Analyzing sales goals:

• visualization by using various dashboards in python (dash, plotly express, matplotlib/ pyplot) Code,
• making prediction (classification) by using linear models and others (Suppor Vector for Classification, Decision Tree Classification and Random Forest Classifaction) Code.

Author of visualization ~ Veronika Hordieieva

Author of predictions ~ Juliusz Łosiński

Data: https://www.kaggle.com/datasets/aungpyaeap/supermarket-sales/

Jira tracking: https://panfrog.atlassian.net/jira/software/projects/KAN/boards/1

Predictions:

Gender prediction (classification):

Description/ visualization:

Apply to Supermarket sales data: https://www.kaggle.com/datasets/aungpyaeap/supermarket-sales to calculate the following values:

1. Arithmetic mean (average)
2. Median
3. Variance
4. Standard Deviation
5. Quartiles
6. Percentiles
7. Range (range)
8. Coefficient of variation
9. Skewness (asymmetry)
10. Kurtosis
11. Histogram