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#artificial-intelligence #jupyter-notebook #lasso-regression #learning-material #linear-regression #machine-learning #machine-learning-algorithms #matplotlib #model-evaluation #model-training #pandas #prediction-model #prediction-system #python #r2-score #scikit-learn #streamlit #streamlit-webapp #supervised-learning

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Jupyter Notebook 96.2%

Python 3.8%

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README

Linear-Lasso-Regression-Car-Price-Prediction

Overview

Linear-Lasso-Regression-Car-Price-Prediction is an interactive web application that predicts the price of a car (or house) using machine learning models: Linear Regression and Lasso Regression. Built with Streamlit, it provides an intuitive interface for users to input car features and instantly get price predictions using two different regression models.

Author: Nhan Pham
Email: ptnhanit230104@gmail.com
Version: 1.0.0
Created: 2025-07-26

Visual Workflow

flowchart TD
    A[User Inputs Car Features] --> B{Choose Model}
    B -- Linear Regression --> C[Preprocess Features]
    B -- Lasso Regression --> C
    C --> D[Load Model]
    D --> E[Predict Price]
    E --> F[Display Result in UI]

Features

User-friendly Web UI: Built with Streamlit for easy interaction.
Dual Model Support: Predict prices using both Linear Regression and Lasso Regression.
Instant Results: Get predictions in real-time as you input data.
Educational: Explore and compare the effects of different regression techniques.
Extensible: Modular codebase for easy extension to other regression models or datasets.
Open Source: Freely available for learning, research, and extension.
Well-documented: Includes guides for dataset, models, and Streamlit usage.
Actively Maintained: Issues and PRs are welcome!

How It Works

User Input: Enter car features (year, price, kms driven, fuel type, seller type, transmission, owner).
Model Selection: Choose between Linear Regression or Lasso Regression.
Preprocessing: The app encodes categorical features and prepares the input for the model.
Prediction: The app loads the selected model (from .pkl files) and predicts the price.
Result Display: The predicted price is shown instantly in the UI.
Model Comparison: Users can easily compare predictions from both models to understand the impact of regularization.

Example Prediction

Suppose you want to predict the price of a car with the following features:

| Feature | Value | |-----------------|--------------| | Year | 2018 | | Present Price | 500000 | | Kms Driven | 30000 | | Fuel Type | Petrol | | Seller Type | Individual | | Transmission | Manual | | Owner | 1 |

Step 1: Enter these values in the app fields.
Step 2: Click With Linear model or With Lasso model.
Step 3: The app will display something like:

Predicted Price: $420,000.00

Try changing the model or input values to see how the prediction changes!

Technical Stack

Frontend/UI: Streamlit (Python-based web app framework)
Backend/ML: scikit-learn (for model training and inference)
Data Handling: pandas
Visualization: matplotlib (for EDA and model training)
Serialization: pickle (for saving/loading models)
Jupyter Notebook: For model development and experimentation

Dataset

Source: See dataset/car_data.csv
Description: Contains 301 rows and 9 columns, including features like Year, PresentPrice, KmsDriven, FuelType, SellerType, Transmission, Owner, and the target Selling_Price.
Details:

- No missing values - Categorical features are encoded for model compatibility - For more, see docs/dataset.md

Sample Columns:

- Year: Year of manufacture - Present_Price: Current ex-showroom price - Kms_Driven: Kilometers driven - Fuel_Type: Petrol/Diesel/CNG - Seller_Type: Dealer/Individual - Transmission: Manual/Automatic - Owner: Number of previous owners - Selling_Price: Price at which the car was sold (target)

Model Training

Notebook: model/model-training.ipynb
Models:

- linear_model.pkl: Trained Linear Regression model - lasso_model.pkl: Trained Lasso Regression model

Workflow:

1. Data loaded and cleaned 2. Categorical features encoded 3. Data split into train/test sets 4. Models trained and evaluated 5. Best models saved as .pkl files

Learn more:

- Linear Regression Guide - Lasso Regression Guide

Installation

1. Clone the Repository

git clone https://github.com/NhanPhamThanh-IT/Linear-Lasso-Regression-Car-Price-Prediction.git
cd Linear-Lasso-Regression-Car-Price-Prediction

2. Install Dependencies

Create a virtual environment (optional but recommended):

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

Install required packages:

pip install streamlit scikit-learn pandas matplotlib

3. (Optional) Retrain Models

If you want to retrain the models, open and run model/model-training.ipynb in Jupyter Notebook or VSCode.

Usage

1. Run the App

streamlit run app/main.py

The app will open in your browser at http://localhost:8501.

2. Using the App

Fill in the car features in the input fields
Click With Linear model or With Lasso model to get a prediction
The predicted price will be displayed instantly
Try different inputs and compare model results

Advanced Usage

Custom Port: Run on a different port:

streamlit run app/main.py --server.port 8502

Headless Mode: For deployment:

streamlit run app/main.py --server.headless true

Docker: See docs/streamlit.md for Docker deployment instructions.
Streamlit Cloud: Deploy directly from GitHub for free.
API Integration: (Advanced) Wrap prediction logic in a FastAPI or Flask API for programmatic access.

Project Structure

Linear-Lasso-Regression-Car-Price-Prediction/
├── app/
│   ├── main.py           # Streamlit app entry point
│   ├── predictor.py      # Model loading and prediction logic
│   └── ui.py             # Streamlit UI components
├── dataset/
│   └── car_data.csv      # Car price dataset
├── docs/
│   ├── dataset.md        # Dataset documentation
│   ├── lasso-regression-model.md  # Lasso regression theory & practice
│   ├── linear-regression-model.md # Linear regression theory & practice
│   └── streamlit.md      # Streamlit learning guide
├── model/
│   ├── lasso_model.pkl   # Trained Lasso model
│   ├── linear_model.pkl  # Trained Linear model
│   └── model-training.ipynb # Model training notebook
├── LICENSE
└── README.md

Extending the Project

Want to add new features or support more models? Here are some ideas:

Add More Regression Models: Integrate Ridge, ElasticNet, or custom models.
Feature Engineering: Add new input features or transformations.
Visualization: Show feature importance, residuals, or model diagnostics in the UI.
API Integration: Expose predictions via a REST API for integration with other apps.
Deployment: Deploy to Streamlit Cloud, Heroku, or Docker (see docs/streamlit.md).
UI Enhancements: Add charts, explanations, or user authentication.

How to add a new model:

Train and save your model as a .pkl file.
Update app/predictor.py to load and use the new model.
Add a button or option in app/ui.py for users to select the new model.
Update the README and docs as needed.

Troubleshooting & FAQ

Q: The app doesn't start or crashes on launch.

Make sure all dependencies are installed (pip install streamlit scikit-learn pandas matplotlib).
Check your Python version (should be 3.7+).
Ensure you are running the command from the project root directory.

Q: I get a ModuleNotFoundError for 'streamlit' or 'sklearn'.

Activate your virtual environment if you created one.
Run pip install -r requirements.txt if you have a requirements file.

Q: The prediction is always the same or seems off.

Check that the input features are reasonable and within the expected range.
Make sure the model .pkl files are present in the model/ directory.
Retrain the models if needed using the provided notebook.

Q: How do I deploy this app online?

See deployment instructions in docs/streamlit.md for Streamlit Cloud, Heroku, and Docker.

Q: Can I use my own dataset?

Yes! Replace dataset/car_data.csv with your own data (matching the expected columns), retrain the models, and update the code as needed.

Contributing

Contributions are welcome! To contribute:

Fork the repository
Create a new branch (git checkout -b feature/your-feature)
Make your changes and commit them
Push to your fork (git push origin feature/your-feature)
Open a Pull Request describing your changes

Guidelines:

Write clear, descriptive commit messages
Document new features or changes in the README/docs
Follow PEP8 style for Python code
Add tests or example usage if possible
Please read our Code of Conduct before contributing.

Changelog

See CHANGELOG.md for a list of major changes, new features, and bug fixes.

Learning Resources

Requirements

Python 3.7+
streamlit
scikit-learn
pandas
matplotlib

You can also create a requirements.txt file for easy installation:

streamlit
scikit-learn
pandas
matplotlib

License

This project is licensed under the MIT License.

Acknowledgements

Streamlit
scikit-learn
pandas
matplotlib
UCI Machine Learning Repository (for dataset inspiration)

Contact

For questions, suggestions, or contributions, please contact:

Nhan Pham
Email: ptnhanit230104@gmail.com

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