carGo

Revolutionizing Vehicle Valuation with AI

carGo is a project that aims to predict car prices based on real-world data from the Palestinian market. Using machine learning techniques, the project analyzes various factors influencing car prices and builds a predictive model to estimate the price of a car given its features.

Table of Contents

• Introduction
• Description
• Goal
• Features
• Summary
• Installation
• Dataset
• Model Training
• Evaluation
• Technologies
• Prerequisites
• License
• Contact

Introduction

Accurate car price prediction can benefit buyers, sellers, and market analysts by providing insights into market trends and helping in making informed decisions. This project leverages machine learning to develop a model that predicts the price of a car based on its attributes.

Description

carGo project is designed to assist individuals in estimating the value of a car based on various features such as make, model, year of manufacture, mileage, engine size, fuel type, and transmission type. By leveraging machine learning algorithms, our application aims to provide accurate and reliable price predictions, helping users make informed decisions when buying or selling cars.

Goal

The primary aim of this project is to build a robust and efficient car price prediction model tailored to the Palestinian market. This involves collecting and preprocessing relevant data, training machine learning models, and evaluating their performance to ensure high accuracy in predictions. Ultimately, the project seeks to enhance transparency and trust in the car market by providing valuable insights into car pricing.

Features

• Data preprocessing and cleaning
• Feature engineering
• Model training and validation
• Model evaluation
• Price prediction for new data

Summary

Here is a summary 📝 for the purpose of each major module or component in this project:

• app.py: The main application file that serves the Flask application.
• static: Contains static assets like CSS, JavaScript, and images.
• templates: Holds the HTML files for rendering the web pages.
• models: Contains the trained machine learning model (model.sav).
• data_cleaning_code.ipynb: Jupyter Notebook for data cleaning and preprocessing.
• dataset_extraction.ipynb: Jupyter Notebook for data extraction and exploration.
• testing.py: Script for testing the model's predictions.

Installation and usage

To run this project, ensure you have Python 3.7+ and the necessary libraries installed. Follow the steps below to set up the environment:

1. Clone the repository:

   git clone https://github.com/saifalaasabelaish/carGo.git

2. Navigate to the car-price-prediction-system directory:

cd carGo

3. Install the required dependencies:

   pip install -r requirements.txt

4. Run the Application:

   python app.py

5. Access the Application:

   Open a web browser and go to http://127.0.0.1:5000 to use the application.

Dataset

The dataset used in this project includes various features such as:

• Car make and model
• Year of manufacture
• Mileage
• Engine size
• Fuel type

The dataset is collected from the most visited Palestinian car market website oppensooq and has been cleaned and preprocessed for training.

Model Training

The model is trained using various machine learning algorithms including:

• Linear Regression
• Decision Tree
• Polynomial Regression

Evaluation

The model is evaluated using the R squared Score. Additional evaluation metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) can also be included for better assessment.

Technologies

Technologies & Tools used in this project:

Prerequisites

• Python 3.7+
• Pip

License

This project is licensed under the MIT License. See the LICENSE file for more details.

Contact

For any questions or suggestions, please contact us at Saifsabelaish@outlook.com.