Exploratory Data Analysis (EDA):

Compute Basic Statistics:

Mean, Median, Standard Deviation:

For each important economic indicator, compute the mean (average), median (middle value), and standard deviation
(a measure of data spread). These statistics measure variability and a central trend in your data.

Visualize Trends Over Time:

Line Charts or Time-Series Plots:

• Plot each economic statistic on a monthly basis from January 2013 to December 2019.
• Recognize patterns, peaks, and troughs. This graphical format aids comprehension of the general behavior of
  economic data.

Identify Seasonality or Patterns:

Seasonal Decomposition:

• To find seasonal components in data, use approaches such as seasonal decomposition of time series (e.g., with Python tools such as ‘statsmodels’).
• Look for repeating patterns or cycles at regular intervals.

Feature Engineering:

Create New Features:

Calculate Growth Rate:

• Develop a new capability for calculating the growth rate of economic indicators. This can be expressed as a
  percentage change from one period to the next.
• Growth rates can reflect the pace at which economic indices rise or fall.

Aggregating Data:

• To acquire a higher-level picture, aggregate the data by quarters or years.
• This can aid in smoothing out data noise and identifying long term patterns.

Correlation Analysis:

Explore Relationships:

Correlation Matrices:

• Determine the correlation coefficients (e.g., Pearson and Spearman) between two economic indices.
• A correlation matrix can assist in comprehending the strength and direction of correlations. A near to 1 number implies a high positive connection, -1 is a strong negative correlation, and 0 is no correlation.

Scatter Plots:

• Create scatter plots for pairs of economic indicators to inspect their relationships visually.
• Looking for linear or non-linear patterns in the scatter plots.