Walmart Sales Analysis Description

Analysis Overview

To boost sales, key factors such as pricing, seasonal trends, holidays, and customer behavior are analyzed, along with the impact of weather conditions. Predictive models using historical data and machine learning can forecast future sales accurately. Insights from these analyses help optimize inventory levels, staffing schedules, and targeted marketing strategies for better efficiency and customer engagement.

Key Questions Addressed

• 1. What are the key factors that influence Walmart's weekly sales?
• 2. How do holidays and temperature impact weekly sales?
• 3. Are there any seasonal trends or patterns in the sales data?
• 4. How do external factors like CPI, unemployment, and fuel prices affect sales?
• 5. Can we build a machine learning model to predict weekly sales based on the given features?
• 6. How do sales vary during **Thanksgiving**, **Black Friday**, and **Christmas** weeks compared to non-festive

Dataset Source

The dataset used for this analysis is sourced from Kaggle Click here Kaggle Walmart Dataset

Libraries Used

• Pandas
• NumPy
• Matplotlib
• Seaborn
• Scipy
• Statsmodels
• Scikit-learn
• Warning