https://github.com/2003harsh/laptop-price-predictor
"Laptop Price Predictor 🎮🔍 - ML-driven app using Random Forest Regressor. Predict prices based on features like RAM, memory, and processor. Achieved R2 score of 90%. Built with Scikit-learn, Pandas, and Numpy. #MachineLearning #DataScience #Streamlit 🚀"
https://github.com/2003harsh/laptop-price-predictor
machine-learning-algorithms pandas random-forest regression-analysis
Last synced: 2 months ago
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"Laptop Price Predictor 🎮🔍 - ML-driven app using Random Forest Regressor. Predict prices based on features like RAM, memory, and processor. Achieved R2 score of 90%. Built with Scikit-learn, Pandas, and Numpy. #MachineLearning #DataScience #Streamlit 🚀"
- Host: GitHub
- URL: https://github.com/2003harsh/laptop-price-predictor
- Owner: 2003HARSH
- Created: 2023-08-25T05:07:58.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2024-07-19T13:46:13.000Z (almost 2 years ago)
- Last Synced: 2025-01-11T09:47:52.545Z (over 1 year ago)
- Topics: machine-learning-algorithms, pandas, random-forest, regression-analysis
- Language: Jupyter Notebook
- Homepage: https://laptop-price-predictor-2023.streamlit.app/
- Size: 3.08 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Laptop Price Predictor 🚀💻
Welcome to the Laptop Price Predictor, a robust machine learning model using a Random Forest Regressor to provide accurate price predictions based on laptop features. The project is designed to be user-friendly with a Streamlit interface.
### Streamlit UI:
## Key Features
- **Predictive Power:** Achieved an impressive R2 score of 90%.
- **Advanced Techniques:** Utilized Scikit-learn, Pandas, Numpy, Regex, Streamlit, Matplotlib, Seaborn, and more.
- **Streamlined Processing:** Implemented Pipelines for efficient data processing and model building.
- **Deployment:** Access the project through [this link](https://laptop-price-predictor-2023.streamlit.app/).
## How to Use
1. **Clone Repository:**
```
git clone https://github.com/your-username/laptop-price-predictor.git
cd laptop-price-predictor
```
2. **Install Dependencies:**
```
pip install -r requirements.txt
```
3. **Run the Streamlit App:**
```
streamlit run main.py
```
4. **Access the App:**
Open your browser and go to `http://localhost:8501`.
5. **Input:**
Fill in the laptop features (RAM, memory, processor).
6. **Output:**
The app will predict the laptop price based on the provided features.
This project exemplifies the power of machine learning in predicting laptop prices, with an intuitive Streamlit interface for ease of use. #MachineLearning #PricePrediction #LaptopPredictor #Streamlit 🚀💻📊