E-COMMERCE SALES DATA ANALYSIS

INTRODUCTION

This project focuses on analyzing an e-commerce dataset to understand sales trends , customer behaviour and product performance. The analysis aims to extract meaningful insights that can help businesses make data-driven decisions. Tools like Python , Pandas , Matplotlib and Seaborn are used for data processing and visualization.

OBJECTIVES

The main objective of this project is :
- To analyze sales and revenue trends.
- To identify top-performing products.
- To understand customer and country-wise performance.
- To generate actionable business insights.

DATASET DESCRIPTION

The dataset contains transactional data of an online retail store . It includes details such as Invoice Number , Product Description , Quantity , Invoice Date , Unit Price , Customer ID and Country.

TOOLS & TECHNOLOGIES USED

- Python
- Pandas
- Matplotlib
- Seaborn
- Jupyter Notebook / Google Colab

FEATURE ENGINEERING

A new column 'Revenue' was created using: Revenue = Quantity * Unit Price

EXPLORATORY DATA ANALYSIS (EDA)

Exploratory Data Analysis was performed to understand patterns and trends in the dataset using various visualization such as bar charts , line plots and heatmaps.

IMPORT & UPLOAD THE ZIP FILE

In [1]:

from google.colab import files
uploaded = files.upload()

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Saving OnlineRetail.csv.zip to OnlineRetail.csv.zip

UNZIP THE FILE

In [3]:

import zipfile
with zipfile.ZipFile("OnlineRetail.csv.zip",'r') as zip_ref: zip_ref.extractall()

EXTRACTED FILE NAME CHECKING

In [4]:

import os
os.listdir()

Out[4]:

['.config', 'OnlineRetail.csv', 'OnlineRetail.csv.zip', 'sample_data']

INSTALL LIBRARIES

In [5]:

pip install pandas matplotlib seaborn jupyter plotly streamlit

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LOAD DATASET

In [6]:

import pandas as pd
df = pd.read_csv("OnlineRetail.csv",encoding = "ISO-8859-1")
df.head()

Out[6]:

	InvoiceNo	StockCode	Description	Quantity	InvoiceDate	UnitPrice	CustomerID	Country
0	536365	85123A	WHITE HANGING HEART T-LIGHT HOLDER	6	12/1/2010 8:26	2.55	17850.0	United Kingdom
1	536365	71053	WHITE METAL LANTERN	6	12/1/2010 8:26	3.39	17850.0	United Kingdom
2	536365	84406B	CREAM CUPID HEARTS COAT HANGER	8	12/1/2010 8:26	2.75	17850.0	United Kingdom
3	536365	84029G	KNITTED UNION FLAG HOT WATER BOTTLE	6	12/1/2010 8:26	3.39	17850.0	United Kingdom
4	536365	84029E	RED WOOLLY HOTTIE WHITE HEART.	6	12/1/2010 8:26	3.39	17850.0	United Kingdom

DATA CLEANING

In [7]:

# Remove duplicates
df = df.drop_duplicates()

# Remove missing values
df= df.dropna()

# Convert date column
df['InvoiceDate']=pd.to_datetime(df['InvoiceDate'])

# Remove negative or zero values
df = df[df['Quantity']> 0]
df.info()

<class 'pandas.core.frame.DataFrame'> Index: 392732 entries, 0 to 541908 Data columns (total 8 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 InvoiceNo 392732 non-null object 1 StockCode 392732 non-null object 2 Description 392732 non-null object 3 Quantity 392732 non-null int64 4 InvoiceDate 392732 non-null datetime64[ns] 5 UnitPrice 392732 non-null float64 6 CustomerID 392732 non-null float64 7 Country 392732 non-null object dtypes: datetime64[ns](1), float64(2), int64(1), object(4) memory usage: 27.0+ MB

CREATED REVENUE COLUMN

In [10]:

df["Revenue"] = df["Quantity"] * df["UnitPrice"]

DATA ANALYSIS

In [12]:

top_products = df.groupby("Description")["Revenue"].sum().sort_values(ascending=False).head(10)
print(top_products)

Description PAPER CRAFT , LITTLE BIRDIE 168469.60 REGENCY CAKESTAND 3 TIER 142264.75 WHITE HANGING HEART T-LIGHT HOLDER 100392.10 JUMBO BAG RED RETROSPOT 85040.54 MEDIUM CERAMIC TOP STORAGE JAR 81416.73 POSTAGE 77803.96 PARTY BUNTING 68785.23 ASSORTED COLOUR BIRD ORNAMENT 56413.03 Manual 53419.93 RABBIT NIGHT LIGHT 51251.24 Name: Revenue, dtype: float64

In [14]:

country_sales = df.groupby("Country")["Revenue"].sum().sort_values(ascending=False)

In [17]:

df["Month"] = df['InvoiceDate'].dt.to_period('M')
monthly_sales = df.groupby('Month')["Revenue"].sum()

DATA VISUALIZATION

In [18]:

import matplotlib.pyplot as plt
top_products.plot(kind='bar')
plt.title ("Top 10 Products")
plt.show()

In [20]:

monthly_sales.plot()
plt.title("Monthly Revenue Trend")
plt.show()

In [21]:

country_sales.head(10).plot(kind='bar')
plt.title("Top Countries By Revenue")
plt.show()

In [23]:

top_customers = df.groupby('CustomerID')['Revenue'].sum().sort_values(ascending=False).head(10)

top_customers.plot(kind='bar')
plt.title("Top Customers By Revenue")
plt.show()

In [25]:

df['Hour'] = df['InvoiceDate'].dt.hour
hour_sales = df.groupby('Hour')['Revenue'].sum()
hour_sales.plot(title='Sales by Hour')
plt.show()

HEATMAP USING SEABORN

In [26]:

import seaborn as sns
corr = df[['Quantity', 'UnitPrice', 'Revenue']].corr()
sns.heatmap(corr, annot=True)
plt.show()

In [27]:

df['Day'] = df['InvoiceDate'].dt.day_name()
day_sales = df.groupby('Day')['Revenue'].sum()
day_sales.plot(kind='bar')
plt.title('Sales by Day of the Week')
plt.show()

In [28]:

import matplotlib.pyplot as plt
plt.scatter(df['Quantity'],df['Revenue'])
plt.title("Quantity vs Revenue")
plt.xlabel("Quantity")
plt.ylabel("Revenue")
plt.show()

In [29]:

df['Revenue'].plot(kind='hist',bins=50,title = "Revenue Distribution")

Out[29]:

<Axes: title={'center': 'Revenue Distribution'}, ylabel='Frequency'>

In [30]:

import seaborn as sns
sns.boxplot(x=df['Revenue'])

Out[30]:

<Axes: xlabel='Revenue'>

In [31]:

country_sales.head(5).plot(kind='pie',autopct='%1.1f%%')

Out[31]:

<Axes: ylabel='Revenue'>

In [32]:

monthly_sales_pct = monthly_sales.pct_change()
monthly_sales_pct.plot(title = "Monthly Growth Rate")

Out[32]:

<Axes: title={'center': 'Monthly Growth Rate'}, xlabel='Month'>

In [34]:

top_product_name = df.groupby("Description")["Revenue"].sum().idxmax()
product_trend = df[df['Description'] == top_product_name].groupby('Month')['Revenue'].sum().plot()

/usr/local/lib/python3.12/dist-packages/pandas/plotting/_matplotlib/core.py:1561: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding. ax.set_xlim(left, right)

KPI SUMMARY

In [41]:

total_revenue = df["Revenue"].sum()
total_orders = df["InvoiceNo"].nunique()
top_product = top_products.index[0]
top_country = country_sales.index[0]

print("Total Revenue:", total_revenue)
print("Total Orders:", total_orders)
print("Top Product:", top_product)
print("Top Country:", top_country)

Total Revenue: 8887208.894000003 Total Orders: 18536 Top Product: PAPER CRAFT , LITTLE BIRDIE Top Country: United Kingdom

INSIGHTS

* Top 10 products contribute a major share of total revenue.
* Sales are dominated by a few countries(highest from one main country).

* Revenue shows clear monthly/seasonal fluctuations.
* Peak sales occur during specific months(likely holiday season).

* A small number of customers generate most of the revenue.
* Sales vary by time(peak hours and specific days).

* Presence of negative quantities indicates product returns.
* Revenue distribution is highly skewed(few high-value transactions).

* Revenue growth is inconsistent across months.
* Many products contribute very little to overall sales.

CONCLUSION

This analysis provided valuable insights into sales performance , customer behaviour and product trends . The findings can help businesses optimize strategies , improve customer retention and increase revenue.