Walmart Sales

Project Insights

The objective was to transform raw transactional data into actionable insights. Through analysis, key patterns such as high-performing branches, peak sales periods, and preferred payment methods were identified. These insights demonstrate how data analytics can guide business strategy, optimize product focus, and improve operational decisions.

# End-to-End Analytics Pipeline
Raw Data → Data Cleaning → EDA → Feature Understanding
        → Load to PostgreSQL → SQL Queries → Business Insights

# End-to-End Analytics Pipeline
Raw Data → Data Cleaning → EDA → Feature Understanding
        → Load to PostgreSQL → SQL Queries → Business Insights

# End-to-End Analytics Pipeline
Raw Data → Data Cleaning → EDA → Feature Understanding
        → Load to PostgreSQL → SQL Queries → Business Insights

Key Features

• Data cleaning and preprocessing using Pandas

• Exploratory Data Analysis (EDA) for trend discovery

• SQL-based business question answering

• Revenue and sales trend analysis

• Customer type and payment method insights

• Branch and product line performance evaluation

Tech Stack

• Python (Pandas)

• PostgreSQL

• SQL

• Jupyter Notebook