type of viz.txt

Each type of data visualization serves a specific purpose and is suitable for different scenarios:

1. Bar plot:
   - Use when you want to compare categorical data or display the distribution of a single categorical variable.
   - Suitable for visualizing counts, frequencies, or proportions of different categories.
   - Examples: Comparing sales of different products, displaying the distribution of survey responses.

2. Pie chart:
   - Use when you want to show the proportion of different categories relative to the whole.
   - Suitable for visualizing parts of a whole or percentages.
   - Best for a small number of categories (around 6-8) to avoid clutter.
   - Examples: Market share of different companies, percentage distribution of expenses.

3. Histogram:
   - Use when you want to visualize the distribution of a continuous variable.
   - Suitable for exploring the shape, spread, and central tendency of data.
   - Helpful in identifying patterns such as skewness or bimodality in the data.
   - Examples: Distribution of exam scores, income levels, or product prices.

4. Box plot:
   - Use when you want to compare the distribution of a continuous variable across different categories.
   - Suitable for identifying outliers, variations, and central tendency in data.
   - Effective for detecting differences or similarities between groups.
   - Examples: Comparing test scores of students from different schools, analyzing sales performance of products from different regions.

5. Scatter plot:
   - Use when you want to explore the relationship between two continuous variables.
   - Suitable for identifying correlations, clusters, or trends in the data.
   - Useful for detecting outliers or influential data points.
   - Examples: Investigating the relationship between height and weight, analyzing the correlation between advertising spend and sales.

6. Heatmap:
   - Use when you want to visualize the correlation or relationships between multiple variables.
   - Suitable for exploring complex datasets with multiple numerical variables.
   - Helpful for identifying patterns and clusters in large datasets.
   - Examples: Correlation between different economic indicators, exploring patterns in customer behavior with multiple metrics.