Pizza-restaurant-analysis

Pizza restaurant analysis using Python + SQL + Power BI + Power Query

Table of Contents

1.Introduction

1.Overview Dashboard

3.Dataset

4.Python EDA and Visualization

5.SQL

6.Power Query

7.Usage

Intoduction

The project aims to analyze the 'Pizza restaurant' dataset to derive insights and facilitate data-driven decision-making for anyone interested in data analysis and dashboards.

Overview Dashboard

Process of the project

This project involves leveraging SQL to extract Key Performance Indicators (KPIs) as per the client's requirements. The data is extracted from an MS SQL-connected database and seamlessly integrated into Power BI. Subsequently, Power Query is employed to perform a thorough data transformation, involving tasks such as eliminating unnecessary columns, renaming fields, and executing additional data cleansing processes. The final step entails crafting an insightful dashboard using Power BI, thereby presenting the refined data in a visually compelling manner for effective analysis.

Dataset

The dataset contains the following columns:

-pizza_id

-order_id

-quantity

-order_date

-order_time

-unit_price

-total_price

-pizza_size

-pizza_category

-pizza_ingredients

-pizza_name

-Order Month

Python for EDA and Visualization

I utilized Python for Exploratory Data Analysis (EDA), data cleansing, and proficient data visualization to ensure a comprehensive and precise understanding of the dataset. Python's versatile libraries allowed me to perform intricate data cleaning tasks, ensuring data integrity and consistency. Moreover, employing advanced visualization techniques enabled the creation of insightful graphical representations that facilitated easy comprehension and acceptance by users.

to access Kaggle notebook Click Here

SQL

SQl queries the sql queris contains a 11 type of queries:

1. KPI's
2. Daily Trend for Total Orders
3. Monthly Trend for Orders
4. % of Sales by Pizza Category
5. % of Sales by Pizza Size
6. Total Pizzas Sold by Pizza Category
7. Top 5 Pizzas by Revenue
8. Bottom 5 Pizzas by Revenue
9. Top 5 Pizzas by Quantity
10. Bottom 5 Pizzas by Quantity

to view the SQL queries Click Here

Power Query

I used Power Query to clean and preprocess my data before visualizing it, following a series of steps:

1. Eliminate unnecessary columns from the dataset.
2. Designate the initial row as column headers for better organization.
3. Rename the columns to enhance clarity and understanding.
4. Adjust data types to ensure consistency and compatibility.
5. Substitute specific values to refine and standardize the data.

Usage

to acces the Dashboard Click Here

to access Kaggle notebook Click Here

to access the SQL Kpis Click Here

to access the Dataset Click Here