- Store 20 has the highest mean weekly sales, indicating its strong performance.
- Department 92 stands out with the highest mean weekly sales, while department 47 has the lowest.
- Weekly sales exhibit variations across different departments and stores.
- Certain weeks, like week 51 in 2010, experience notably higher sales, suggesting potential seasonality.
- Store type A tends to have larger sizes compared to types B and C, as observed in box plots.
- The distribution of weekly sales is positively skewed, indicating that a majority of sales are concentrated towards lower values.
- Some features in the dataset exhibit strong correlations, while certain columns are weakly correlated.
- Correlation heatmap helps identify relationships between different variables in the dataset.
- Line plots for each year (2010, 2011, 2012) showcase variations in weekly sales over time, providing insights into annual trends.
- Considerable negative values in weekly sales raise questions about data integrity and should be further investigated and addressed.
- Feature selection: Consider dropping columns with weak correlations or high collinearity to enhance model performance.
- Time-based analysis: Explore patterns in weekly sales over different years to identify contributing factors to peak sales weeks.
- Store and department insights: Investigate the factors influencing the high performance of store 20 and department 92.
- Outliers handling: Assess and address outliers in weekly sales data, especially negative values.
- Modeling: Utilize insights gained for feature engineering in machine learning models to predict and optimize future sales.