Leveraging SQL & Python for Data-Driven Decision Making

This project presents a comprehensive analysis of Blinkit's sales data, focusing on evaluating sales performance, understanding customer satisfaction, and optimizing inventory distribution. Utilizing SQL for data extraction and transformation, and Python for data visualization, the analysis aims to uncover key insights and opportunities for business optimization.

Assess Sales Performance: Analyze total and average sales to identify trends and patterns.

Understand Customer Satisfaction: Evaluate customer ratings to gauge satisfaction levels.

Optimize Inventory Distribution: Examine sales across different item types and fat content to inform inventory decisions.

Identify Key Insights: Derive actionable insights to support strategic decision-making.

SQL: Data extraction, cleaning, and transformation.

Python: Data analysis and visualization using libraries such as pandas, matplotlib, and seaborn.

Jupyter Notebook: Interactive environment for combining code, visuals, and narrative text.

GitHub: Version control and project documentation.

Total Sales: Overall revenue generated from all items sold.

Average Sales: Mean revenue per sale.

Number of Items Sold: Total count of items sold.

Average Rating: Mean customer rating for items sold.

Sales by Fat Content: Revenue breakdown by low-fat and regular-fat products.

Sales by Item Type: Revenue distribution across various product categories.

Sales by Outlet Establishment Year: Performance analysis based on the year outlets were established.

Sales by Outlet Size and Location: Insights into sales performance by outlet size and geographical location.

Data Extraction & Cleaning (SQL) Imported raw sales data into a relational database.

Performed data cleaning operations, including handling missing values and correcting data types.

Created normalized tables for items, outlets, and transactions to ensure data integrity.

Data Transformation & Analysis (SQL) Wrote complex SQL queries to aggregate data.

Calculated KPIs such as total sales, average sales, and average ratings.

Analyzed sales trends over time and across different dimensions (e.g., item type, fat content).

Data Visualization (Python) Utilized pandas for data manipulation and preparation.

Created visualizations using matplotlib and seaborn to illustrate key findings:

Bar Charts: Sales by item type, outlet tier by fat content and outlet location type

Line Graphs: Sales trends over time by outlet establishment

Pie Charts: Sales distribution by outlet size and fat content.

High Demand for Low-Fat Products: Low-fat items account for a significant portion of total sales, indicating a consumer preference for healthier options.

Top-Selling Categories: Fruits, vegetables, and snack foods are among the highest revenue-generating categories.

Outlet Performance:

Establishment Year: Outlets established in recent years show higher sales performance, suggesting successful newer business models or locations.

Size & Location: Medium-sized outlets in Tier 3 cities exhibit the highest profitability, highlighting potential areas for expansion.

Customer Satisfaction: An average rating close to 4 out of 5 suggests overall positive customer experiences, but there is room for improvement in certain categories.

SQL Queries; https://github.com/Oluchiumeh/BlinkIT-Analysis/blob/main/Blinkit_Query.sql

Python Script; https://github.com/Oluchiumeh/BlinkIT-Analysis/blob/main/Blinkit%20Analysis%20in%20python.ipynb