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Data Visualizations

Here are some ideas to create data visualizations for portfolio.
Time Series Plots: Plot the historical trend of variables alongside each another variables over time. This can help visualize any potential correlations or trends between the variables.
Correlation Matrix: Create a heatmap showing the correlation matrix between variables. This can help identify which indicators have the strongest correlation with variable, aiding in feature selection for modeling.
Feature Importance Plot: If you've used machine learning algorithms for prediction, visualize the feature importance ranking. This can be done using a bar plot or a heatmap to show which economic indicators have the most significant impact on predicting the variable.
Model Performance Comparison: Plot the performance metrics (e.g., mean absolute error, root mean squared error) of different predictive models used in the project. This can be a grouped bar chart or a line plot showing the performance of each model over time or across different evaluation metrics.
Residual Plot: If you've used regression models, create residual plots to visualize the difference between predicted values and actual values. This can help assess the model's performance and identify any patterns or outliers in the residuals.
Predicted vs. Actual Plot: Plot the predicted values against the actual values over time. This visual representation can demonstrate how well your predictive model captures the actual movements.
Distribution Plots: Create distribution plots for each economic indicator to visualize their distributions and identify any outliers or skewness in the data. This can be particularly useful during the data preprocessing stage.
Interactive Dashboard: Combine multiple visualizations into an interactive dashboard using tools like Tableau, Power BI, or Plotly. This allows viewers to explore the data and insights dynamically.
By including these data visualizations in your project, we can effectively communicate our data analysis process, model development, and insights gained from predicting the target variable.

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