Insights Obtained from Walmart Sales Data Analysis

Store and Department Performance:

• 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 Patterns:

• Weekly sales exhibit variations across different departments and stores.
• Certain weeks, like week 51 in 2010, experience notably higher sales, suggesting potential seasonality.

Store Types and Sizes:

• Store type A tends to have larger sizes compared to types B and C, as observed in box plots.

Distribution of Weekly Sales:

• The distribution of weekly sales is positively skewed, indicating that a majority of sales are concentrated towards lower values.

Correlation Analysis:

• 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.

Time-Based Trends:

• Line plots for each year (2010, 2011, 2012) showcase variations in weekly sales over time, providing insights into annual trends.

Outliers:

• Considerable negative values in weekly sales raise questions about data integrity and should be further investigated and addressed.

Recommendations for Further Analysis:

1. Feature selection: Consider dropping columns with weak correlations or high collinearity to enhance model performance.
2. Time-based analysis: Explore patterns in weekly sales over different years to identify contributing factors to peak sales weeks.
3. Store and department insights: Investigate the factors influencing the high performance of store 20 and department 92.
4. Outliers handling: Assess and address outliers in weekly sales data, especially negative values.
5. Modeling: Utilize insights gained for feature engineering in machine learning models to predict and optimize future sales.