https://github.com/ganesh774218/eda-book-store
Exploratory data analysis on a book store dataset to uncover sales trends, popular genres, and top publishers.
https://github.com/ganesh774218/eda-book-store
data-visualization datacleaning datamanipulation eda matplotlib numpy pandas python pythonp pythonproject seaborn
Last synced: 2 months ago
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Exploratory data analysis on a book store dataset to uncover sales trends, popular genres, and top publishers.
- Host: GitHub
- URL: https://github.com/ganesh774218/eda-book-store
- Owner: Ganesh774218
- Created: 2025-09-10T08:27:33.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2025-09-10T08:53:38.000Z (10 months ago)
- Last Synced: 2025-09-10T12:03:17.764Z (10 months ago)
- Topics: data-visualization, datacleaning, datamanipulation, eda, matplotlib, numpy, pandas, python, pythonp, pythonproject, seaborn
- Language: Jupyter Notebook
- Homepage:
- Size: 742 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# EDA-Book-Store
Introduction -
This project performs exploratory data analysis (EDA) on a Book Store dataset to reveal sales trends, popular genres, and top publishers. It examines the impact of language, genre, and ratings on sales and revenue. The dataset contains details like publishing year, author, ratings, and sales figures. Python libraries such as Pandas, NumPy, Matplotlib, and Seaborn are used for data analysis and visualization. The insights support informed decisions in marketing and inventory management.
Technologies & Tools -
- Python
- Pandas & NumPy for data manipulation
- Matplotlib & Seaborn for data visualization
- Scikit-learn for machine learning
- Jupyter Notebook for interactive coding and presentations
- Visual Studio Code (VS Code) as the code editor
General EDA Workflow -
1. **Data Importing** – Load dataset
2. **Data Cleaning** – Handle missing values, duplicates, and incorrect formats
3. **Exploratory Analysis** – Understand dataset structure, summary statistics
4. **Feature Engineering** – Create new useful variables
5. **Visualization** – Generate charts, plots, and graphs for insights
6. **Insights Extraction** – Highlight important patterns and business conclusions
Types of Visualizations -
- Histograms
- Bar plots
- Scatter plots
- Heatmaps (correlation)
- Boxplots & Violin plots
- Time-series analysis plots