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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.\n\nTechnologies \u0026 Tools -\n- Python  \n- Pandas \u0026 NumPy for data manipulation  \n- Matplotlib \u0026 Seaborn for data visualization  \n- Scikit-learn for machine learning  \n- Jupyter Notebook for interactive coding and presentations  \n- Visual Studio Code (VS Code) as the code editor\n\n\nGeneral EDA Workflow - \n1. **Data Importing** – Load dataset  \n2. **Data Cleaning** – Handle missing values, duplicates, and incorrect formats  \n3. **Exploratory Analysis** – Understand dataset structure, summary statistics  \n4. **Feature Engineering** – Create new useful variables  \n5. **Visualization** – Generate charts, plots, and graphs for insights  \n6. **Insights Extraction** – Highlight important patterns and business conclusions  \n\n\nTypes of Visualizations - \n- Histograms  \n- Bar plots  \n- Scatter plots  \n- Heatmaps (correlation)  \n- Boxplots \u0026 Violin plots  \n- Time-series analysis plots  \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fganesh774218%2Feda-book-store","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fganesh774218%2Feda-book-store","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fganesh774218%2Feda-book-store/lists"}