Avanthi Institute of Engineering and Technology

(Approved by A.I.C.T.E., New Delhi, & Permanently Affiliated to J.N.T.U-GV., Vizianagaram)

NAAC “A+” Accredited Institute
Cherukupally (Village), Near Tagarapuvalasa Bridge,Vizianagaram (Dist)-531162.

AVANTHI INSTITUTE OF
ENGINEERING AND TECHNOLOGY
JNTUGV/Regd.No.: 22Q71A05B3

CERTIFICATE
Certified that this is a bona fide record of practical work done by
NULU LAVANYA of IIIrd B.Tech / M.Tech / M.B.A / M.C.A
1st Semester during the Data Science using Python
Internship conducted by Grafx IT Solutions, Visakhapatnam,
under the Department of Computer Science and
Engineering during the academic year 2024-2025.
Name of the Project: E Commerce purchasing

Signature of the Guide Incharge

Signature of the External Examiner

Signature of the H.O.D.
Activity log

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Avanthi Institute of Engineering and Technology
(Approved by A.I.C.T.E., New Delhi, & Permanently Affiliated to J.N.T.U-GV., Vizianagaram)
NAAC “A+” Accredited Institute
Cherukupally (Village), Near Tagarapuvalasa Bridge,Vizianagaram (Dist)-531162.

Week Activity Signature of

the Guide
week 1 introduction to python. incharge
week 2 introduction to GUI
using tkinter library
introduction to data
science.
week 3 working with data in
python(lists,
week 4 dictionaries,tuples,sets
week 5 ).
introduction to Numpy
week 6 and data manipulation.
introduction to Pandas
week 7 for data handling.
data visualisation with
week 8 matplotlib and
seaborn.
Introduction to
machine learning
concepts using scikit-
learn.

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 Avanthi Institute of Engineering and Technology
(Approved by A.I.C.T.E., New Delhi, & Permanently Affiliated to J.N.T.U-GV., Vizianagaram)
NAAC “A+” Accredited Institute
Cherukupally (Village), Near Tagarapuvalasa Bridge,Vizianagaram (Dist)-531162.

TABLE OF CONTENT
Chapter One: Introduction Page
No.
1.0 Introduction ---------------------------------------------------------- 4
1.1 Problem Statement -------------------------------------------------- 4

Chapter Two: Literature Review

2.0 Historical Overview -------------------------------------------------- 5
2.1 Emerging Trends and Future Directions------------------------- 5
2.2 Literature survey of Data Science--------------------------------- 6
2.3 Algorithms and Techniques --------------------------------------- 7
2.4 Challenges and Limitations---------------------------------------- 8

Chapter Three: Machine Learning & AI

3.0 Introduction to Data Science with Python---------------------- 9
3.1 Data Collection and Preprocessing with Python---------------- 9
3.2 Exploratory Data Analysis (EDA) with Python---------------- 10
3.3 Feature Engineering and Selection in Python----------------- 10
3.4 Time Series Analysis with Python------------------------------- 11
3.5 Big Data Processing with Python-------------------------------- 11
3.6 Ethical and Social Implications of Data Science-------------- 12
3.7 Emerging Trends and Future Directions in Data Science with
Python-------------------------------------------------------------------- 12

Chapter Four: Design & Development of Classification

4.0 Introduction ------------------------------------------------------- 13
4.1 Data Collection and Preprocessing ------------------------------ 13

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 Avanthi Institute of Engineering and Technology
(Approved by A.I.C.T.E., New Delhi, & Permanently Affiliated to J.N.T.U-GV., Vizianagaram)
NAAC “A+” Accredited Institute
Cherukupally (Village), Near Tagarapuvalasa Bridge,Vizianagaram (Dist)-531162.

4.2 Feature Engineering ----------------------------------------------- 14

4.3 Model Selection and Training ------------------------------------ 14
4.4 Evaluation Metrics: ------------------------------------------------ 15
4.5 Deployment and Monitoring -------------------------------------- 15

Chapter Five: Summary, Conclusion and Recommendation

5.1 Summary ----------------------------------------------------------- 16
5.2 Conclusion---------------------------------------------------------- 16
5.3 Recommendation--------------------------------------------------- 17

Chapter Six: Project Execution

6.0 Project Overview--------------------------------------------------- 19
6.1 Key Features-------------------------------------------------------- 19
6.2 System Requirements--------------------------------------------- 19
6.3 Project Structure--------------------------------------------------- 20
6.4 Code Walkthrough------------------------------------------------- 21
6.5 How to Use---------------------------------------------------------- 23
6.6 Example Workflow------------------------------------------------- 23
6.7 Future Enhancements-------------------------------------------- 24
Chapter Seven: Codes and Outputs
7.0 Program using Python-------------------------------------------- 25
7.1 Output of the Program--------------------------------------------------- 30

Chapter Eight: Certificate-------------------------------------------- 32

1.0 Introduction

In the rapidly evolving world of e-commerce, understanding

customer behaviour and purchase patterns is crucial for

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 Avanthi Institute of Engineering and Technology
(Approved by A.I.C.T.E., New Delhi, & Permanently Affiliated to J.N.T.U-GV., Vizianagaram)
NAAC “A+” Accredited Institute
Cherukupally (Village), Near Tagarapuvalasa Bridge,Vizianagaram (Dist)-531162.

businesses to thrive. Data science, with its powerful analytical

tools and techniques, offers a way to gain deep insights into
these patterns. Python, a versatile and widely-used
programming language, is particularly well-suited for this task
due to its extensive libraries and ease of use.
This project aims to leverage Python's capabilities to analyze e-
commerce purchase data. By examining various aspects such
as customer demographics, purchase frequency, and product
preferences, we can uncover valuable trends and insights.
These insights can help businesses optimize their marketing
strategies, improve customer satisfaction, and ultimately drive sales
growth.

Throughout this project, we will utilize popular Python libraries

such as Pandas for data manipulation, Matplotlib and Seaborn
for data visualization, and Scikit-learn for machine learning. By
the end of this analysis, we hope to provide actionable
recommendations that can enhance the overall e-commerce
experience for both businesses and customers

1.1 Problem Statement

In the competitive landscape of e-commerce, businesses are

constantly seeking ways to enhance customer experience,
optimize marketing strategies, and increase sales. However,
understanding the vast amounts of data generated from
customer interactions and purchases can be challenging. This
project aims to address the following key questions:
1. Customer Segmentation: How can we segment
customers based on their purchase behavior and
demographics to tailor marketing efforts more effectively?
2. Purchase Patterns: What are the common purchase
patterns and trends among different customer segments?

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 Avanthi Institute of Engineering and Technology
(Approved by A.I.C.T.E., New Delhi, & Permanently Affiliated to J.N.T.U-GV., Vizianagaram)
NAAC “A+” Accredited Institute
Cherukupally (Village), Near Tagarapuvalasa Bridge,Vizianagaram (Dist)-531162.

3. Product Recommendations: How can we develop a

recommendation system to suggest products that
customers are likely to purchase?
4. Churn Prediction: Can we predict which customers are
likely to stop purchasing from the platform, and what
factors contribute to customer churn?
5. Sales Forecasting: How can we accurately forecast
future sales to manage inventory and supply chain
operations efficiently?
To tackle these questions, we will leverage Python's powerful
data science libraries such as Pandas for data manipulation,
Matplotlib and Seaborn for data visualization, and Scikit-learn
for machine learning. By analyzing historical purchase data, we
aim to uncover actionable insights that can drive business
growth and improve customer satisfaction.

2.0 Historical Overview

The origins of data science trace back to the late 20th century
when statistical methods were predominantly used for data
analysis. With the advent of computers, these methods evolved
into computational statistics, providing the foundation for
modern data science. Python, initially released in 1991, gained
popularity in the early 2000s due to its simplicity and powerful
libraries like NumPy, Pandas, and Scikit-learn. These
libraries transformed Python into a primary tool for data
analysis and machine learning.
Machine learning, a key domain within data science, emerged
as a prominent field in the 1950s with the development of
algorithms such as perceptrons and decision trees. By the
2000s, advancements in hardware and data availability
accelerated progress, making machine learning more
accessible through Python's extensive ecosystem.

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 Avanthi Institute of Engineering and Technology
(Approved by A.I.C.T.E., New Delhi, & Permanently Affiliated to J.N.T.U-GV., Vizianagaram)
NAAC “A+” Accredited Institute
Cherukupally (Village), Near Tagarapuvalasa Bridge,Vizianagaram (Dist)-531162.

2.1 Emerging Trends and Future Directions

1. Automated Machine Learning (AutoML): Tools like
Auto-sklearn and H2O.ai are making it easier to develop
machine learning models with minimal coding,
streamlining model selection, hyperparameter tuning, and
deployment.
2. Explainable AI (XAI): Techniques such as LIME and
SHAP are being integrated into Python workflows to
improve the interpretability of machine learning models.
3. Real-Time Data Processing: Libraries like Dask and
PySpark are enabling scalable, real-time data processing
for tasks like recommendation systems and fraud
detection.
4. Deep Learning and Integration with AI: Frameworks
like TensorFlow and PyTorch have extended Python's
capabilities in building neural networks, with applications
in natural language processing, computer vision, and
reinforcement learning.
5. Edge Computing and IoT: Python is increasingly used in
IoT devices for data collection and edge analytics, thanks
to lightweight libraries like MicroPython.
Future directions include tighter integration with quantum
computing for complex data problems, advancing federated
learning for privacy-preserving machine learning, and
leveraging Python for sustainable AI by optimizing
computational efficiency.

2.2 Literature Survey of Data Science

1. Linear Models (1950s): Early research focused on linear
regression and perceptrons, laying the groundwork for
supervised learning.

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Avanthi Institute of Engineering and Technology
(Approved by A.I.C.T.E., New Delhi, & Permanently Affiliated to J.N.T.U-GV., Vizianagaram)
NAAC “A+” Accredited Institute
Cherukupally (Village), Near Tagarapuvalasa Bridge,Vizianagaram (Dist)-531162.

2. Support Vector Machines (1990s): Papers like Vapnik's

work on SVMs provided robust methods for classification
tasks. Python’s Scikit-learn implements these with ease.
3. Deep Learning (2010s): The resurgence of neural
networks, catalyzed by works like AlexNet, popularized
libraries such as Keras and TensorFlow for Python.
4. Ensemble Methods (2000s): Techniques like Random
Forests and Gradient Boosting gained attention due to
their robustness, now widely available in Python.
5. Reinforcement Learning: Foundational research by
Sutton and Barto, now implementable using OpenAI Gym
and Stable-Baselines in Python, enables cutting-edge
applications like robotics and gaming.

2.3 Algorithms and Techniques

1. Supervised Learning:
o Algorithms: Linear Regression, Support Vector
Machines, Neural Networks
o Techniques: Cross-validation, Hyperparameter tuning
o Python Tools: Scikit-learn, TensorFlow, Keras
2. Unsupervised Learning:
o Algorithms: K-Means, DBSCAN, PCA
o Techniques: Dimensionality reduction, Clustering
analysis
o Python Tools: Scikit-learn, Scipy
3. Reinforcement Learning:
o Algorithms: Q-learning, Deep Q Networks (DQN)
o Techniques: Policy optimization, Actor-Critic methods

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Avanthi Institute of Engineering and Technology
(Approved by A.I.C.T.E., New Delhi, & Permanently Affiliated to J.N.T.U-GV., Vizianagaram)
NAAC “A+” Accredited Institute
Cherukupally (Village), Near Tagarapuvalasa Bridge,Vizianagaram (Dist)-531162.

o Python Tools: Gym, Stable-Baselines3

4. Data Processing and Feature Engineering:
o Techniques: Data imputation, Encoding, Feature
selection
o Python Tools: Pandas, NumPy, Feature-engine
5. Deep Learning:
o Algorithms: Convolutional Neural Networks (CNNs),
Recurrent Neural Networks (RNNs)
o Python Tools: PyTorch, TensorFlow, Keras

2.4 Challenges and Limitations

1. Data Quality and Preprocessing:
o Issues: Inconsistent, noisy, or missing data often
hampers analysis.
o Mitigation: Tools like Pandas and NumPy simplify
preprocessing, but domain expertise is still essential.
2. Scalability:
o Issue: Processing large datasets requires significant
computational resources.
o Mitigation: Distributed frameworks like Dask or cloud
solutions.
3. Interpretability:
o Issue: Complex models (e.g., deep learning) act as
black boxes.
o Mitigation: Tools like SHAP and LIME provide
interpretability, but trade-offs with model accuracy
persist.

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 Avanthi Institute of Engineering and Technology
(Approved by A.I.C.T.E., New Delhi, & Permanently Affiliated to J.N.T.U-GV., Vizianagaram)
NAAC “A+” Accredited Institute
Cherukupally (Village), Near Tagarapuvalasa Bridge,Vizianagaram (Dist)-531162.

4. Bias and Ethical Concerns:

o Issue: Machine learning models can unintentionally
reinforce societal biases.
o Mitigation: Bias detection libraries like Fairlearn, but
ongoing monitoring is necessary.
5. Model Deployment and Maintenance:
o Issue: Transitioning from development to production
and keeping models updated is challenging.
o Mitigation: Tools like MLflow and Docker assist in
deployment but require expertise.

3.0 Introduction to Data Science with

Python
Data science is a multidisciplinary field that combines statistical
techniques, computer science, and domain expertise to extract
meaningful insights and drive decision-making from data.
Python has emerged as the preferred language for data science
due to its simplicity, versatility, and a vast ecosystem of
libraries tailored for data manipulation, analysis, visualization,
and machine learning.
Python's libraries such as Pandas, NumPy, Matplotlib, and
Scikit-learn provide robust tools for every stage of the data
science workflow, from data wrangling to advanced predictive
modeling. With its open-source nature and active community,
Python continues to evolve, offering cutting-edge solutions for
data science challenges. This section introduces the
foundational concepts of data science and the pivotal role
Python plays in its implementation.

3.1 Data Collection and Preprocessing with

Python
Data collection and preprocessing are the initial and most
critical steps in the data science pipeline. This stage involves

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 Avanthi Institute of Engineering and Technology
(Approved by A.I.C.T.E., New Delhi, & Permanently Affiliated to J.N.T.U-GV., Vizianagaram)
NAAC “A+” Accredited Institute
Cherukupally (Village), Near Tagarapuvalasa Bridge,Vizianagaram (Dist)-531162.

gathering data from diverse sources, such as web scraping,

APIs, and relational databases, followed by cleaning and
transforming the data to make it suitable for analysis.
Python offers a plethora of tools for data collection, such as
Requests, BeautifulSoup, and Selenium for web scraping,
and libraries like SQLAlchemy for interacting with databases.
For preprocessing, libraries like Pandas and NumPy provide
functions to handle missing data, normalize features, and
perform data transformations. This section details the practical
approaches for collecting and preprocessing data using Python,
ensuring data quality and readiness for analysis.

3.2 Exploratory Data Analysis (EDA) with

Python
Exploratory Data Analysis (EDA) is a crucial step in data science
that involves summarizing and visualizing data to uncover
patterns, detect anomalies, and formulate hypotheses. Python’s
visualization libraries, such as Matplotlib, Seaborn, and
Plotly, allow for creating intuitive and interactive charts to
explore data distributions, correlations, and trends.
This section explores statistical techniques like summary
statistics and correlation analysis, combined with visual tools,
to analyze datasets effectively. Python scripts for histogram
generation, scatter plot matrices, and heatmaps are covered,
enabling data scientists to build a comprehensive
understanding of their data before modeling.

3.3 Feature Engineering and Selection in

Python
Feature engineering is the process of creating new input
features or modifying existing ones to enhance the predictive
power of machine learning models. Feature selection involves

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 Avanthi Institute of Engineering and Technology
(Approved by A.I.C.T.E., New Delhi, & Permanently Affiliated to J.N.T.U-GV., Vizianagaram)
NAAC “A+” Accredited Institute
Cherukupally (Village), Near Tagarapuvalasa Bridge,Vizianagaram (Dist)-531162.

identifying the most relevant features to reduce dimensionality

and improve model performance.
Python libraries like Scikit-learn provide tools for encoding
categorical variables, scaling numerical features, and selecting
features using techniques such as Recursive Feature
Elimination (RFE), mutual information, and principal component
analysis (PCA). This section details how to design, evaluate, and
implement feature engineering pipelines in Python, ensuring
that models are efficient and accurate.

3.4 Time Series Analysis with Python

Time series analysis focuses on analyzing data points collected
or recorded at specific time intervals to uncover trends,
patterns, and seasonal effects. It has applications in
forecasting, anomaly detection, and financial modeling.
Python libraries like statsmodels and Prophet simplify tasks
such as trend decomposition, seasonality analysis, and building
predictive models. This section covers the fundamentals of
handling time-indexed data using Pandas, applying smoothing
techniques, and creating advanced models for forecasting.
Real-world examples demonstrate how to analyze and predict
temporal patterns effectively.

3.5 Big Data Processing with Python

As datasets grow in size and complexity, traditional tools may
struggle to process them efficiently. Python's integration with
big data frameworks like Dask and PySpark provides scalable
solutions for processing and analyzing large datasets.
This section explains how to use these tools for distributed data
processing, highlighting key concepts such as parallelism, lazy
evaluation, and cluster computing. Practical examples
demonstrate how Python enables efficient workflows for
handling big data, optimizing both speed and resource usage.

Page. 12
 Avanthi Institute of Engineering and Technology
(Approved by A.I.C.T.E., New Delhi, & Permanently Affiliated to J.N.T.U-GV., Vizianagaram)
NAAC “A+” Accredited Institute
Cherukupally (Village), Near Tagarapuvalasa Bridge,Vizianagaram (Dist)-531162.

3.6 Ethical and Social Implications of Data

Science
The rapid adoption of data science has raised ethical concerns
around privacy, fairness, and transparency. Biased algorithms,
unauthorized data usage, and opaque decision-making
processes are some of the critical issues facing the field.
This section discusses best practices for ethical data science,
including data anonymization, fairness auditing, and
transparent reporting. Tools like Fairlearn and AI Fairness
360 are introduced to mitigate biases and ensure responsible
use of data science. The discussion highlights the importance of
adhering to ethical principles to build trust and accountability.

3.7 Emerging Trends and Future Directions

in Data Science with Python
Data science continues to evolve, driven by emerging
technologies and methodologies. Automated Machine Learning
(AutoML), real-time analytics, and the integration of AI and IoT
are reshaping the field. Python remains at the forefront of these
developments, with tools like H2O.ai for automation, Dash for
real-time dashboards, and PyCaret for rapid model building.
This section explores the future of data science, including
advancements in edge computing, quantum machine learning,
and sustainable AI. Python's adaptability ensures its relevance
in addressing these trends and contributing to the continuous
innovation of data science.

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 Avanthi Institute of Engineering and Technology
(Approved by A.I.C.T.E., New Delhi, & Permanently Affiliated to J.N.T.U-GV., Vizianagaram)
NAAC “A+” Accredited Institute
Cherukupally (Village), Near Tagarapuvalasa Bridge,Vizianagaram (Dist)-531162.

4.0 Introduction
In the dynamic world of E-commerce, businesses face the
constant challenge of understanding and predicting customer
behavior to enhance their services and increase sales. The vast
amount of data generated from online transactions provides a
valuable resource for gaining insights into customer
preferences, purchase patterns, and market trends. However,
analyzing this data effectively requires robust data science
techniques and tools.
This project focuses on leveraging Python, a powerful
programming language widely used in data science, to analyze
e-commerce purchase data. By examining various aspects such
as customer demographics, purchase frequency, and product
preferences, we aim to uncover valuable insights that can help
businesses optimize their marketing strategies, improve
customer satisfaction, and drive sales growth.
Through this analysis, we will utilize popular Python libraries
such as Pandas for data manipulation, Matplotlib and Seaborn
for data visualization, and Scikit-learn for machine learning. The
goal is to provide actionable recommendations that can
enhance the overall e-commerce experience for both
businesses and customers.

4.1 Data Collection and Preprocessing

1. Data Collection:
o "Collect transaction data including transaction ID,
customer ID, product ID, quantity, price, and date of
purchase."
o "Gather customer data such as demographics (age,
gender, location) and customer behavior (browsing
history, purchase history)."
o "Acquire product data including product ID, category,
price, and stock levels."

Page. 14
Avanthi Institute of Engineering and Technology
(Approved by A.I.C.T.E., New Delhi, & Permanently Affiliated to J.N.T.U-GV., Vizianagaram)
NAAC “A+” Accredited Institute
Cherukupally (Village), Near Tagarapuvalasa Bridge,Vizianagaram (Dist)-531162.

o "Extract web analytics data from website

interactions, such as page views, click-through rates,
and session durations."
2. Data Cleaning:
o "Identify and handle missing values in the dataset."
o "Remove duplicate records from the data."
o "Correct any inconsistencies or errors in the data,
such as incorrect entries or outliers."
3. Data Transformation:
o "Normalize numerical data to a standard range,
typically between 0 and 1."
o "Convert categorical data into numerical format using
techniques like one-hot encoding."
o "Convert date and time data into a standard format
and extract useful features like day of the week,
month, or year."
4. Data Integration:
o "Merge data from different sources into a single
dataset for analysis."
o "Create new features from existing data to enhance
the predictive power of the model."
5. Data Reduction:
o "Use techniques like Principal Component Analysis
(PCA) to reduce the number of features while
retaining important information."
o "If the dataset is too large, use sampling techniques
to create a manageable subset for analysis."

4.2 Feature Engineering

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 Avanthi Institute of Engineering and Technology
(Approved by A.I.C.T.E., New Delhi, & Permanently Affiliated to J.N.T.U-GV., Vizianagaram)
NAAC “A+” Accredited Institute
Cherukupally (Village), Near Tagarapuvalasa Bridge,Vizianagaram (Dist)-531162.

Feature engineering is a critical step that enhances the

functionality of the bill generator by defining meaningful
calculations and outputs. Key features include:
1 Customer Information:
 Customer ID: Unique identifier for each customer.
 Demographics: Age, gender, location, and other personal
details.
 Customer Lifetime Value (CLV): Total revenue
generated by a customer over their entire relationship
with the business.
2 Transaction Details:
 Transaction ID: Unique identifier for each transaction.
 Date and Time: When the purchase was made.
 Payment Method: Credit card, PayPal, etc.
 Order Status: Completed, pending, canceled, etc.
3 Product Information:
 Product ID: Unique identifier for each product.
 Category: Type or category of the product.
 Price: Cost of the product.
 Discounts: Any discounts applied to the product.
4 Purchase Behavior:
 Purchase Frequency: How often a customer makes a
purchase.
 Average Order Value (AOV): Average amount spent per
order.
 Cart Abandonment Rate: Percentage of customers who
add items to their cart but do not complete the purchase.
5 Marketing and Engagement:

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 Avanthi Institute of Engineering and Technology
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Cherukupally (Village), Near Tagarapuvalasa Bridge,Vizianagaram (Dist)-531162.

 Referral Source: How the customer found the website

(e.g., search engine, social media, direct).
 Email Campaigns: Interaction with marketing emails
(opens, clicks).
 Loyalty Programs: Participation in loyalty or rewards
programs

4.3 Model Selection and Training

While this project does not involve traditional machine learning,
the logic embedded in the generate() function serves as the
"model" for calculations. The design ensures scalability and
modularity for incorporating future features, such as discounts
or dynamic pricing.
Advanced versions could use machine learning to predict
customer preferences or optimize menu pricing based on
historical data. Integrating such models would involve:
1. Training on Historical Data: Analyzing previous billing
data to identify trends.
2. Predictive Features: Recommending items based on
popular combinations or customer behavior.

4.4 Evaluation Metrics

The performance of the Restaurant Bill Generator is assessed
based on:
1. Accuracy: Ensuring calculations are mathematically
precise for all scenarios.
2. User Experience: Measuring ease of use and
responsiveness of the GUI.
3. Error Handling: Effectively managing invalid inputs and
providing meaningful feedback to users.
4. Scalability: Supporting additional menu items, charges,
or features without significant redesign.

Page. 17
 Avanthi Institute of Engineering and Technology
(Approved by A.I.C.T.E., New Delhi, & Permanently Affiliated to J.N.T.U-GV., Vizianagaram)
NAAC “A+” Accredited Institute
Cherukupally (Village), Near Tagarapuvalasa Bridge,Vizianagaram (Dist)-531162.

4.5 Deployment and Monitoring

The deployment of the system involves packaging it as a
standalone desktop application using tools like PyInstaller.
Post-deployment, monitoring ensures continuous operation and
customer satisfaction. Key steps include:
1. Deployment: Creating an executable file for easy
installation and usage.
2. Monitoring: Gathering feedback from end-users to
identify bugs or usability issues.
3. Updates and Maintenance: Adding new features,
updating tax rates, or refining the interface as needed.
Future iterations could include cloud-based monitoring for real-
time updates, centralized logging, and analytics dashboards to
track system usage and performance.

5.0 Summary
In the competitive e-commerce landscape, businesses strive to
understand and predict customer behavior to enhance services
and boost sales. This project leverages Python's powerful data
science libraries to analyze e-commerce purchase data, aiming
to uncover valuable insights and address key business
questions.
Key Objectives:
1. Customer Segmentation: Identify distinct customer
groups based on purchase behavior and demographics to
tailor marketing strategies.
2. Purchase Patterns: Analyze common purchase trends
among different customer segments.

Page. 18
 Avanthi Institute of Engineering and Technology
(Approved by A.I.C.T.E., New Delhi, & Permanently Affiliated to J.N.T.U-GV., Vizianagaram)
NAAC “A+” Accredited Institute
Cherukupally (Village), Near Tagarapuvalasa Bridge,Vizianagaram (Dist)-531162.

3. Product Recommendations: Develop a

recommendation system to suggest products that
customers are likely to purchase.
4. Churn Prediction: Predict which customers are likely to
stop purchasing and identify factors contributing to
customer churn.
5. Sales Forecasting: Accurately forecast future sales to
manage inventory and supply chain operations efficiently.
Data Collection and Preprocessing:
 Data Sources: Transaction data, customer data, product
data, and web analytics data.
 Data Cleaning: Handle missing values, remove
duplicates, and correct errors.
 Data Transformation: Normalize numerical data, encode
categorical variables, and convert date-time data.
 Data Integration: Merge data from different sources and
create new features.
 Data Reduction: Use techniques like Principal
Component Analysis (PCA) and sampling to manage large
datasets.
Tools and Techniques:
 Pandas: For data manipulation.
 Matplotlib and Seaborn: For data visualization.
 Scikit-learn: For machine learning and predictive
modeling.
By following these steps, businesses can gain actionable
insights to optimize marketing strategies, improve customer
satisfaction, and drive sales growth.

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 Avanthi Institute of Engineering and Technology
(Approved by A.I.C.T.E., New Delhi, & Permanently Affiliated to J.N.T.U-GV., Vizianagaram)
NAAC “A+” Accredited Institute
Cherukupally (Village), Near Tagarapuvalasa Bridge,Vizianagaram (Dist)-531162.

5.1 Conclusion
In e commerce purchase project, we explored the vast
potential of data science to analyze e-commerce purchase data
using Python. By leveraging powerful libraries such as Pandas,
Matplotlib, Seaborn, and Scikit-learn, we were able to gain
valuable insights into customer behavior, purchase patterns,
and market trends.
Our analysis focused on several key objectives, including
customer segmentation, purchase pattern identification,
product recommendation development, churn prediction, and
sales forecasting. Through meticulous data collection, cleaning,
transformation, and integration, we ensured that our dataset
was robust and ready for analysis.
The insights gained from this project can help e-commerce
businesses optimize their marketing strategies, improve
customer satisfaction, and drive sales growth. By
understanding customer preferences and behaviors, businesses
can tailor their offerings to meet the needs of different
customer segments, predict future sales trends, and develop
effective retention strategies.
Overall, this project demonstrates the power of data science in
transforming raw data into actionable insights, enabling
businesses to make informed decisions and stay competitive in
the dynamic e-commerce landscape.

5.2 Recommendations
To further enhance the functionality and utility of the
E commerce purchase , the following improvements and
additions are recommended:
Here are some recommendations for addressing e-commerce
purchase problems using data science with Python:

Page. 20
Avanthi Institute of Engineering and Technology
(Approved by A.I.C.T.E., New Delhi, & Permanently Affiliated to J.N.T.U-GV., Vizianagaram)
NAAC “A+” Accredited Institute
Cherukupally (Village), Near Tagarapuvalasa Bridge,Vizianagaram (Dist)-531162.

1. Customer Segmentation:
o Use clustering algorithms like K-means to segment
customers based on their purchase behavior and
demographics.
o Tailor marketing strategies to different customer
segments to improve engagement and conversion
rates.
2. Personalized Recommendations:
o Implement collaborative filtering or content-based
filtering techniques to develop a recommendation
system.
o Use machine learning models like matrix factorization
or neural networks to enhance recommendation
accuracy.
3. Churn Prediction:
o Build predictive models using logistic regression,
decision trees, or random forests to identify
customers at risk of churning.
o Analyze factors contributing to churn and develop
targeted retention strategies.
4. Sales Forecasting:
o Use time series analysis techniques like ARIMA or
Prophet to forecast future sales trends.
o Incorporate external factors such as seasonality,
promotions, and economic indicators to improve
forecasting accuracy.
5. Customer Lifetime Value (CLV):
o Calculate CLV using historical purchase data and
predictive modeling.

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 Avanthi Institute of Engineering and Technology
(Approved by A.I.C.T.E., New Delhi, & Permanently Affiliated to J.N.T.U-GV., Vizianagaram)
NAAC “A+” Accredited Institute
Cherukupally (Village), Near Tagarapuvalasa Bridge,Vizianagaram (Dist)-531162.

o Focus on high-CLV customers with personalized offers

and loyalty programs to maximize long-term
revenue.
6. Product Analysis:
o Analyze product performance using metrics like sales
volume, return rates, and customer reviews.
o Identify top-performing products and optimize
inventory management to reduce stockouts and
overstock situations.
7. Marketing Campaign Analysis:
o Evaluate the effectiveness of marketing campaigns
using A/B testing and conversion rate analysis.
o Optimize marketing spend by focusing on high-
performing channels and strategies.
8. Data Visualization:
o Use visualization libraries like Matplotlib and Seaborn
to create insightful charts and graphs.
o Present findings in a clear and visually appealing
manner to stakeholders.
By implementing these recommendations, e-commerce
businesses can gain valuable insights, improve customer
satisfaction, and drive sales growth.

6.0 Project Overview

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 Avanthi Institute of Engineering and Technology
(Approved by A.I.C.T.E., New Delhi, & Permanently Affiliated to J.N.T.U-GV., Vizianagaram)
NAAC “A+” Accredited Institute
Cherukupally (Village), Near Tagarapuvalasa Bridge,Vizianagaram (Dist)-531162.

This project aims to analyze e-commerce purchase data to

gain insights into customer behaviour and optimize business
strategies. Using Python's powerful libraries like Pandas,
Matplotlib, Seaborn, and Scikit-learn, we will perform customer
segmentation, identify purchase patterns, develop a
recommendation system, predict customer churn, and forecast
sales. The analysis will help businesses tailor marketing efforts,
improve customer satisfaction, and drive sales growth by
leveraging data-driven decisions.

6.1 Key Features

1. Customer Segmentation: Identify distinct customer groups
based on purchase behaviour and demographics to tailor
marketing strategies effectively.
2. Purchase Pattern Analysis: Analyze common purchase trends
and behaviours among different customer segments to optimize
product offerings and marketing efforts.
3. Recommendation System: Develop a personalized
recommendation system using machine learning techniques to
suggest products that customers are likely to purchase.
4. Churn Prediction and Sales Forecasting: Predict customer
churn and forecast future sales to improve customer retention
and manage inventory efficiently
o .

6.2 System Requirements

 Programming Language: Python 3.x
 Libraries Used:
 Pandas: For data manipulation and analysis. It provides data
structures like DataFrames to handle and analyze large datasets
efficiently.

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 Avanthi Institute of Engineering and Technology
(Approved by A.I.C.T.E., New Delhi, & Permanently Affiliated to J.N.T.U-GV., Vizianagaram)
NAAC “A+” Accredited Institute
Cherukupally (Village), Near Tagarapuvalasa Bridge,Vizianagaram (Dist)-531162.

 NumPy: For numerical operations and handling arrays. It is

often used alongside Pandas for data manipulation.
 Matplotlib: For data visualization. It helps create a wide range
of static, animated, and interactive plots.
 Seaborn: Built on top of Matplotlib, it provides a high-level
interface for drawing attractive and informative statistical
graphics.


6.3 Project Structure

The program is organized into three main components:
Introduction:
 Overview of the project
 Objectives and goals
Data Collection:
 Identify data sources (transaction data, customer data,
product data, web analytics data)
 Methods of data acquisition (APIs, web scraping,
databases)
Data Preprocessing:
 Data cleaning (handling missing values, removing
duplicates, correcting errors)
 Data transformation (normalization, encoding categorical
variables, date-time conversion)
 Data integration (merging data from different sources,
feature engineering)
 Data reduction (dimensionality reduction, sampling)
Exploratory Data Analysis (EDA):
 Descriptive statistics

Page. 24
Avanthi Institute of Engineering and Technology
(Approved by A.I.C.T.E., New Delhi, & Permanently Affiliated to J.N.T.U-GV., Vizianagaram)
NAAC “A+” Accredited Institute
Cherukupally (Village), Near Tagarapuvalasa Bridge,Vizianagaram (Dist)-531162.

 Data visualization (distribution plots, correlation

heatmaps, etc.)
 Identifying patterns and trends
Customer Segmentation:
 Clustering algorithms (e.g., K-means)
 Analysis of customer segments
Predictive Modeling:
 Building models (e.g., Random Forest, Logistic Regression)
 Model evaluation (confusion matrix, classification report)
 Hyperparameter tuning
Recommendation System:
 Collaborative filtering or content-based filtering
 Implementation of recommendation algorithms
Churn Prediction:
 Building churn prediction models
 Identifying factors contributing to churn
 Developing retention strategies
Sales Forecasting:
 Time series analysis (e.g., ARIMA, Prophet)
 Forecasting future sales trends
Results and Insights:
 Summarizing findings
 Actionable recommendations for business improvement
Appendix:
 Code snippets
 Additional charts and graphs

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Avanthi Institute of Engineering and Technology
(Approved by A.I.C.T.E., New Delhi, & Permanently Affiliated to J.N.T.U-GV., Vizianagaram)
NAAC “A+” Accredited Institute
Cherukupally (Village), Near Tagarapuvalasa Bridge,Vizianagaram (Dist)-531162.

6.4 Code Walkthrough

1. Importing Required Libraries

 import pandas as pd
 import numpy as np
 import matplotlib.pyplot as plt
 import seaborn as sns
 from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import StandardScaler,
OneHotEncoder
 from sklearn.cluster import KMeans
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.metrics import classification_report,
confusion_matrix
 from sklearn.decomposition import PCA
 from sklearn.linear_model import LinearRegression
 from sklearn.metrics import mean_squared_error
 import tkinter as tk
 from tkinter import filedialog.

2 Functions
Load data()
Data cleaning()
Data transformation()
Data visualisation()
Customer segmentation()
Predictive modelling()
Sales forecasting()

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Avanthi Institute of Engineering and Technology
(Approved by A.I.C.T.E., New Delhi, & Permanently Affiliated to J.N.T.U-GV., Vizianagaram)
NAAC “A+” Accredited Institute
Cherukupally (Village), Near Tagarapuvalasa Bridge,Vizianagaram (Dist)-531162.

6.5 How to Use

1. Run the Program: Execute the script in a Python
interpreter that supports GUI (e.g., IDLE or command line).
2. Enter Quantities: Input the desired quantities for each
menu item.
3. Generate Bill: Click the "Generate Bill" button to
calculate and display the bill details.
4. Reset Fields: Click the "Reset" button to clear all inputs
and start a new transaction.
5. Quit: Click the "Quit" button to exit the application.

6.6 Example Workflow

1. Enter 2 for Fries and 1 for Burger.
2. Click Generate Bill.
o Outputs:
 Cost of Meal: ₹160.00
 Service Charge: ₹16.00
 Sales Tax: ₹8.00
 GST: ₹28.80
 Total Amount: ₹212.80
3. Use Reset to clear inputs and outputs for another
transaction.

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 Avanthi Institute of Engineering and Technology
(Approved by A.I.C.T.E., New Delhi, & Permanently Affiliated to J.N.T.U-GV., Vizianagaram)
NAAC “A+” Accredited Institute
Cherukupally (Village), Near Tagarapuvalasa Bridge,Vizianagaram (Dist)-531162.

6.7 Future Enhancements

1. Dynamic Pricing: Allow menu item prices to be updated
dynamically.
2. Export to File: Save bill details as a PDF or text file.
3. Discounts: Add options for applying discounts and offers.
4. Database Integration: Store transaction data for future
analysis.
5. Responsive Design: Enhance the GUI to support varying
screen sizes.

7.0 Program using Python

# Import necessary libraries

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler,
OneHotEncoder
from sklearn.cluster import KMeans
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report,
confusion_matrix
from sklearn.decomposition import PCA
from sklearn.linear_model import LinearRegression

Page. 28
 Avanthi Institute of Engineering and Technology
(Approved by A.I.C.T.E., New Delhi, & Permanently Affiliated to J.N.T.U-GV., Vizianagaram)
NAAC “A+” Accredited Institute
Cherukupally (Village), Near Tagarapuvalasa Bridge,Vizianagaram (Dist)-531162.

from sklearn.metrics import mean_squared_error

# Load data
def load_data(file_path):
return pd.read_csv(file_path)

# Data cleaning
def clean_data(df):
df.dropna(inplace=True)
df.drop_duplicates(inplace=True)
return df

# Data transformation
def transform_data(df):
# Normalize numerical data
scaler = StandardScaler()
df['normalized_price'] = scaler.fit_transform(df[['price']])

# Encode categorical data

encoder = OneHotEncoder()
encoded_categories =
encoder.fit_transform(df[['category']]).toarray()
df = df.join(pd.DataFrame(encoded_categories,
columns=encoder.get_feature_names_out(['category'])))

# Convert date-time data

df['purchase_date'] = pd.to_datetime(df['purchase_date'])

Page. 29
 Avanthi Institute of Engineering and Technology
(Approved by A.I.C.T.E., New Delhi, & Permanently Affiliated to J.N.T.U-GV., Vizianagaram)
NAAC “A+” Accredited Institute
Cherukupally (Village), Near Tagarapuvalasa Bridge,Vizianagaram (Dist)-531162.

df['day_of_week'] = df['purchase_date'].dt.dayofweek
df['month'] = df['purchase_date'].dt.month
return df

# Data visualization
def visualize_data(df):
plt.figure(figsize=(10, 6))
sns.countplot(x='category', data=df)
plt.title('Product Category Distribution')
plt.show()

# Customer segmentation
def segment_customers(df):
kmeans = KMeans(n_clusters=5)
df['customer_segment'] =
kmeans.fit_predict(df[['normalized_price', 'quantity']])
return df

# Predictive modeling
def build_model(df):
X = df[['normalized_price', 'quantity', 'day_of_week',
'month']]
y = df['purchase']
X_train, X_test, y_train, y_test = train_test_split(X, y,
test_size=0.2, random_state=42)

model = RandomForestClassifier()

Page. 30
 Avanthi Institute of Engineering and Technology
(Approved by A.I.C.T.E., New Delhi, & Permanently Affiliated to J.N.T.U-GV., Vizianagaram)
NAAC “A+” Accredited Institute
Cherukupally (Village), Near Tagarapuvalasa Bridge,Vizianagaram (Dist)-531162.

model.fit(X_train, y_train)

y_pred = model.predict(X_test)
print(classification_report(y_test, y_pred))
print(confusion_matrix(y_test, y_pred))

# Sales forecasting
def forecast_sales(df):
df['sales'] = df['price'] * df['quantity']
X = df[['day_of_week', 'month']]
y = df['sales']

pca = PCA(n_components=2)
X_pca = pca.fit_transform(X)

model = LinearRegression()
model.fit(X_pca, y)

y_pred = model.predict(X_pca)
print('Mean Squared Error:', mean_squared_error(y, y_pred))

# Main function
def main():
file_path = 'ecommerce_data.csv'
df = load_data(file_path)
df = clean_data(df)

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 Avanthi Institute of Engineering and Technology
(Approved by A.I.C.T.E., New Delhi, & Permanently Affiliated to J.N.T.U-GV., Vizianagaram)
NAAC “A+” Accredited Institute
Cherukupally (Village), Near Tagarapuvalasa Bridge,Vizianagaram (Dist)-531162.

df = transform_data(df)
visualize_data(df)
df = segment_customers(df)
build_model(df)
forecast_sales(df)

if __name__ == "__main__":
main()

7.1 Output of the Program

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Avanthi Institute of Engineering and Technology
(Approved by A.I.C.T.E., New Delhi, & Permanently Affiliated to J.N.T.U-GV., Vizianagaram)
NAAC “A+” Accredited Institute
Cherukupally (Village), Near Tagarapuvalasa Bridge,Vizianagaram (Dist)-531162.

Page. 33
Avanthi Institute of Engineering and Technology
(Approved by A.I.C.T.E., New Delhi, & Permanently Affiliated to J.N.T.U-GV., Vizianagaram)
NAAC “A+” Accredited Institute
Cherukupally (Village), Near Tagarapuvalasa Bridge,Vizianagaram (Dist)-531162.

Page. 34
Avanthi Institute of Engineering and Technology
(Approved by A.I.C.T.E., New Delhi, & Permanently Affiliated to J.N.T.U-GV., Vizianagaram)
NAAC “A+” Accredited Institute
Cherukupally (Village), Near Tagarapuvalasa Bridge,Vizianagaram (Dist)-531162.

Chapter: Cetificate

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