Used car price prediction using ML

Keywords: car price,prediction,machine learning, algorithm

Background

The Multibrand Used Car Dataset contains extensive data related to used cars, suitable for analysis, research, and prediction tasks. One popular application is using machine learning to predict used car prices accurately based on essential features like model year, transmission type, mileage, fuel type, and others

Aim & Objectives

Aim
The aim of this study is to perform used car price prediction using machine learning techniques. Analyzing the dataset's features, the study seeks to develop a model that can learn patterns and relationships in the data to make accurate price predictions for used cars.
Objective
1. Utilize regression algorithms like linear regression, decision trees, and random forests for used car price prediction.
2. Preprocess the data, handling missing values, encoding categorical variables, and scaling numerical features if needed.

Research Methodology

The study involves using regression algorithms like linear regression, decision trees, and random forests for price prediction. Data preprocessing includes handling missing values and encoding categorical variables. The dataset is split into training and testing sets, and the model is trained on the training set and evaluated on the testing set. The trained model is then used to predict used car prices based on input features, aiding buyers and sellers in pricing decisions

Software & Tools

Jupyter, Google Collab

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