Laptop Price Predictor

This is a machine learning project that predicts laptop prices using regression techniques. The project includes heavy data preprocessing, manipulation, and feature engineering. Exploratory data analysis (EDA) was also conducted to gain insights into the data. After testing both machine learning and deep learning algorithms, we decided to use the Random Forest algorithm as our final model.

Installation

To install and run the project, follow these steps:

1. Clone the repository to your local machine.
2. Run the Jupyter notebook: Laptop Price Predictor.ipynb

Note: The processed data, machine learning pipeline, and dataset used in this project are included in the repository.

File Description

• Laptop Price Predictor.ipynb - This Jupyter notebook contains the complete code for this project including data preprocessing, EDA, feature engineering, model building, and evaluation.
• df.pkl - Pickle file of the processed data used in the model.
• pipe.pkl - Pickle file of the machine learning pipeline used in the model.
• laptop_data.csv - The dataset used in this project.

Dependencies

• Python 3.7+
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Scikit-learn

Dataset

The dataset used in this project is 'laptop_data.csv'. It contains information about various laptops such as brand, screen size, processor, RAM, storage, and price. The dataset has 1300 rows and 11 columns. The dataset required extensive data preprocessing, feature engineering, and one-hot encoding.

Usage

To use the laptop price predictor, follow these steps:

1. Run the Jupyter notebook.
2. Load the processed data and machine learning pipeline from the pickle files provided.
3. Enter the laptop features for which you want to predict the price.
4. The system will use the trained model to predict the price and display the result.

Contributing

We welcome contributions to this project. If you want to contribute, please follow these guidelines:

• Fork the repository.
• Create a new branch for your changes.
• Make your changes and commit them with clear commit messages.
• Push your changes to your forked repository.
• Create a pull request with a clear description of your changes.

Contact

If you have any questions or issues, please contact at rishabhvyas472@gmail.com