This project focuses on analyzing laptop prices using a dataset of 1,300 laptop models. Through exploratory data analysis and the application of machine learning algorithms, it aims to uncover patterns and predict prices based on various configurations.
The application predicts laptop prices using a dataset of 1,300 laptop models. The dataset includes:
- Company Name
- Product Name
- Laptop Type
- Screen Size and Resolution
- CPU Model
- RAM Characteristics
- Storage Capacity
- GPU Details
- Operating System
- Weight
- Price
Installation of the following moldules is required beforehand:
- numpy
- pandas
- seaborn
- matplotlib
- sklearn
- xgboost
- pickle
To install them, open a new terminal and run the command: pip install <module_name>
The project employs both supervised and unsupervised machine learning algorithms for analysis and predictions. The following algorithms have been implemented:
- Linear Regression
- Ridge Regression
- Lasso Regression
- k-Nearest Neighbors (kNN)
- Support Vector Machines (SVM)
- Decision Tree
- Random Forest
- Gradient Boosting
These algorithms were applied to the dataset to extract insights, compare performances, and determine the most suitable model for price prediction. Detailed analysis and visualization were performed in a Jupyter Notebook to ensure a robust evaluation process.
- Business Applications: The model can be refined and used to assist businesses in setting competitive prices for laptops based on market trends and configurations.
- User Input Program: Develop a user-friendly application where users can input desired laptop specifications to get an estimated price in real-time.
- Web Application Deployment: Integrate the model into a web application using frameworks like Streamlit or Flask for broader accessibility.
- Integration with E-commerce: Partner with e-commerce platforms to provide dynamic price recommendations for laptops listed on their websites.
- Expanded Dataset: Enhance the dataset by including more laptop models, regional pricing, and brand-specific trends for better predictions.
- Real-Time Updates: Incorporate APIs to fetch real-time data on market prices and hardware trends for dynamic predictions.
- Recommendation System: Build a system that not only predicts prices but also recommends laptops based on user preferences and budget constraints.