CompuWorth: Hardware–Price Regression for Laptop Valuation

Introduction

This repository contains the analysis and machine learning model implementation for the laptop-pricing dataset. The goal is to predict various price of laptops having various attributes using different machine learning techniques.

Table of Contents

1. Data Import and Cleaning
2. Exploratory Data Analysis (EDA)
3. Model Evaluation
4. Over-fitting, Under-fitting, and Model Selection
5. Ridge Regression
6. Grid Search

Technologies Used

• Programming Language: Python
• Libraries: Pandas, NumPy, Scikit-Learn, Matplotlib, Seaborn
• Tools: Jupyter Notebook

Getting Started

To get started with this project, clone the repository and install the necessary dependencies:

git clone https://github.com/burhanahmed1/LaptopPricing-MachineLearning-Analysis.git
cd LaptopPricing-MachineLearning-Analysis
pip install -r requirements.txt

Usage

Open the Jupyter notebook:

jupyter notebook LaptopPricing-ML.ipynb

Dataset

The dataset used in this analysis is LaptopPricing.csv, which contains various features related to laptops such as CPU_frequency, RAM_GB, Storage_GB_SSD , CPU_core , OS , GPU, Category and price.

R^2 scores

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R^2 scores of the Linear Regression model created using different degrees of polynomial features, ranging from 1 to 5. <img src="src/R2_1.png" alt="R2_polynomial-features" width="800"/>

R^2 values of Ridge Regression model for training and testing sets with respect to the values of alpha. <img src="src/R2_2.png" alt="R2_for-alphas" width="800"/>

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Contributing

Contributions are welcome! Please fork this repository and submit pull requests.

License

This project is licensed under the MIT License.