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Build an integrated ML pipeline for vehicle pricing. This project includes data loading, preprocessing, regression models, and hyperparameter optimization. πŸš—πŸ’»

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Integrated ML Pipeline for Vehicle Pricing πŸš—πŸ’°

Vehicle Pricing Pipeline

Welcome to the Integrated ML Pipeline for Vehicle Pricing repository! This project is a culmination of my work during a Machine Learning course in my Master's Degree in Computer Science and Engineering at the University of Catania. Here, you will find a comprehensive pipeline that utilizes various machine learning techniques to predict vehicle prices based on multiple features.

Table of Contents

Project Overview

The Integrated ML Pipeline for Vehicle Pricing project aims to provide an efficient and scalable solution for predicting vehicle prices. The project employs several machine learning algorithms, including regression models and ensemble methods, to ensure accurate predictions. The pipeline includes steps for data collection, preprocessing, model training, evaluation, and deployment.

Key features of the project include:

  • Data Collection: Automated scripts to gather vehicle data from various online sources.
  • Data Preprocessing: Cleaning and transforming raw data into a usable format.
  • Model Training: Utilizing different algorithms to train models on the processed data.
  • Model Evaluation: Assessing model performance using metrics like RMSE and RΒ².
  • Deployment: Instructions for deploying the model for real-time predictions.

You can find the latest releases of this project here. Please download and execute the necessary files to get started.

Technologies Used

This project incorporates a variety of technologies and libraries:

  • Programming Language: Python
  • Data Analysis Libraries:
    • Pandas
    • NumPy
  • Visualization Libraries:
    • Matplotlib
    • Seaborn
  • Machine Learning Libraries:
    • Scikit-learn
  • Development Environment:
    • Jupyter Notebook
  • Version Control:
    • Git
    • GitHub

Installation

To set up the Integrated ML Pipeline for Vehicle Pricing on your local machine, follow these steps:

  1. Clone the Repository: Open your terminal and run:

    git clone https://github.com/kotyll11/Integrated_ML_Pipeline_for_Vehicle_Pricing.git
  2. Navigate to the Project Directory:

    cd Integrated_ML_Pipeline_for_Vehicle_Pricing
  3. Create a Virtual Environment (optional but recommended):

    python -m venv venv
    source venv/bin/activate  # On Windows use `venv\Scripts\activate`
  4. Install Required Packages: Use pip to install the necessary libraries:

    pip install -r requirements.txt
  5. Run the Jupyter Notebook: Start Jupyter Notebook:

    jupyter notebook

Now, you are ready to explore the pipeline and experiment with the data.

Usage

Once the setup is complete, you can start using the Integrated ML Pipeline for Vehicle Pricing. Open the Jupyter Notebook files in the notebooks directory. Here’s a brief guide on how to navigate through the pipeline:

  1. Data Collection:

    • Review the data collection scripts to understand how data is gathered.
  2. Data Preprocessing:

    • Examine the preprocessing steps to see how raw data is cleaned and transformed.
  3. Model Training:

    • Explore different cells that demonstrate how various algorithms are implemented.
  4. Model Evaluation:

    • Check the evaluation metrics used to assess model performance.
  5. Deployment:

    • Follow the instructions to deploy the model for predictions.

For any updates or changes, please refer to the Releases section.

Data Sources

The dataset used in this project consists of various features related to vehicles, such as:

  • Make and Model
  • Year of Manufacture
  • Mileage
  • Engine Size
  • Fuel Type
  • Transmission Type

Data was sourced from reputable online platforms and APIs to ensure accuracy and relevance.

File Structure

The repository has the following structure:

Integrated_ML_Pipeline_for_Vehicle_Pricing/
β”‚
β”œβ”€β”€ notebooks/
β”‚   β”œβ”€β”€ Data_Collection.ipynb
β”‚   β”œβ”€β”€ Data_Preprocessing.ipynb
β”‚   β”œβ”€β”€ Model_Training.ipynb
β”‚   └── Model_Evaluation.ipynb
β”‚
β”œβ”€β”€ scripts/
β”‚   β”œβ”€β”€ collect_data.py
β”‚   β”œβ”€β”€ preprocess_data.py
β”‚   └── train_model.py
β”‚
β”œβ”€β”€ data/
β”‚   β”œβ”€β”€ raw/
β”‚   └── processed/
β”‚
β”œβ”€β”€ requirements.txt
└── README.md

Contributing

Contributions are welcome! If you would like to contribute to the project, please follow these steps:

  1. Fork the repository.
  2. Create a new branch for your feature:
    git checkout -b feature-name
  3. Make your changes and commit them:
    git commit -m "Add a descriptive message"
  4. Push to the branch:
    git push origin feature-name
  5. Create a pull request.

Please ensure your code adheres to the project's coding standards and includes appropriate tests.

License

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

Contact

For questions or feedback, feel free to reach out:

Thank you for checking out the Integrated ML Pipeline for Vehicle Pricing! For further updates, visit the Releases section.