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.
- 📥 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.
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
git clone https://github.com/yourusername/google-data-analysis.git
cd google-data-analysispip install -r requirements.txt
⚠️ Make sure you have Jupyter installed. You can install it via:pip install notebook
jupyter notebookOpen Goggle Data Analysis.ipynb and run the cells to perform the analysis.
- Python 3.x
- Jupyter Notebook
- Pandas – Data manipulation and analysis
- Matplotlib / Seaborn – Data visualization
- Plotly – Interactive plots
- Correlation heatmaps
- Category-wise rating distributions
- Install trends
- Outlier detection in app size and rating
This project is open-source under the MIT License.
Contributions are welcome!
Feel free to fork this repository, enhance the notebook, or fix issues via pull requests.