Transactional Retail Dataset Of Electronics Store by Kaggle | Technology and IT

About this Dataset

Transactional Retail Dataset Of Electronics Store

Context

This dataset contains information about an online electronic store. The store has three warehouses from which goods are delivered to customers.

Columns Description

order_id: A unique id for each order
customer_id: A unique id for each customer
date: The date the order was made, given in YYYY-MM-DD format
nearest_warehouse: A string denoting the name of the nearest warehouse to the
customer
shopping_cart: A list of tuples representing the order items: the first element of
the tuple is the item ordered, and the second element is the
quantity ordered for such item.
order_price: A float denoting the order price in USD. The order price is the
price of items before any discounts and/or delivery charges
are applied.
delivery_charges: A float representing the delivery charges of the order
customer_lat: Latitude of the customer’s location
customer_long: Longitude of the customer’s location
coupon_discount: An integer denoting the percentage discount to be applied to
the order_price.
order_total: A float denoting the total of the order in USD after all
discounts and/or delivery charges are applied.
season: A string denoting the season in which the order was placed.
is_expedited_delivery: A boolean denoting whether the customer has requested an
expedited delivery
distance_to_nearest_warehouse: A float representing the arc distance, in kilometres, between
the customer and the nearest warehouse to him/her.
latest_customer_review: A string representing the latest customer review on his/her
most recent order
is_happy_customer: A boolean denoting whether the customer is a happy
customer or had an issue with his/her last order.

Inspiration

Use this dataset to perform graphical and/or non-graphical EDA methods to understand
the data first and then find and fix the data problems.

Detect and fix errors in dirty_data.csv
Impute the missing values in missing_data.csv
Detect and remove Anolamies
To check whether a customer is happy with their last order

All the Best

Tables

Dirty Data

@kaggle.muhammadshahrayar_transactional_retail_of_electronics_store.dirty_data

87.2 KB
500 rows
16 columns


CREATE TABLE dirty_data (
  "order_id" VARCHAR,
  "customer_id" VARCHAR,
  "date" VARCHAR,
  "nearest_warehouse" VARCHAR,
  "shopping_cart" VARCHAR,
  "order_price" BIGINT,
  "delivery_charges" DOUBLE,
  "customer_lat" DOUBLE,
  "customer_long" DOUBLE,
  "coupon_discount" BIGINT,
  "order_total" DOUBLE,
  "season" VARCHAR,
  "is_expedited_delivery" BOOLEAN,
  "distance_to_nearest_warehouse" DOUBLE,
  "latest_customer_review" VARCHAR,
  "is_happy_customer" BOOLEAN
);

Missing Data

@kaggle.muhammadshahrayar_transactional_retail_of_electronics_store.missing_data

87.51 KB
500 rows
16 columns


CREATE TABLE missing_data (
  "order_id" VARCHAR,
  "customer_id" VARCHAR,
  "date" TIMESTAMP,
  "nearest_warehouse" VARCHAR,
  "shopping_cart" VARCHAR,
  "order_price" DOUBLE,
  "delivery_charges" DOUBLE,
  "customer_lat" DOUBLE,
  "customer_long" DOUBLE,
  "coupon_discount" BIGINT,
  "order_total" DOUBLE,
  "season" VARCHAR,
  "is_expedited_delivery" BOOLEAN,
  "distance_to_nearest_warehouse" DOUBLE,
  "latest_customer_review" VARCHAR,
  "is_happy_customer" VARCHAR
);

Warehouses

@kaggle.muhammadshahrayar_transactional_retail_of_electronics_store.warehouses

2.68 KB
3 rows
3 columns


CREATE TABLE warehouses (
  "names" VARCHAR,
  "lat" DOUBLE,
  "lon" DOUBLE
);