Walmart Sales Analysis (Exploratory Data Analysis)

I analyzed weekly sales data for 45 Walmart stores (6,435 records, 2010-2012) using Python, Pandas, NumPy, Matplotlib and Seaborn. I checked data quality (nulls, duplicates, data types), engineered date and season features, and handled outliers using the IQR method. I answered business questions such as which store has the highest sales, which store has the most volatile sales, how holidays (Super Bowl, Labor Day, Thanksgiving, Christmas) affect revenue, and how monthly and seasonal patterns differ. I also studied the relationship between weekly sales and external factors (fuel price, unemployment, CPI, temperature) using scatter plots and a correlation heatmap, then gave recommendations such as targeting promotions at stores with high unemployment and fuel prices.

Highest revenue: Store 20 had the highest total sales.

Most volatile sales: Store 14 showed the highest sales variability (standard deviation).

Holidays: Thanksgiving was the best-performing holiday in revenue, and Christmas was the weakest.

Seasonality: April and the spring season had the highest sales.

External factors: Fuel price and unemployment both have a negative relationship with weekly sales. Stores 12, 28 and 38 had both high unemployment and high fuel prices.