Car Price Prediction

Instructions:

1. Project Overview: A brief description of the project, its goal, and the improvement in the R² score.
2. Dataset: A short note on the dataset used.
3. Models Used: List the models applied in the project.
4. Results: Highlight the improvement in the model's performance.

Table of Contents

• Project Overview
• Dataset
• Models Used
• Results

Project Overview

This project aims to predict car prices using a dataset of 6000 data points. We implemented and compared two machine learning models: Linear Regression and Random Forest Regressor. The project involves data preprocessing, model training, evaluation, and optimization. The R² score was improved from 69% to 91% through careful feature engineering and model tuning.

Dataset

The dataset consists of 6000 data points, each representing a car with various attributes such as make, model, year, mileage, fuel type, and more. The target variable is the car price.

Models Used

1. Linear Regression
2. Random Forest Regressor

Results

Through iterative training and optimization, the R² score of our models was improved from an initial 75% to a final 93%. This significant improvement demonstrates the effectiveness of feature engineering and model selection.