📱 Smartphone Usage and App Trends

🎯 Project Overview

Smartphones have revolutionized how people communicate, work, and entertain themselves. With millions of apps on app stores, analyzing their usage patterns reveals deep insights into user behavior, technology adoption, and digital ecosystems.

This project explores Google Play Store app data to uncover trends in app categories, downloads, pricing strategies, and user engagement. Through data preprocessing, exploratory analysis, and visualization, we aim to identify what drives app popularity and satisfaction.

Dataset Source: Google Play Store Apps Dataset

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👩‍💻 Team Members

• Pintu Singh (Team Lead)
• Samyak mittal
• Suyash Parmar

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📂 Project Structure

googleplaystore-analysis/
│
├── data/
│   └── googleplaystore.csv
│
├── notebooks/
│   └── phase1_analysis.ipynb
│
├── reports/
│   └── data_summary.txt
│
├── README.md
├── requirements.txt
└── .gitignore

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🧠 Phase 1 Goals

• Create GitHub repository and divide roles clearly.
• Upload dataset and set up working environment (Kaggle or Jupyter).
• Perform initial Exploratory Data Analysis (EDA):
  • Dataset overview (info(), describe())
  • Check missing and duplicate values
  • Identify categorical and numerical columns
  • Document early insights
• Generate a summary report (data_summary.txt)

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🔍 Future Phases

1. Phase 2 — Data Cleaning & Preprocessing

   • Handle missing values, duplicates, and outliers
   • Convert data types and clean formats (Price, Installs, Size)

2. Phase 3 — Exploratory Analysis & Visualization

   • Category-wise trends and download patterns
   • Correlation between ratings, installs, and price
   • Regression/classification models to predict app popularity
   • Visualize model performance and insights

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⚙️ Tools & Libraries

• Python 3.x
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Scikit-learn (for later phases)

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Expected Outcomes

• Understand how different app categories perform over time.
• Analyze the effect of monetization strategies on user satisfaction.
• Explore relationships between ratings, installs, and app features.
• Build predictive models for app success.

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Credits

This project is conducted as part of the Data Mining Course Project (Phase 1) — analyzing and interpreting real-world app data.