PricePredictorX : The Scientific guess work

Welcome to the PricePredictorX! This application leverages machine learning techniques to predict the prices of products or services.

        

Table of Contents

• Features
• Technologies Used
• Installation
• Usage
• Data
• Model Training
• Results
• Contributing
• Contact

Features

• Data Preprocessing: Clean and preprocess data for optimal model performance.
• Multiple Algorithms: Experiment with different machine learning algorithms.
• Model Evaluation: Evaluate models using various metrics.
• Visualization: Visualize data and prediction results with charts and graphs.
• User Interface: Simple UI to input data and view predictions.

Technologies Used

• Python
• Pandas
• NumPy
• Scikit-Learn
• Matplotlib
• Streamlit (for web interface)
• Jupyter Notebook
• Linux (for development environment)

 

Installation

1. Clone the repository:

   git clone https://github.com/HarshalMPatil20/PricePredictorX.git

2. Install the required packages:

   pip install -r requirements.txt

 

Usage

1. Preprocess your data:

   python laptop_price_predictor.ipynb

2. Run the app:

   python app.py

3. Open your web browser and go to:

   http://127.0.0.1:8501/
   

4. For same Network :

    Network URL: http://192.168.10.41:8501
   

 

Data

• Ensure your data is in CSV format.

• The dataset should contain relevant features that influence the price.

• Example of a data structure:

  Company	TypeName	Inches	ScreenResolution	Cpu	Ram	Memory	Gpu	OpSys	Weight	Price
  HP	Notebook	15.6	Full HD 1920x1080	Intel Core i5 7200U 2.5GHz	8GB	256GB SSD	Intel HD Graphics 620	windows OS	1.86kg	30636.0000

 

Model Training

• Various machine learning models are supported, including Linear Regression, Decision Trees, and Random Forest.

• Adjust the parameters and experiment with different models in the train.py script.

   

 

Results

• View the model performance metrics in the console.

• Predictions are displayed in the web interface with visualization for better understanding.

     

   

     

   

 

Contributing

I welcome contributions from the community! If you have suggestions, improvements, or additional examples, please feel free to open an issue or submit a pull request. Your feedback and collaboration are highly appreciated :

1. Fork the repository and create a new branch for your contributions.
2. Add your changes (code examples, documentation, etc.) to the appropriate directories.
3. Write clear and concise commit messages.
4. Submit a pull request to merge your changes into the main branch.

For more information on contributing, refer to the CONTRIBUTING.md file in this repository.

 

Contact

If you have any questions, suggestions, or just want to connect, you can reach out to me via:

• Email: Harshalmpatil210@gmail.com
• GitHub Issues: Issues Page

Thank you for your interest in this OOP repository. Happy coding!

Best regards,
Harshal Patil