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🚗Car Price Prediction System

Welcome to the Car Price Prediction System repository! This project leverages advanced machine learning techniques to predict the prices of cars based on a range of features. Whether you're a car enthusiast or a data scientist, this tool offers valuable insights into the factors that influence car prices.

Home Screen | Landing Page

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Input Section

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Prediction | Working of Model

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📋Table of Contents

🛠️Introduction

The Car Price Prediction System uses data from a variety of sources to train a machine learning model capable of predicting car prices. By analyzing features such as Company, model, year, kms driven, and more, this system can provide pretty accurate and insightful predictions.

✨Features

Data Preprocessing: Clean and preprocess raw data to ensure model accuracy. Model Training: Train the model using state-of-the-art algorithms and techniques. Price Prediction: Predict car prices based on user input and trained model. Visualization: Visualize data trends and model performance. User-Friendly Interface: Intuitive user interface for easy data input and predictions.

💻Installation

To run the Car Price Prediction System, you'll need to have Python and the necessary libraries installed on your system. Follow these steps:

1. Clone the repository:

Command:

2. Navigate to the project directory:

Command:

  • cd car-price-prediction

Suppose if i have downloaded this project in desktop Command will be:

  • cd C:\Users\os\Desktop\Car_Price_Prediction_System

3. Install the required libraries:

Command:

  • pip install -r requirements.txt

🚀Usage

  1. Prepare your data in the specified format.
  2. Run the system using the command:

bash

  • python application.py
  1. Follow the on-screen instructions to input data and receive predictions.

📊Data

The system uses a dataset containing various car features such as Brand/Company, model, year, kms driven, and fuel type. The data is cleaned and preprocessed before being used for training and testing the model.

🤖Model

The model is trained using a regression algorithm and is fine-tuned for optimal performance. Advanced metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) are used to evaluate the model's accuracy.

🙌Contributing

Contributions to this project are welcome! If you would like to contribute, please fork the repository and submit a pull request. Make sure to follow the project's code of conduct.

📄License

This project is licensed under the MIT License.