Laptop-Price-Predictor

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.

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Repository Under: AcWoC'25

Club: Android Club, VIT Bhopal University

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Dataset Overview

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

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Modules Required

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>

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Approach and Methodologies

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.

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Future Possibilities

• 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.