Laptops Cleaned Dataset (India, 2025)

💻 Project Overview

This repository contains the complete workflow behind building a clean, structured, and analysis-ready laptop dataset for the Indian market.
The project starts from raw scraped HTML and unprocessed data and ends with a fully curated dataset of 992 laptops, suitable for data analysis, machine learning, and price prediction.

The goal of this project is to demonstrate real-world data engineering and data cleaning skills, with special emphasis on parsing complex CPU and GPU specifications.

📋 What This Project Solves

Public laptop data available online is often:

• Unstructured with complex processor naming conventions (Intel Core Ultra, AMD Ryzen AI, Apple M-series)
• Missing standardized GPU information (integrated vs dedicated, VRAM)
• Difficult to parse for hybrid-core CPU architectures (P-cores, E-cores, LP E-cores)
• Hard to use directly for analysis or machine learning

This project transforms that raw data into a reliable, machine-readable dataset by applying systematic cleaning, validation, and advanced feature engineering techniques.

📂 Repository Structure

laptops-dataset/
├── LICENSE
├── README.md
├── requirements.txt
│
├── data/
│   ├── raw/
│   │   └── laptops_raw.csv          # Original scraped data
│   └── processed/
│       └── laptops_cleaned.csv      # Cleaned dataset (992 laptops)
│
├── images/                           # Visualizations & assets
│
├── notebooks/
│   ├── 01_data_cleaning.ipynb       # Data cleaning & feature engineering
│   └── 02_eda.ipynb                 # Exploratory data analysis
│
├── scraped/
│   └── smartprix_laptops.html       # Raw scraped HTML
│
└── scripts/
    └── scrape.py                    # Selenium-based scraper

🧩 Data Pipeline Explained

1️⃣ Data Collection → scripts/scrape.py

• Raw laptop listings were scraped from Smartprix.com (Indian marketplace)
• Used Selenium with Chrome WebDriver for dynamic page loading
• Infinite scroll automation to load all laptop listings
• Collected 1,000+ laptop entries with raw specifications

Technology Stack:

• selenium - Dynamic web scraping
• Infinite scroll handling
• HTML saved for reproducibility

Files:
scripts/scrape.py → scraped/smartprix_laptops.html

2️⃣ Data Cleaning → notebooks/01_data_cleaning.ipynb

This is the most intensive phase with 200+ lines of cleaning logic.

Key Operations:

• Removed currency symbols from prices
• Extracted brand from model names
• Cleaned RAM/Storage values (converted TB to GB)
• Handled missing values systematically
• Standardized OS names

Validation Rules Applied:

• Valid CPU required (Intel, AMD, Apple, Qualcomm)
• Price range validation
• Display size validation
• Warranty data normalization

Files:
data/raw/laptops_raw.csv → notebooks/01_data_cleaning.ipynb → data/processed/laptops_cleaned.csv (992 rows)

3️⃣ Feature Engineering → notebooks/01_data_cleaning.ipynb

Transformed messy text columns into 27 well-defined features:

CPU Parsing (Complex):

• Brand: Intel, AMD, Apple, Qualcomm
• Family: Core, Ryzen, M-Series, Celeron
• Series: Core i5, Ryzen 7, Core Ultra 7, M4 Pro
• Model: 13420H, 7530U, M4 Pro
• Suffix: U, H, HX, Pro, Max
• Core Details: cpu_core_count, cpu_thread_count, cpu_p_cores, cpu_e_cores, cpu_lp_e_cores

GPU Parsing:

• Brand: NVIDIA, AMD, Intel, Apple, Qualcomm
• Series: GeForce RTX, Radeon, Iris Xe, Apple GPU
• Model: RTX 4060, Radeon Graphics, Apple 10-Core GPU
• VRAM: Dedicated GPU memory in GB
• Type: Integrated vs Dedicated classification

Other Features:

• Display: display_width_px, display_height_px, display_size_inch
• Memory: ram_gb, storage_gb
• System: os_name, warranty_years
• Category: device_category (Gaming, Business, Thin & Light, General)

4️⃣ Exploratory Data Analysis → notebooks/02_eda.ipynb

Interactive visualizations to understand:

• Price distribution across brands
• CPU/GPU market share
• Device category analysis
• RAM and storage trends
• Brand positioning in market segments

📊 Final Dataset Highlights

Dataset Statistics

• 992 laptop models (cleaned and validated)
• 27 engineered features (from raw columns)
• 15+ brands (Lenovo, HP, Dell, Asus, Acer, Apple, etc.)
• Price range: ₹12,490 - ₹605,990

Feature Categories

Market Statistics

• Intel: 71% CPU market share
• AMD: 23% CPU market share
• NVIDIA: 29% of GPUs
• Integrated GPUs: 70% of laptops
• Windows: 94% of operating systems

Data Quality Improvements

✅ Extracted brand from model names
✅ Parsed complex processor names (Intel Core Ultra, AMD Ryzen AI, Apple M-series)
✅ Extracted numeric core counts from text (e.g., "20 Cores (8P + 12E)")
✅ Classified GPU as integrated vs dedicated
✅ Converted TB storage to GB
✅ Standardized OS names
✅ Handled missing warranty values

🚀 Potential Use Cases

Machine Learning

• Price prediction using regression models
• Feature importance analysis for pricing strategy
• Brand positioning clustering
• Value-for-money recommendation systems

Data Analysis

• Market trend analysis across brands
• Hardware spec evolution over time
• CPU/GPU performance vs price analysis
• Gaming laptop vs business laptop comparison

Portfolio Projects

• EDA dashboards (Plotly, Streamlit, Dash)
• Interactive price prediction apps
• Teaching real-world data cleaning workflows
• Practice dataset for beginners

🔗 Kaggle Dataset

The cleaned dataset is published on Kaggle:

👉 Laptops Cleaned Dataset

🛠️ Tech Stack & Dependencies

Core Libraries

pandas          # Data manipulation
numpy           # Numerical operations
jupyter         # Interactive notebooks
regex           # Pattern matching for CPU/GPU parsing

Installation

# Clone the repository
git clone https://github.com/abhinavflac/laptops-specs-dataset.git
cd laptops-specs-dataset

# Create virtual environment (optional)
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Run the notebooks
jupyter notebook notebooks/

🧠 What This Project Demonstrates

Technical Skills

✅ Web Scraping - Selenium automation with infinite scroll handling
✅ Data Cleaning - 200+ lines of cleaning logic
✅ Regex Mastery - Complex pattern extraction for CPU/GPU specs
✅ Feature Engineering - Text to structured data transformation
✅ Data Validation - Business rule implementation for quality assurance
✅ ETL Pipeline - End-to-end reproducible workflow

Data Science Best Practices

✅ Reproducible pipeline with clear documentation
✅ Raw data preservation (never modify source data)
✅ Systematic missing value handling
✅ Feature engineering guided by domain knowledge
✅ Data quality validation at each stage

This repository reflects practical data science work, where 80% of effort goes into understanding and preparing the data correctly, not just model building.

📜 License

This project is released under the CC0 (Public Domain) license.
You are free to use, modify, and distribute the data and code without restriction.

🤝 Contributions & Feedback

Suggestions, issues, or improvements are always welcome!
If you use this dataset in a project, feel free to share your work — I'd love to see it.

Possible Enhancements

• Add more brands (international markets)
• Include battery specifications
• Add weight and dimensions
• Include launch dates for trend analysis
• Expand connectivity features (WiFi, Bluetooth versions)

📧 Contact

For questions or collaboration:

• Kaggle: githubmasterin
• GitHub: @abhinavflac

⭐ If this project helped you, consider giving it a star!