A machine learning-powered web application that predicts laptop prices based on specifications like brand, RAM, processor type, screen resolution, and more. Built with Python and deployed using Streamlit, it delivers real-time price predictions based on user inputs.
- Predicts laptop prices based on various specs
- Interactive UI with Streamlit
- Trained regression model using real-world data
- Handles features like:
- Laptop brand and type
- RAM size
- Touchscreen and IPS display
- Screen size and resolution
- Processor, storage, and GPU
- Operating System
- Python
- Pandas, NumPy, Scikit-learn
- Jupyter Notebook
- Streamlit
- Pickle (for saving the trained model)
-
Data Preprocessing
Cleaned and formatted data, handled missing values. -
Feature Engineering
Extracted relevant specs, encoded categorical variables. -
Model Training
Trained a regression model to predict laptop prices. -
Deployment
Integrated the trained model into a Streamlit app for real-time predictions.
| File/Folder | Description |
|---|---|
LaptopPricePredictor.ipynb |
Notebook for EDA, preprocessing, and model training |
app.py |
Streamlit app script for live predictions |
pipe.pkl |
Serialized machine learning pipeline |
df.pkl |
Processed dataset used in the application |
Input your desired laptop specs in the Streamlit UI and get an estimated price instantly.
Contributions are welcome!
Feel free to open issues or submit pull requests to enhance the project.
This project is licensed under the MIT License.