handling missing values, encoding categorical variables, and feature scaling to prepare it for modeling.
• Model Building: A Linear Regression model is trained on the preprocessed data to predict used car prices.
• Model Evaluation: The performance of the Linear Regression model is evaluated using the R2 score metric.

Dataset

The dataset used for this project contains information about used cars, including their make, model, year of manufacture, mileage, fuel type, transmission type, and other relevant features. The target variable is the price of the used car.

Requirements

• Python 3.x
• Pandas
• NumPy
• Scikit-learn
• Flask (for deployment)