Ecommerce Sales Data Analysis

19AIE214-Big Data Analysis Project

Authors:
Dharshan Kumar K S
Siva Prakash

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Data

Data consists of Ecommerce data from 04-09-2016 to 03-09-2018, which is about 2 years of data. The dataset we have used is a combination of 9 sub-datasets which originally is 120.3 MB sized dataset. But we have pre-processed and removed many unwanted feature columns and used the modified dataset for our project analysis.
Dataset rows : 1,16,573
Dataset columns : 21
Dataset size : 27.4 MB
Dataset link : https://amritavishwavidyapeetham-my.sharepoint.com/:x:/g/personal/cb_en_u4aie19024_cb_students_amrita_edu/EXutaLENebZGmRHiBsClRXMBaE4T7Cz7SHNdObgyzI8oGg?e=IY32cT
Original Dataset link : https://www.kaggle.com/olistbr/brazilian-ecommerce

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Data Description

S.No	Name	Description
1	order_id	unique id for each order (32 fixed-size number)
2	customer_id	unique id for each customer (32 fixed-size number)
3	quantity	1-21
4	price_MRP	cost price, 0.85-6735
5	payment	selling price, 0-13664.8
6	timestamp	order purchase time (local, day-month-year hour:min:sec AM/PM)
7	rating	1-5
8	product_category	category under which product belongs
9	product_id	unique id for each product (32 fixed-size number)
10	payment_type	Type of payment - credit card/debit card/boleto/voucher
11	order_status	delivered/shipped/invoiced
12	product_weight_g	weight of product (in grams), 0-40425
13	product_length_cm	length of product (in centimeter), 7-105
14	product_height_cm	height of product (in centimeter), 2-105
15	product_width_cm	width of product (in centimeter), 6-118
16	customer_city	city where order is placed
17	customer_state	state where order is placed
18	seller_id	unique id for each seller (32 fixed-size number)
19	seller_city	city where order is picked up
20	seller_state	state where order is picked up
21	payment_installments	no. of installments taken by customer to pay bill, 0-24

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Analysis Performed using Spark

1. Customer Segmentation
   Categorizing customers based on their spendings
   [Bar-graph]

2. Monthly Trend Forecasting
   Visualising the monthly trend of sales
   [Bar-graph]

3. Hourly Sales Analysis
   Which hour has more no. of sales?
   [Timeseries-Plot]

4. Product Based Analysis
   Which category product has sold more?
   Which category product has more rating?
   Which product has sold more?
   Top 10 highest & least product rating?
   Order Count for each rating
   [Bar-graph]

5. Payment Preference
   What are the most commonly used payment types?
   Count of Orders With each No. of Payment Installments
   [Pie-Chart]

6. Potential Customer's Location
   Where do most customers come from?
   [Pie-chart]

7. Seller Rating
   Which seller sold more?
   Which seller got more rating?
   [Bar-graph]

8. Logistics based Optimization Insights
   Which city buys heavy weight products and low weight products?
   [Pie-chart]
   How much products sold within seller state?
   [Bar-graph]

Machine Learning Model:

9. Predicting future sales
   ML - Linear regression

Visualization:

Total no. of Graphs & Plots: 19
Python Plots
Excel Plots

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Example Plot