Pizza Sales Analysis

Project Overview

The purpose of our analysis is to gain insights into pizza sales trends and customer preferences in order to inform strategic decision-making for businesses in the food industry. By analyzing a dataset of pizza sales transactions, we aim to identify key patterns, trends, and correlations that can help businesses optimize their menu offerings, pricing strategies, and marketing efforts.

Data Handling

For data manipulation and visualization, we will primarily use the following libraries:

• Pandas: A powerful Python library for data manipulation and analysis.
• Matplotlib: A comprehensive library for creating static, interactive, and animated visualizations.
• Seaborn: Built on top of Matplotlib, providing a high-level interface for creating attractive statistical graphics.

Data Preparation

The process for loading, cleaning, and preprocessing the data involves several steps:

1. Loading the Data
2. Cleaning the Data
3. Preprocessing the Data
4. Handling Missing Values
5. Categorical Data Conversion

Data Analysis Techniques

We used various data analysis techniques, including:

• Descriptive Statistics
• Inferential Statistics
• Correlation Analysis
• Regression Analysis

Soft Copy Files

You can access the soft copy files of the project by clicking the link below:

Appendices

Here are the appendices based on the provided PDF file:

1. Appendix A: Data Dictionary
2. Appendix B: Code Listings
3. Appendix C: Additional Figures and Tables