Car Price Prediction

This project aims to predict the selling price of cars using machine learning techniques. The dataset contains various attributes of cars such as name, year, selling price, present price, kilometers driven, fuel type, seller type, transmission, and owner.

Table of Contents

• Introduction
• Dataset
• Requirements
• Installation

Introduction

Predicting the price of used cars can be a challenging task due to various influencing factors. This project utilizes machine learning algorithms to predict the selling price of a car based on its features.

Dataset

The dataset contains the following columns:

• Car_Name: Name of the car
• Year: Year of manufacture
• Selling_Price: Price at which the car is being sold (in lakhs)
• Present_Price: Current ex-showroom price of the car (in lakhs)
• Kms_Driven: Number of kilometers driven
• Fuel_Type: Type of fuel used (Petrol/Diesel/CNG)
• Seller_Type: Type of seller (Dealer/Individual)
• Transmission: Type of transmission (Manual/Automatic)
• Owner: Number of previous owners

Requirements

• Python 3.6+
• Pandas
• NumPy
• Scikit-learn
• Matplotlib
• Seaborn
• Jupyter Notebook (optional for running the notebook)

Installation

To install the required libraries, run:

pip install pandas numpy scikit-learn matplotlib seaborn jupyter