Revenue trend analysis - Brand Identity - Economics

Handling Year Format: The year data is processed to ensure consistency, particularly for the entry "2022-2023," which is converted to just "2023" for clarity in analysis. Growth Rate Calculation: A new column, "Growth Rate (%)", is added to represent the percentage change in revenue from one year to the next. This is calculated using the pct_change() method, which computes the percentage growth relative to the previous year's revenue.

Statistical Analysis Linear Regression Model: A linear regression model is fitted to the data to predict revenue based on the year. The model helps identify trends in revenue growth over time. Predicted values are generated using the fitted model, which allows for comparison with actual revenue figures.

Visualizations Revenue Trend Plot: A line plot illustrates the actual revenue over the years alongside a trend line derived from the linear regression model. The plot features: Actual revenue points connected by lines (in blue). A dashed red line representing the optimal trend line predicted by the linear regression model.

Axes labeled with appropriate titles and a grid for better readability. Correlation Heatmap: A heatmap visualizes the correlation between year, revenue, and growth rate metrics. The correlation matrix is computed from the relevant columns, providing insights into how these variables relate to one another.

The heatmap uses a color gradient to represent correlation coefficients, with annotations indicating precise values. Conclusion This dataset serves as a valuable resource for analyzing revenue trends over a specified period while employing statistical methods to derive meaningful insights. By cleaning and processing the data effectively, applying linear regression for trend analysis, and visualizing results through plots and heatmaps, this analysis provides a clear understanding of revenue dynamics and growth patterns from 2020 to 2023.