Pizza Place Restaurant Performance Analysis (2015)

Project Title: Analysis of Pizza Place Restaurant Performance in 2015

Github : Dataset

Kaggle : Code

Description:

This project aims to analyze the performance of the Pizza Place restaurant in the year 2015 using their historical data. The analysis encompasses several crucial aspects, including monthly order counts, peak hours, the most popular pizza types, revenue contribution from each pizza size, and revenue contribution from each pizza type. The primary objective of this project is to provide in-depth insights to the management of Pizza Place Restaurant to make better-informed decisions in managing their business.

Activities Conducted:

1. Analysis of Monthly Order Counts (Line Chart): Data from order_id for each year-month is processed and visualized in the form of a line chart. The results indicate which month had the highest order count.

2. Identification of Peak Hours (Bar Plot): Data from order_details is used to create a dataset of peak hours based on order times. These peak hours are then visualized in the form of a bar plot to identify the busiest hours.

3. Analysis of the Most Popular Pizza Types (Bar Plot): Data from order_details is combined with pizza data to calculate the total quantity of each pizza type ordered. The results are visualized in a bar plot with a descending order to identify the most commonly ordered pizza types.

4. Analysis of Revenue Contribution from Each Pizza Size (Pie Chart): Revenue for each pizza size is calculated using the formula revenue = quantity x price. The results are visualized in a pie chart to show the percentage contribution of each pizza size.

5. Analysis of Revenue Contribution from Each Pizza Type (Bar Plot): Revenue for each pizza type is calculated using the formula revenue = quantity x price. The results are visualized in a bar plot, sorted from highest to lowest revenue.

Summary Report: Pizza Place Restaurant Performance Analysis (2015)

In our analysis of Pizza Place restaurant's performance for the year 2015, the following key findings were uncovered:

1. Month with Highest Sales: The highest sales occurred in July 2015 with a total of 1935 orders.

2. Peak Hours: The busiest hours were at 12:00 PM, with a peak order count of 2520.

3. Most Popular Pizza Type: The pizza type that customers ordered the most was 'classic_dlx,' with a total of 2416 orders.

4. Top Revenue Contributor by Size: Large-sized pizzas (L) made the largest revenue contribution, accounting for 45.9% of total revenue.

5. Top 3 Pizza Types by Total Revenue:

• 'thai_ckn' pizza type generated the highest total revenue, amounting to USD 43,434.25.

• 'bbq_ckn' pizza type closely followed with a total revenue of USD 42,768.00.

• 'cali_ckn' pizza type ranked third with a total revenue of USD 41,409.50.

These findings provide valuable insights into the restaurant's performance, highlighting the most successful months, peak hours, and pizza preferences. The data also emphasizes the significant contribution of specific pizza sizes and types to the restaurant's revenue.

This analysis can assist Pizza Place management in making informed decisions, optimizing their menu offerings, and developing strategies to further enhance their business in the future.

This project provides valuable insights for the Pizza Place Restaurant to enhance their business strategy, identify trends, and optimize sales. In-depth data analysis and informative visualizations will assist management in making better decisions.