#Data Visualization with Seaborn for Walmart Sales Projection using Python

This project explores Walmart sales data through data preprocessing, feature engineering, and visual analysis. Using Seaborn and Matplotlib, I created a variety of plots to uncover patterns in sales across stores, departments, holidays, fuel prices, and consumer price indices.

Key steps include:

Cleaning and merging multiple datasets.

Encoding categorical variables with LabelEncoder.

Extracting time-based features such as month and holiday indicators.

Applying transformations (e.g., log scaling) to stabilize weekly sales data.

Building training and test sets for sales forecasting.

Visualizing relationships between features (PairGrid, scatter plots, count plots, etc.) to identify trends and insights.

This repository highlights how data visualization plays a crucial role in understanding sales patterns, identifying seasonality, and preparing features for predictive modeling.