💻 Laptop Data Visualization

This project explores a dataset of laptops with attributes such as brand, CPU, RAM, storage, GPU, operating system, and price.
The goal is to transform raw data into clear, compelling visualizations that reveal insights and support decision-making.

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🎯 Project Motivation

Data visualization is a critical skill for analysts and support engineers. This project demonstrates:

• Cleaning and transforming real-world datasets with Pandas.
• Creating impactful charts using Matplotlib and Seaborn.
• Crafting data stories that highlight trends and anomalies.
• Structuring a GitHub-ready portfolio project.

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📊 Dataset Overview

• Rows: ~200 laptops
• Columns: Company, TypeName, Inches, ScreenResolution, CPU, RAM, Memory, GPU, OpSys, Weight, Price
• Source: Laptop specifications dataset (CSV format)

Key features include:

• Company → Brand (Apple, Dell, HP, etc.)
• RAM → Memory size in GB
• Storage → SSD, HDD, or Flash
• Price → Target variable for analysis

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📈 Visualizations & Insights

1. Price Distribution

   • Most laptops fall between ₹20,000–₹60,000.
   • Premium brands (Apple, Razer) dominate the higher end.

2. Average Price by Brand

   • Apple laptops with SSDs are consistently higher priced.
   • Acer and HP show more variation between HDD and SSD models.

3. RAM vs Price (Scatterplot)

   • Strong positive correlation: higher RAM generally means higher price.

4. Storage Type vs Price

   • SSD laptops are more expensive across all brands.
   • Gaming brands (MSI, Asus ROG, Dell Gaming) cluster at higher prices.

5. Correlation Heatmap

   • Price correlates most strongly with RAM and storage type.
   • Weight and screen size show weaker correlations.