Sales-Analysis

Sales Data Analysis for Everyday Essentials Mart

Project Overview

This project involves an in-depth analysis of the sales data for Everyday Essentials Mart, a retail business offering a wide range of products including Food, Clothing, and Electronics. The analysis focuses on understanding customer demographics, purchasing behaviors, and product preferences to provide actionable insights for improving marketing strategies and sales performance.

Data

The dataset used for this project consists of sales records with the following columns:

1. User_ID: Unique identifier for each customer
2. Cust_name: Name of the customer
3. Product_ID: Unique identifier for each product
4. Gender: Gender of the customer
5. Age Group: Age group of the customer
6. Age: Age of the customer
7. Marital_Status: Marital status of the customer (0 for single, 1 for married)
8. State: State where the customer resides
9. Zone: Zone where the state is located
10. Occupation: Occupation of the customer
11. Product_Category: Category of the purchased product
12. Orders: Number of orders placed by the customer
13. Amount: Total amount spent by the customer

Exploratory Data Analysis (EDA)

The following analyses were conducted:

Gender Analysis

• Distribution of buyers by gender.
• Total amount spent by each gender.

Age Group Analysis

• Distribution of buyers by age group and gender.
• Total amount spent by each age group.

Geographic Analysis

• Total number of orders and sales amount from the top 10 states.

Marital Status Analysis

• Distribution of buyers by marital status.
• Total amount spent by marital status and gender.

Occupation Analysis

• Distribution of buyers by occupation.
• Total amount spent by each occupation.

Product Category Analysis

• Distribution of sold products by category.
• Total sales amount by product category.
• Top 10 most sold products.

Results

• Customer Profile: The primary customers are married women aged 26-35 years, residing in Uttar Pradesh, Maharashtra, and Karnataka, and working in IT, Healthcare, and Aviation sectors.

• Product Preferences: Most purchases are in the Food, Clothing, and Electronics categories.

Conclusion

The analysis provides insights into customer demographics, purchasing behaviors, and product preferences, enabling targeted marketing strategies and better inventory management to drive sales growth.

Visualizations

The project includes various visualizations created using matplotlib and seaborn to support the analysis. These can be generated using the provided code snippets in the project notebook.