This project demonstrates data visualization using Python libraries:

• Matplotlib – for creating line plots, histograms, and customized charts.
• NumPy – for generating and manipulating numerical data.
• Pandas – for handling structured datasets (if used).

• Compare multiple data series.
• Customized colors, markers, line styles, and legends.
• Grid, titles, and axis labels for readability.

plt.plot(arr, label='Series 1', color='#42e9f5', linewidth=3, marker='o')
plt.plot(arr1, label='Series 2', color='purple', linestyle='--', marker='s')
plt.legend()
plt.grid(True)
plt.show()

-Visualize distributions of numerical data. -Adjustable number of bins, colors, transparency, and grid lines.

random_data = np.random.normal(loc=50, scale=10, size=1000)
plt.hist(random_data, bins=60, color='#fcca03', edgecolor='black', alpha=0.7)
plt.title('Histogram of Random Data')
plt.xlabel('Value')
plt.ylabel('Frequency')
plt.grid(True)
plt.show()

• Imported and explored the dataset
• Grouped data using Pandas to calculate summary statistics
• Analyzed patterns across time-related variables
• Created line charts to visualize trends
• Customized plots with:
  • Figure sizing
  • Titles and axis labels
  • Improved readability

The notebook includes:

• Line plots showing aggregated values
• Trend analysis based on grouped data
• Clear labeling for easy interpretation

The purpose of this project is to:

• Practice data manipulation with Pandas
• Improve data visualization skills using Matplotlib
• Build clean, interpretable plots suitable for analysis and portfolios