This repository contains the code and documentation for a machine learning project focused on predicting laptop prices based on their specifications. The project was completed as part of the EE 559 course at University of Southern California.
In today's digital era, laptops have become an essential tool for various tasks, from work to entertainment. However, determining the price of laptops can be challenging due to the proliferation of numerous laptop models in the market, each with its unique features and specifications. This project aims to address this challenge by leveraging machine learning algorithms to predict laptop prices based on their key specifications.
The dataset used in this project is sourced from Kaggle and contains detailed information about various laptops from different manufacturers. Each entry in the dataset includes key specifications such as CPU, company, memory, GPU, RAM, etc., among others, as well as the corresponding price.
The project follows a systematic approach, including data preprocessing, feature engineering, model selection, training, and evaluation. Various machine learning algorithms such as Linear Regression, Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Random Forest, and Neural Networks were explored and evaluated to determine their effectiveness in predicting laptop prices.
The performance of each model was evaluated using metrics such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared (R²). The results indicate that [insert best-performing model] outperformed the other models, achieving the lowest RMSE and MAE and the highest R² score.
To use the code in this repository, follow these steps:
- Clone the repository to your local machine.
- Install the required dependencies listed in the
requirements.txtfile. - Run the main script or notebooks to preprocess the data, train the models, and evaluate their performance.
- Experiment with different algorithms, hyperparameters, and feature engineering techniques to improve the model's performance.
This project is licensed under the MIT License.