1_Examine your datasets with Pandas, which displays all columns and their data types, the top ten for each dataset, and basic statistics for numeric columns (Count, mean, std, min, max). Add your comments about the data.
2_Show the missing data and incorrect values for each column, such as zeros or negative sales.
3_Decide how you want to handle missing and incorrect values and implement it.
4_Merge all datasets into data frame based on the date and store.
1_ Make a chart to illustrate if weekly sales are increasing or decreasing over time.
2_ Make a chart to show how much each brand sells.
3_ Determine the top ten selling stores.
4_ Make a histogram to show the top 10 stores sales.
5_ Create a chart that compares average weekly sales for the top ten selling stores during holidays and non-holidays.
6_ Create a chart that displays the average weekly sales for each brand department for the top 10 selling stores.
7_ Make a line chart to show the relationship between weekly sales and weather Temperature.
8_ Make a line chart to show the relationship between the cost of fuel and weather weekly sales.
1_ Divides the data into training and testing categories (80 percent training data and 20 percent testing data).
2_ Create two separate supervised learning models to forecast weekly sales based on specific characteristics.
3_ Compare the accuracy of the two models (in percentages).