Google Data Analysis 📊

⭐ Overview

This project presents an exploratory data analysis (EDA) of a Google-related dataset using Python. It covers data cleaning, transformation, visualization, and insight extraction. The goal is to derive meaningful trends and patterns from the dataset that can support better decision-making.

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🚀 Features

• 📥 Data Loading: Load and inspect structured data from CSV/Excel files.
• 🧹 Data Cleaning: Handle missing values, duplicates, and inconsistent types.
• 📊 Exploratory Data Analysis:
  • Descriptive statistics
  • Correlation analysis
  • Group-based aggregations
• 📈 Visualizations:
  • Bar charts, histograms, pie charts
  • Heatmaps for correlations
  • Line graphs and distribution plots
• 📌 Insight Extraction: Highlights key findings, anomalies, and patterns in user behavior, ratings, installs, and more.

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📁 File Structure

google-data-analysis/
│
├── Goggle Data Analysis.ipynb   # Main Jupyter notebook
├── dataset.csv (optional)       # Dataset used (add if available)
├── README.md                    # Project description
└── requirements.txt             # Python dependencies

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📦 Installation

1️⃣ Clone the Repository

git clone https://github.com/yourusername/google-data-analysis.git
cd google-data-analysis

2️⃣ Install Dependencies

pip install -r requirements.txt

⚠️ Make sure you have Jupyter installed. You can install it via:

pip install notebook

3️⃣ Run the Notebook

jupyter notebook

Open Goggle Data Analysis.ipynb and run the cells to perform the analysis.

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🛠️ Tech Stack

• Python 3.x
• Jupyter Notebook
• Pandas – Data manipulation and analysis
• Matplotlib / Seaborn – Data visualization
• Plotly – Interactive plots

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📊 Sample Visualizations

• Correlation heatmaps
• Category-wise rating distributions
• Install trends
• Outlier detection in app size and rating

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📄 License

This project is open-source under the MIT License.

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🤝 Contributing

Contributions are welcome!
Feel free to fork this repository, enhance the notebook, or fix issues via pull requests.

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