📊 Big Data Analytics Project: Amazon Reviews Analysis 📱💻

Welcome to our Big Data Analytics Project! 🚀 This project studies the ever-persistent iOS vs Android debate by analyzing user behavior, sentiments, and preferences using Amazon reviews for cell phones and accessories. 📱✨

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🌟 Project Highlights

• 📁 Dataset: Amazon Reviews - Cell Phones and Accessories (~9GB of big data!)
• 🔍 Focus: Sentiment analysis, user behavior trends, and platform comparisons.
• 🛠️ Technologies:
  • Hadoop 🐘 (HDFS for data storage)
  • PySpark 🔥 (Data processing and analytics)
  • Pandas & Matplotlib 📊 (Data cleaning and visualization)

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🚀 Workflow Overview

1. Data Preprocessing:
   • JSON to CSV conversion.
   • Data cleaning: Missing values, outliers, and invalid timestamps.
   • Feature engineering: Sentiment classification and platform categorization.
2. Data Analysis:
   • Sentiment mapping (positive, neutral, negative).
   • Behavioral analysis of Android vs iOS users.
3. Visualization:
   • Word clouds 🌈
   • Sentiment distribution graphs 📉
   • Time-series trends ⏳

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🖼️ Visual Insights

• iOS Users: Focus on aesthetics and brand loyalty (keywords like "perfect," "fit," "Apple").
• Android Users: Practicality and functionality (keywords like "battery," "Samsung," "work").

🎨 Sample Visualizations:

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🧑‍🤝‍🧑 Contributors

A big shoutout to our amazing team! 🌟

• Sam El Saati 🎯
  • Specialization: Feature engineering & data cleaning, system optimization, and sentiment analysis.
  • 🌐 GitHub: github.com/sams258
• Mohamad Alloush 🌍
  • Specialization: Data cleaning & feature engineering, visualization, and storytelling through analytics.
  • 🌐 GitHub: github.com/alloush95

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🛠️ Challenges & Lessons

• Data Volume: Managing a massive 9GB dataset with limited resources. 🗃️
• System Crashes: Overcame memory limitations with optimized configurations. 🛠️
• Visualization Complexity: Simplified insights for clear and actionable outcomes. 🎨

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💡 Key Takeaways

• Actionable Insights:
  • Android users prioritize functionality and value.
  • iOS users emphasize aesthetics and premium experience.
• Technical Growth:
  • Mastery of Hadoop and PySpark.
  • Hands-on experience with big data tools and techniques.

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📥 How to Use

1. Clone this repository: git clone https://github.com/sams258/Big-Data-Analytics-Project
2. Install dependencies and set up Hadoop and PySpark.
3. Run analysis scripts (Jupyter notebook) and visualize insights.

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📝 References

• Hadoop Documentation
• PySpark Quick Start Guide
• Pandas Documentation
• Amazon Reviews Dataset

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⭐ Don't forget to give this project a star if you find it useful! ✨