https://github.com/efficientgoose/laptop-price-predictor

"Laptop Price Prediction" is a GitHub repository containing codes and datasets for a machine learning model that estimates the price of a laptop based on its specifications.
https://github.com/efficientgoose/laptop-price-predictor

linear-regression machine-learning ml-project regression regression-analysis

Last synced: 15 days ago
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"Laptop Price Prediction" is a GitHub repository containing codes and datasets for a machine learning model that estimates the price of a laptop based on its specifications.

• Host: GitHub
• URL: https://github.com/efficientgoose/laptop-price-predictor
• Owner: efficientgoose
• Created: 2023-03-17T12:34:59.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2023-03-18T16:29:41.000Z (almost 2 years ago)
• Last Synced: 2024-10-29T19:57:59.612Z (3 months ago)
• Topics: linear-regression, machine-learning, ml-project, regression, regression-analysis
• Language: Jupyter Notebook
• Homepage: https://ajinkode-laptop-price-predictor-app-49ikqt.streamlit.app/
• Size: 2.12 MB
• Stars: 3
• Watchers: 1
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Laptop Price Predictor Repository

[![Streamlit App](https://static.streamlit.io/badges/streamlit_badge_black_white.svg)](https://ajinkode-laptop-price-predictor-app-49ikqt.streamlit.app/)

This repository contains code, dataset and all other required files for a machine learning project that predicts the price of a laptop based on its specifications. The user can input details about the laptop such as the brand, processor, RAM, storage, and screen size, and the program will estimate the cost of the laptop.

### Here's how the project works:

1. Follow this link: https://ajinkode-laptop-price-predictor-app-49ikqt.streamlit.app/

2. Input your desired specifications for the laptop like HDD, SSD, RAM, Screen Size, etc.

3. Click the "Predict Price" Button at the bottom.

Voila! The model has estimated a price for the laptop with giiven specifications.

## Contents of the Repository

## 1. Dataset

The dataset required for this project in .csv format.

## 2. Jupyter Notebook 

The Jupyter Notebook in which the model was trained using a regression model in .ipynb file format.

## 3. Pickle Files

The pickle files which contain the dataframe and trained model.

## 4. Python Script File

The python script containing the code for streamlit app.

## 5. Requirements File

The requirements and dependencies needed for this project.

## Contributing

If you would like to contribute to this project, you can fork the repository and submit a pull request with your changes. Please make sure to test your changes thoroughly before submitting the pull request.

### Thanks for visiting!

I would like to take a moment to express my gratitude for visiting my repository on GitHub. Your interest in my work means a lot to me and encourages me to continue contributing to the community. If you have any questions or feedback, please do not hesitate to contact me here: [email protected]