Indian Used Car Price Prediction 2023

This project aims to predict the prices of used cars in India using machine learning techniques. The dataset used for this project is usedCars.csv.

Project Structure

Indian Used Car Prediction 2023.ipynb
usedCars.csv
.ipynb_checkpoints/
    Indian Used Car Prediction 2023-checkpoint.ipynb

• Indian Used Car Prediction 2023.ipynb: Jupyter notebook containing the code and analysis for predicting used car prices.
• usedCars.csv: Dataset containing information about used cars in India.
• .ipynb_checkpoints/: Directory containing checkpoint files for the Jupyter notebook.

Getting Started

Prerequisites

• Python 3.x
• Jupyter Notebook
• Required Python libraries (listed in requirements.txt)

Installation

1. Clone the repository:

   git clone https://github.com/Mayank7317/Indian-Used-Car-Price-Prediction-2023.git
   cd Indian-Used-Car-Price-Prediction-2023

2. Install the required libraries:

   pip install -r requirements.txt

3. Open the Jupyter notebook:

   jupyter notebook Indian\ Used\ Car\ Prediction\ 2023.ipynb

Usage

Run the cells in the Jupyter notebook to perform data analysis, preprocessing, and model training for predicting used car prices.

Dataset

The dataset usedCars.csv contains the following columns:

• Id: Unique identifier for each car
• Company: Car manufacturer
• Model: Car model
• Variant: Car variant
• FuelType: Type of fuel used by the car (Petrol/Diesel/CNG)
• Colour: Color of the car
• Kilometer: Distance driven in kilometers
• BodyStyle: Body style of the car (Hatchback/Sedan/SUV/MPV)
• TransmissionType: Transmission type (Manual/Automatic)
• ManufactureDate: Date of manufacture
• ModelYear: Year of the model
• CngKit: Presence of CNG kit (if any)
• Price: Selling price of the car
• Owner: Number of previous owners
• DealerState: State of the dealer
• DealerName: Name of the dealer
• City: City of the dealer
• Warranty: Warranty status
• QualityScore: Quality score of the car