Car Price and Feature Estimator

Analyze-Visualize-Predict-Contribute

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Table of Contents

1. About The Project
2. Features
3. Tech Stack
4. Getting Started
5. ScreenShots
6. Contributing
7. License

Why Car Price Predictor ?

A Chinese automobile company Geely Auto aspires to enter the US market by setting up their manufacturing unit there and producing cars locally to give competition to their US and European counterparts.

They have contracted an automobile consulting company to understand the factors on which the pricing of cars depends. Specifically, they want to understand the factors affecting the pricing of cars in the American market, since those may be very different from the Chinese market. The company wants to know:

• Which variables are significant in predicting the price of a car
• How well those variables describe the price of a car
• Based on various market surveys, the consulting firm has gathered a large data set of different types of cars across the America market.

About The Project

Suppose you are interested to buy a car, you need to check the list of cars or else want to know that what will be the price of the car which have the respective features in it.

In order to handle this situation , this website is formed which will make a suggestion list for the user and extract the details of the car available in the cart.

Key Features Implemented

• Car details extractor
• Linear Regression Prediction Model which predicts the car price
• Contribution section to add up files
• Analysis part which provides brief detail information about the dataset

Tech Stacks

Virtual Environment:

• Anaconda
• VS Code

Module used:

• Numpy
• Pandas
• Pandas-Profiling
• Matplotlib
• Seaborn
• sklearn

Dynamic Pricing:

• ML Model used- Linear-Regression Model
• Dataset used- Car details from Kaggle.

Getting Started

You can test EV Sathi in your own development environment. This section shows you how:

Prerequisites

You'll need to set up the IDE and virtual environment, or any web testing device on your local system.

Python Environment: You'll need to have the following installed:

1. Anaconda

Development Environment: For setting up the development environment, follow the steps given below.

• Clone this repository after forking using git clone https://github.com/somyarj1637/Car-Price-and-Feature-Estimator
• cd into Car-Price-and-Feature-Estimator
• open terminal
• type : pip install

Project Structure

This project structure:

View Website

link: Click Here

Contributing

Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

Distributed under the MIT License. See LICENSE for more information.