Name: Black Friday Sales EDA
Creator: Kaggle
License: https://creativecommons.org/publicdomain/zero/1.0/

About this Dataset

Black Friday Sales EDA

Dataset History

A retail company “ABC Private Limited” wants to understand the customer purchase behaviour (specifically, purchase amount) against various products of different categories. They have shared purchase summaries of various customers for selected high-volume products from last month.
The data set also contains customer demographics (age, gender, marital status, city type, stay in the current city), product details (productid and product category) and Total purchase amount from last month.

Now, they want to build a model to predict the purchase amount of customers against various products which will help them to create a personalized offer for customers against different products.

Tasks to perform

The purchase column is the Target Variable, perform Univariate Analysis and Bivariate Analysis w.r.t the Purchase.

Masked in the column description means already converted from categorical value to numerical column.

Below mentioned points are just given to get you started with the dataset, not mandatory to follow the same sequence.

DATA PREPROCESSING

Check the basic statistics of the dataset
Check for missing values in the data
Check for unique values in data
Perform EDA
Purchase Distribution
Check for outliers
Analysis by Gender, Marital Status, occupation, occupation vs purchase, purchase by city, purchase by age group, etc
Drop unnecessary fields
Convert categorical data into integer using map function (e.g 'Gender' column)
Missing value treatment
Rename columns
Fill nan values
map range variables into integers (e.g 'Age' column)

Data Visualisation

visualize individual column
Age vs Purchased
Occupation vs Purchased
Productcategory1 vs Purchased
Productcategory2 vs Purchased
Productcategory3 vs Purchased
City category pie chart
check for more possible plots

All the Best!!

Tables

Train

@kaggle.rishikeshkonapure_black_friday_sales_eda.train

3.92 MB
550,068 rows
12 columns

CREATE TABLE train (
  "user_id" BIGINT,
  "product_id" VARCHAR,
  "gender" VARCHAR,
  "age" VARCHAR,
  "occupation" BIGINT,
  "city_category" VARCHAR,
  "stay_in_current_city_years" VARCHAR,
  "marital_status" BIGINT,
  "product_category_1" BIGINT,
  "product_category_2" DOUBLE,
  "product_category_3" DOUBLE,
  "purchase" BIGINT
);