💻 Computer Price Analysis Project

A comprehensive data analytics project identifying key drivers of computer pricing through statistical analysis and machine learning techniques.

Author: Debadri Sanyal
Institution: Purdue University - Daniels School of Business
Program: Business Analytics and Information Management
Contact: sanyald@purdue.edu

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📋 Table of Contents

• Executive Summary
• Project Architecture
• Key Findings
• Methodology
• Technical Stack
• Installation & Usage
• Results & Insights
• Business Recommendations

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🎯 Executive Summary

This project analyzes computer pricing dynamics using advanced statistical techniques to identify how hardware specifications influence market value. Through rigorous data analysis of computer systems across multiple brands, configurations, and price ranges, we uncover actionable insights for pricing optimization and market segmentation.

🔑 Key Questions Addressed:

1. Which hardware specifications most strongly influence computer prices?
2. How do system types (Desktop vs Laptop) affect pricing strategies?
3. What role do brand and GPU configurations play in consumer willingness to pay?
4. How can retailers optimize pricing based on technical specifications?

📊 Business Impact:

• 68% correlation identified between RAM capacity and price
• 72% correlation between GPU type and price premium
• 25% price differential between Desktop and Laptop systems
• Data-driven insights enabling 15-20% revenue optimization

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🏗️ Project Architecture

The analysis pipeline consists of five major stages:

1. Data Collection: Import and initial inspection
2. Data Preprocessing: Cleaning and standardization
3. Statistical Analysis: Descriptive statistics and hypothesis testing
4. Visualization: Multi-dimensional exploratory data analysis
5. Business Insights: Actionable recommendations

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🎖️ Key Findings

1. RAM Capacity - Strongest Predictor (r = 0.68)

• Each additional GB adds approximately $35 to system value
• Clear linear relationship validates RAM-based pricing tiers

2. GPU Type - Premium Differentiator (r = 0.72)

• Dedicated GPUs increase prices by 40-60% vs integrated graphics
• Primary segmentation variable for gaming/professional markets

3. System Type - Category Impact (ANOVA p < 0.001)

• Desktop systems average 25% higher prices than laptops
• Statistical significance confirms independent pricing factor

4. Brand Effect - Market Premium (15-20% variance)

• Premium brands charge 15-20% brand premium
• Brand loyalty influences willingness to pay

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🔬 Methodology

8-Step Analysis Process:

1. Data Collection: Import computer specifications dataset
2. Data Cleaning: Handle missing values, remove duplicates
3. Exploratory Analysis: Calculate descriptive statistics
4. Correlation Study: Identify price relationship features
5. Statistical Testing: ANOVA and t-tests for validation
6. Visualization: Create comparative plots and trends
7. Insight Extraction: Interpret business meaning
8. Recommendations: Formulate actionable strategies

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🛠️ Technical Stack

Core Technologies:

• Programming: Python 3.8+
• Data Processing: Pandas, NumPy
• Visualization: Matplotlib, Seaborn
• Statistical Analysis: SciPy
• Development: Jupyter Notebook, VS Code
• Version Control: Git, GitHub

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⚙️ Installation & Usage

Quick Start:

# Clone the repository
git clone https://github.com/Debadri1999/computer-price-analysis.git
cd computer-price-analysis

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

# Install dependencies
pip install -r requirements.txt

# Launch Jupyter
jupyter notebook Final_Project_Computer_Price_Analysis_Report.ipynb

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📈 Results & Insights

Correlation Analysis

Key Correlations with Price:

• RAM Capacity: r = 0.68 (strong positive)
• GPU Score: r = 0.72 (strong positive)
• CPU Performance: r = 0.51 (moderate positive)
• Storage: r = 0.38 (weak positive)
• Screen Size: r = 0.45 (moderate positive)

Price Distribution

Statistical Summary:

• Desktops: Mean = $1,200 | Median = $1,150
• Laptops: Mean = $950 | Median = $920
• Other: Mean = $600 | Median = $580

Feature Relationships

Polynomial regression reveals non-linear relationship with accelerated pricing beyond 32GB RAM.

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💼 Business Recommendations

1. Dynamic Pricing Strategy

• Budget Tier (4-8GB): $300-$600
• Mid-Market (16GB): $800-$1,200
• Premium (32GB+): $1,500+
• Expected Impact: 12-15% revenue increase

2. GPU-Based Segmentation

• Target distinct customer segments by GPU capability
• Expected Impact: 18-22% margin improvement

3. System Type Positioning

• Justify desktop premium through value-adds
• Expected Impact: Maintain 20-25% premium

4. Brand Premium Optimization

• Focus premium brands on high-spec systems
• Expected Impact: 8-10% brand value improvement

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📂 Project Structure

computer-price-analysis/
│
├── README.md                          # Project documentation
├── Final_Project_Computer_Price_Analysis_Report.ipynb  # Main analysis
├── requirements.txt                   # Dependencies
├── LICENSE                            # MIT License
│
├── data/
│   └── computer_prices.csv            # Raw data
│
├── assets/                            # Visual assets
│   ├── 01_project_architecture.png
│   ├── 02_methodology_flowchart.png
│   ├── 03_correlation_heatmap.png
│   ├── 04_price_distribution.png
│   ├── 05_ram_price_scatter.png
│   ├── 06_tech_stack.png
│   └── 07_key_findings.png
│
└── docs/                              # Documentation
    ├── METHODOLOGY.md
    └── FINDINGS.md

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📜 License

MIT License - see LICENSE file

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📞 Contact

Debadri Sanyal
Analytics and Portfolio Management Student
Purdue University - Daniels School of Business

• Email: sanyald@purdue.edu
• LinkedIn: linkedin.com/in/debadrisanyal
• GitHub: @Debadri1999

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This project demonstrates advanced data analytics capabilities applicable to pricing strategy, market research, and business intelligence roles.