Black Friday Sales Analysis Project

Overview

This project involves the analysis of a Black Friday sales dataset using Python and various data analysis libraries, such as Pandas and Seaborn.

Purpose

The goal is to gain insights into customer behavior, purchasing patterns, and the impact of different demographic factors on sales.Some example questions include:

Were purchasing patterns dependant on gender and age groups?

Should consumer's occupation be considered impactful on sales?

Table of content

-- Loading & Cleaning the Dataset

-- Performing basic analysis on columns

-- Analysis (Based on gender, Age, Marital Status)

-- Multicolumn Analysis

-- Analysis (Based on occupation & Gender)

-- Combining Gender and Marital Status

Dataset (Columns)

1. User_ID
2. Product_ID
3. Gender
4. Age
5. Occupation
6. City_Category
7. Stay_In_Current_City_Years
8. Marital_Status
9. Product_Category_1
10. Product_Category_2
11. Product_Category_3
12. Purchase

Loading & Cleaning the Dataset

The dataset is loaded using Pandas, and basic information about its structure is displayed using df.info() and df.head(). Null values in the dataset are identified and handled, with columns containing excessive null values dropped to maintain data integrity.

Performing basic analysis on columns

Basic statistics and visualizations are performed on various columns, such as the count of unique values, summary statistics, and total purchase amount.

Analysis

Gender Analysis

Analysis is conducted on the 'Gender' column, including the count of each gender, purchasing patterns, and visualizations representing the gender distribution.

Age & Marital Status Analysis

The 'Age' and 'Marital_Status' columns are analyzed individually, including visualizations depicting the age distribution, amount spent by age group, and the marital status distribution.

Multi Column Analysis

Relationships between multiple columns are explored through visualizations, such as the interaction between 'Gender' and 'Age', 'Gender' and 'Marital_Status', 'City_Category' and 'Age', and more.

Occupation & Product Analysis

Analysis is performed on the 'Occupation' column, including count, purchase amount, and mean purchase amount for each occupation. The 'Product_Category_1' column is analyzed, showing the count, purchase amount, and mean purchase amount for each category. Top products based on purchase amount and mean purchase amount are visualized.

Combining Gender and Marital Status

A combined analysis is performed to understand the relationship between 'Gender' and 'Marital_Status', including visualizations depicting the distribution.

Conclusion

The detailed exploration of various columns and their interactions contributes to a better understanding of the factors influencing sales during Black Friday.