📊 Data Distribution Visualization

This project visualizes the distribution of variables such as age (continuous) and gender (categorical) in a dataset using Python. The visualizations are created using powerful libraries such as Pandas, Matplotlib, and Seaborn.

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📌 Table of Contents

• Features
• Technologies Used
• Usage
• Visualizations

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✅ Features

• Generate histograms for continuous variables (e.g., Age).
• Create bar charts for categorical variables (e.g., Gender).
• Combine histograms and KDE plots for better distribution insights.
• Visualize grouped distributions using Seaborn (e.g., Age by Gender).
• Responsive and customizable plots for exploratory data analysis.

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🛠 Technologies Used

• Python 3.x
• Pandas – For data manipulation and loading.
• Matplotlib – For basic plotting.
• Seaborn – For advanced, aesthetic visualizations.
• Jupyter Notebook – For interactive data exploration (optional).

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🚀 Usage

• Open the Jupyter Notebook
• Open the file
• Run the cells

📊 Visualizations

• Histogram of Age
• Bar Chart of Gender
• Grouped Histogram (Age by Gender)