To analyze relationships between variables within a dataset containing economic indicators i have calculated the correlation coefficients and visualized them through a heatmap.
First computed a correlation matrix, which details the pairwise correlations among all columns in the DataFrame. Each cell in the matrix represented the correlation coefficient between two variables, signifying the strength and direction of their linear relationship. Correlation coefficients range from -1 (perfect negative correlation) to 1 (perfect positive correlation), with 0 indicating no linear relationship.
Using sns.heatmap() function generated a heatmap using the correlation matrix data obtained. The heatmap offered a visual representation of correlations, employing a color spectrum to depict the strength of relationships. Annotations within each cell displayed the correlation coefficient values, aiding in the interpretation of the heatmap.
The resulting heatmap served a powerful visualization tool, enabling quick identification of strong positive or negative correlations (values closer to 1 or -1) and weak correlations (values closer to 0). This visual representation assisted me in understanding complex relationships between economic indicators, facilitating informed decision-making processes such as feature selection, identifying multicollinearity, or guiding further analysis and modeling.