Use the following Daily Gold Price on World dataset with different currencies (available at Kaggle.com). It contains historical gold prices (end of year) from 1979 to 2022.

Create a Jupyter notebook file that includes Python code to do the following:

1. Read the data as a dataframe.

2. Summarize the observations from the basic statistics you apply on the dataset. (Number of columns/rows; number of missing values -if any; datatype of each column; measures of central tendency and variability; etc).

3. Convert data in each column into an appropriate data type.

4. Create visualizations that show the distribution of each numerical column.

5. Group the data by year, considering the average price in each year.

6. Plot the data you got as an output from group by with time on the horizontal axis and gold price rate for SAR and USD on the vertical.

Be sure to provide a title and labels for the horizontal and vertical axes.

7. Visualize the gold price rate in the last ten years (choose the appropriate chart).

8. Create, then train a regression model, then use it to predict the gold price in the next five time periods.

full python code with comments