Sales Data Analysis

This repository contains code and analysis for sales data. The analysis is divided into several sections, and each section is explained below:

Table of Contents

• Getting Started
  • Importing Necessary Libraries
  • Loading Data
  • Data Processing
• Exploratory Data Analysis (EDA)
  • Sales Analysis
    • Best Month for Sales
    • What Day of the Week Has the Highest Sales?
    • Timeline of Day of the Week vs. Revenue
    • Sales Per Hour
  • Product Analysis
    • What Product Sold the Most?
    • Top Products for Each City
    • Top 5 Products with the Highest Revenue for Each City
    • What Products Are Most Often Sold Together?
    • What Percentage of Orders Include Multiple Products?
  • Order Value Analysis
    • What Was the Highest Single-Order Value?
  • City and Revenue Analysis
    • What City Has the Highest Revenue?
    • What City Sold the Most Products?
  • State Analysis
    • What Is the Distribution of States?

Getting Started

Importing Necessary Libraries

In this section, we import the required Python libraries to perform data analysis and visualization.

Loading Data

Data loading is a crucial step in any data analysis project. Here, we load the dataset that we'll be working with.The Dataset The dataset contains 12 CSV files containing sales details for the 12 months of the year 2019. Each file contains anywhere from around 9000 to 26000 rows and 6 columns. The columns are as follows: Order ID, Product, Quantity Ordered, Price Each, Order Date, Purchase Address

Data Processing

Data processing involves cleaning and preparing the data for analysis. This section includes various data preprocessing steps.

Exploratory Data Analysis (EDA)

EDA is the heart of this project, where we explore and analyze the data to gain insights.

Sales Analysis

Best Month for Sales

We determine the best month for sales and present our findings.

What Day of the Week Has the Highest Sales?

We identify the day of the week with the highest sales and provide insights.

Timeline of Day of the Week vs. Revenue

We visualize the timeline of day of the week versus revenue to spot trends.

Sales Per Hour

We analyze sales per hour and present the results.

Product Analysis

What Product Sold the Most?

We identify the product that sold the most and share our findings.

Top Products for Each City

We determine the top-selling products for each city and provide insights.

Top 5 Products with the Highest Revenue for Each City

We present the top 5 products with the highest revenue for each city.

What Products Are Most Often Sold Together?

We analyze product associations to identify which products are most frequently sold together.

What Percentage of Orders Include Multiple Products?

We calculate the percentage of orders that include multiple products.

Order Value Analysis

What Was the Highest Single-Order Value?

We identify the highest value for a single order.

City and Revenue Analysis

What City with Highest Revenue?

We determine the city with the highest revenue and provide insights.

What City Sold the Most Products?

We identify the city that sold the most products.

State Analysis

What Is the Distribution of States?

We analyze the distribution of states in the dataset.

Conclusion

In conclusion, our data analysis of sales data has provided valuable insights that can guide decision-making across various aspects of the business. These insights include the identification of peak sales months, best-selling products, top revenue-generating cities, and more. the business can make informed decisions to optimize operations, enhance marketing efforts, and maximize revenue. Continued analysis and monitoring of sales data will be crucial for adapting to changing market dynamics and maintaining a competitive edge.

Contact

• Mohamed Ibrahim