PC Price Prediction Model is a solution that harnesses machine learning to anticipate the potential price of personal computers. Leveraging the capabilities of scikit-learn and data analytics, this model can aid businesses and consumers in understanding and forecasting PC market trends.
- Train a predictive model on historical PC price data.
- Assess model performance using various metrics.
- Employ a range of
scikit-learnmachine learning algorithms for superior accuracy. - Fine-tune and hone model parameters.
- Predict future PC prices based on historical and current data.
- Python 3.7 or higher.
scikit-learnlibrary.- Basic grasp of machine learning principles.
- Clone this repository:
git clone https://github.com/admistdebug/PC-Price-Prediction-Model.git
- Navigate to the project directory:
cd "PC Price Prediction Model"
-
Arrange your dataset as per the format specified in the
*.csvfile. -
Launch the IPython notebook to observe the implementation:
Jupyter Notebook "PC Price Prediction Model.ipynb"
- Fork the project.
- Initiate your feature branch (
git checkout -b feature/BrilliantFeature). - Commit your modifications (
git commit -m 'Introduce some BrilliantFeature'). - Upload to the branch (
git push origin feature/BrilliantFeature). - Submit a pull request.
For significant modifications, kindly initiate an issue first to discuss your proposed alterations.
This initiative is protected under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0) - please refer to the LICENCE file for comprehensive details.
- scikit-learn for the tools enabling the crafting of this machine learning model.
- Machine Learning Mastery for educational resources and industry standards.
- A heartfelt thank you to all the collaborators who've participated in the enhancement and refinement of this initiative!
Should you stumble upon any challenges or have queries, don't hesitate to raise an issue or get in touch with the curators. All feedback and contributions are highly valued!