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https://github.com/burhanahmed1/laptoppricing-machinelearning-analysis
Data Analysis, training Machine Learning models, and Model Evaluation and Refinement for LaptopPricing dataset.
https://github.com/burhanahmed1/laptoppricing-machinelearning-analysis
data-analysis-project data-analytics-project data-aquisition data-wrangling datascience exploratory-data-analysis insights jupyter-notebook machine-learning machine-learning-models matplotlib model-evaluation-and-refinement numpy pandas python scikit-learn scipy-stats seaborn
Last synced: 9 days ago
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Data Analysis, training Machine Learning models, and Model Evaluation and Refinement for LaptopPricing dataset.
- Host: GitHub
- URL: https://github.com/burhanahmed1/laptoppricing-machinelearning-analysis
- Owner: burhanahmed1
- License: mit
- Created: 2024-07-06T05:26:42.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2024-07-06T14:00:52.000Z (5 months ago)
- Last Synced: 2024-07-07T06:46:18.374Z (5 months ago)
- Topics: data-analysis-project, data-analytics-project, data-aquisition, data-wrangling, datascience, exploratory-data-analysis, insights, jupyter-notebook, machine-learning, machine-learning-models, matplotlib, model-evaluation-and-refinement, numpy, pandas, python, scikit-learn, scipy-stats, seaborn
- Language: Jupyter Notebook
- Homepage:
- Size: 95.7 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# LaptopPricing MachineLearning Analysis
## 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:
```bash
git clone https://github.com/burhanahmed1/LaptopPricing-MachineLearning-Analysis.git
cd LaptopPricing-MachineLearning-Analysis
pip install -r requirements.txt
```
## Usage
Open the Jupyter notebook:
```bash
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
**R^2** scores of the **Linear Regression** model created using different degrees of polynomial features, ranging from 1 to 5.
**R^2** values of **Ridge Regression** model for training and testing sets with respect to the values of alpha.
## Contributing
Contributions are welcome! Please fork this repository and submit pull requests.
## License
This project is licensed under the MIT License.