https://github.com/annaanastasy/linear-regression-laptop-price-prediction
This project predicts laptop prices using regression analysis based on features like brand, processor, RAM, storage, and other specifications, providing a beginner-friendly introduction to data preprocessing and modeling.
https://github.com/annaanastasy/linear-regression-laptop-price-prediction
exploratory-data-analysis linear-models linear-regression machine-learning price-prediction
Last synced: 6 months ago
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This project predicts laptop prices using regression analysis based on features like brand, processor, RAM, storage, and other specifications, providing a beginner-friendly introduction to data preprocessing and modeling.
- Host: GitHub
- URL: https://github.com/annaanastasy/linear-regression-laptop-price-prediction
- Owner: AnnaAnastasy
- Created: 2023-06-02T16:27:21.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-12-04T16:28:39.000Z (10 months ago)
- Last Synced: 2025-03-29T03:44:41.546Z (6 months ago)
- Topics: exploratory-data-analysis, linear-models, linear-regression, machine-learning, price-prediction
- Language: Jupyter Notebook
- Homepage: https://www.kaggle.com/code/annastasy/laptop-price-predictions-beginner-friendly
- Size: 1.52 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Laptop Price Prediction
## Project Overview
This project aims to predict laptop prices based on various features such as brand, processor type, RAM, storage capacity, and other specifications. Using regression techniques, the notebook provides insights into how different laptop attributes influence pricing, making it an excellent starting point for beginners in data science.
## Target Audience
This project is designed for beginners in data science looking to learn how to preprocess data, build regression models, and evaluate their performance.
## Technologies Used
The project leverages the following Python libraries:
- `pandas`
- `numpy`
- `matplotlib`
- `seaborn`
- `scipy`
- `scikit-learn`
## Dataset Details
The dataset contains information about laptops, including:
- **Features**: Brand, processor type, RAM, storage capacity, graphics card, weight, operating system, warranty, and more.
- **Target**: Price of laptops.
- **Other Details**: Includes additional metadata such as ratings and reviews.
## Instructions
### Installation
Before running the notebook, ensure you have Python installed along with the necessary libraries. You can install the required libraries using the following command:
```bash
pip install pandas numpy matplotlib seaborn scikit-learn scipy
```
## Results
- **Model Performance**:
- R² on training data: **0.8448**
- R² on testing data: **0.8169**
- Residual analysis confirms that the residuals are normally distributed, indicating a well-fitted model.
## Acknowledgments
This project uses data sourced from [Kaggle](https://www.kaggle.com/code/annastasy/laptop-price-predictions-beginner-friendly).