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Transactional Retail Dataset Of Electronics Store

Online Electronics Store Dataset

@kaggle.muhammadshahrayar_transactional_retail_of_electronics_store

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