Pre Owned Car Price Predictor

Overview

This project aims to predict the prices of Pre-Owend cars using machine learning algorithms. By analyzing various features such as make, model, year, and condition, the predictor provides users with estimated prices for used cars.

Features

• Dataset: The project utilizes a dataset containing information about Pre-Owend cars, including attributes such as make, model, year, and price.
• Machine Learning Model: A machine learning model is trained on the dataset to predict car prices based on input features.
• Web Interface: The predictor includes a user-friendly web interface where users can input car details and receive estimated prices.

Technologies Used

• Python
• Machine Learning
• Scikit-learn
• Pandas
• Numpy
• Matplotlib
• Seaborn
• Flask (for web interface)
• HTML/CSS (for styling the web interface)

Skills Demonstrated:

1. Data Preprocessing
2. Data Manipulation
3. Data Visualization
4. Data Cleaning
5. Statistical Analysis
6. Problem Solving
7. Communication

Usage

1. Clone the repository:

   git clone https://github.com/alokchoudhary05/Car_Price_Predictor.git
   

2. Install dependencies:

   pip install -r requirements.txt
   

3. Run the Flask web server:

   python app.py
   

Output

Data Sources:

• The dataset was obtained from Kaggle and is publicly accessible.
• The data was not fully clean. I was performed preprocessing for analysis.
• All dataset files are included within the project repository.

Model Training

The machine learning model is trained using Linear Regression algorithm on the dataset. It achieves 86% accuracy on the test set.

License

This project is licensed under the MIT License - see the LICENSE.md file for details.

Author:

Alok Choudhary

LinkedIn Profile: Alok Choudhary

Thanks