INDUSTRIAL INTERNSHIP

TRAINING REPORT

Submitted by

Name : JAYAMALINI A
Register NO: 421223243021
B.Tech Artificial Intelligence and Data Science
 KARPAGA VINAYAGA
COLLEGE OF ENGINEERING AND
TECHNOLOGY
(Approved by AICTE, New Delhi, Affiliated to Anna
University, Chennai and Accredited by NAAC ‘A’ Grade)
GST Road, Chinnakolambakkam, Madhuranthagam Taluk,
Chengalpattu District – 603 308, Tamil Nadu

2025-2026
 KARPAGA VINAYAGA
COLLEGE OF ENGINEERING AND TECHNOLOGY
(Approved by AICTE, New Delhi, Affiliated to Anna University, Chennai and
Accredited by NAAC ‘A’ Grade)
GST Road, Chinnakolambakkam, Madhuranthagam Taluk, Chengalpattu
District – 603 308, Tamil Nadu

BONAFIDE CERTIFICATE

This is to certify that the Industrial Internship Training

titled “MACHINE LEARNING” undergone at “

TOWARDS TECHNOLOGY ” is the bonafide work of “

Ms. JAYAMALINI A REG. NO: 421223243021” who

carriedout the training work under my supervision. Certified

further, that to the best of my knowledge the work reported

here in does not form part of any other project report or

dissertation on the basis of which a degree or award was

conferred on an earlier occasion on this or any other

candidate.

Period of Training: 01/07/2025 to 31/07/2025

SIGNATURE SIGNATURE
HEAD OF THE DEPARTMENT INTERNSHIP
COORDINATOR
Department of Artificial Intelligence Department of Artificial
and Data Science Intelligence and Data Science
Karpaga Vinayaga College of Karpaga Vinayaga College of
Engineering and technology, Engineering and technology,
Chinna Kolambakkam, Chinna Kolambakkam,
Maduranthagam TK, Padalam, Maduranthagam TK, Padalam,
Chengalpattu-603308. Chengalpattu-603308.
 DECLARATION

I” JAYAMALINI A REG. 421223243021 ” hereby declare

that the internship entitled “ MACHINE LEARNING ” undergone

at “ TOWARDS TECHNOLOGY , CHENNAI” being submitted in

partial fulfilment of the requirements for the award of the Degree of

“B.TECH ARTIFICIAL INTELLIGENCE AND DATA SCIENCE ”

is the original work carried out by me. It has not formed the part of any

other project work submitted for award of any degree or diploma, either

in this or any other Institution.

Period of Training: 01/07/2025 to 31/07/2025

Name: JAYAMALINI A Place: Ariyalur

Reg.no: 421223243021 Date: 20/08/2025

INTERNSHIP CERTIFICATE
CO- PO & PSO MAPPING

CO1: Participate in the projects in industries during his or her industrial

training.

CO2: Describe use of advanced tools and techniques encountered during

industrial training.

CO3: Interact with industrial personnel and follow engineering practices

and discipline prescribed in industry.

CO4: Develop awareness about general workplace behavior and build

interpersonal and team skills.

CO5: Learn and adopt the engineer’s role and responsibilities with ethics

CO6: Prepare professional work reports and presentations.

PO PO PO PO PO PO PO PO PO PO1 PO1 PO1 PSO PSO
1 2 3 4 5 6 7 8 9 0 1 2 1 2
CO 3 3 3 3 3 3 3 3 3 3 3 3 3 3
1
CO 3 3 3 3 3 1 1 3
2
CO 3 1 3 3 3 1 1
3
CO 1 1 3 3 3 1 1
4
CO 3 1 3 3 3 2 1 1
5
CO 3 3 3 2 1
6
 EXECUTIVE SUMMARY

This report chronicles the key achievements and skills acquired during
a one-month industrial internship with Towards Technology, a period
of intensive learning from July 1, 2025, to July 31, 2025.

The internship was a capstone experience designed to bridge

theoretical knowledge from the B.Tech Artificial Intelligence and Data
Science curriculum with practical, real-world applications in the
domain of Machine Learning.

A central component of this training involved hands-on projects aimed

at developing proficiency in the end-to-end machine learning lifecycle.
My primary project focused on gaining in-depth knowledge of machine
learning fundamentals and advanced concepts.

This involved a multi-faceted approach, beginning with the crucial

stages of data collection, processing, and visualization. I then
progressed to machine learning model design, development, and
rigorous evaluation.

The final, and most significant, phase of the project was model
optimization and deployment, showcasing a complete understanding of
the entire process. Through these tasks, I gained invaluable practical
skills in applying algorithms, tuning models for performance, and
preparing them for real-world use.

The internship also provided a robust understanding of the critical

importance of data-driven decision-making and ethical considerations
in the field of AI.

The experience was instrumental in solidifying my foundational

knowledge and has provided a clear roadmap for my future career
aspirations in the rapidly evolving field of AI and data science.
 ACKNOWLEDGEMENT

First, I would like to thank TOWARDS TECHNOLOGY for giving me the

opportunity to do an internship within the organization.

With profound gratitude and due regards, I whole heartedly and sincerely
acknowledge with thanks the opportunity provided to me by our respectful
Director Dr. Meenakshi Annamalai, for the facilities provided to accomplish this
internship.

I thank our dedicated Principal, Dr. P. Kasinatha Pandian, for his valuable
suggestion and timely advice which helped me in completing the internship.

I am grateful to our Dean Dr. L. Subbaraj, for his effective advice and support for
the planning and processing of the internship.

I thank our respectable Head of the Department, Dr.Delsi kowsalyadavi, for his
continuous encouragement and valuable insights that greatly enhanced my
internship experience.

I wholeheartedly thank my respectable Internship Coordinator, Mr. R. Prem

Kumar, for his unrelenting support, without proper guidance this internship could
not have been completed.

I thank various faculty members and friends for their timely help and guidance in
one way or other, which went a long way in the completion of this internship.
 PREFACE

As a student of the Department of Artificial Intelligence and Data Science ,

I am required to undertake an industrial internship to enhance my knowledge and
skills. The purpose of this internship is to acquaint me with the practical
applications of theoretical concepts taught during my course period. This internship
provided a valuable opportunity to compare theoretical concepts with practical
experiences in the field.

This report is a culmination of my efforts and may reflect some deficiencies,

but it accurately represents my learning experience. The report summarizes the
output of my analysis and outlines the sequence of events during my internship.

I have prepared this report as a requirement for my first industrial internship.

As an aspiring engineer, I aim to utilize my knowledge to invent innovative
solutions that can help society overcome problems. To achieve this goal, I am
working with Towards Technology located at Jayankondam, Ariyalur.
 ABSTRACT

This report details the comprehensive internship experience

focused on Machine Learning and its applications in data-driven
solutions. The training, conducted at Towards Technology, provided
me with hands-on exposure to the complete machine learning
workflow.

My core objective was to acquire a practical understanding of

fundamental machine learning concepts such as supervised and
unsupervised learning, along with mastering the skills necessary for
model development and deployment.

The key project of the internship involved the development of an

end-to-end machine learning solution, from data acquisition to model
optimization. This required leveraging a range of techniques for data
processing and visualization.

Key tasks included feature engineering, model selection, and

performance evaluation. Furthermore, I gained significant experience
with various programming libraries and tools, which allowed me to
build and refine the model and enhanced my understanding of
scalable machine learning pipelines.

Although the internship presented challenges related to

optimizing models for high performance and handling large datasets,
it provided me with a solid and practical foundation in machine
learning.

This experience has not only deepened my technical skills but also
reinforced my commitment to a career in machine learning, equipping
me with the knowledge and tools necessary for continued growth in
this dynamic field.
 TABLE OF CONTENT

CHAPTER TITLE PAG

NO. E
NO.
Bonafide Certificate
Company Certificate
Declaration
CO- PO & PSO Mapping
Executive Summary
Acknowledgment
Preface
Abstract
Project Report
1. Introduction
Overview of Machine Learning
Internship Overview
2. Objectives of the Internship
Learning Goals
Skills to Be Developed
3. Machine Learning Concepts and Service Models
Supervised & Unsupervised Learning
Data Preprocessing and Feature Engineering
4. Google Colab Platforms Used During the
Internship
Zero Configuration
Collaboration
Accessibility
5. Hands-On Projects and Task
 Memory Leak Detection and Analysis
Customer Churn Prediction Model
6. Challenges Faced During the Internship
7. Conclusion
References

1. Introduction
 Machine learning, a dynamic subset of artificial intelligence, is a field
dedicated to enabling computers to learn and make decisions from data
without being explicitly programmed. It is a transformative discipline
that underpins many of the technological advancements seen today,
from personalized recommendation engines and fraud detection systems
to self-driving cars and medical diagnostics.

 The core principle of machine learning lies in the development of

algorithms that can identify patterns and relationships within vast
datasets, using these insights to perform tasks or make predictions. This
process empowers systems to adapt and improve their performance over
time through experience, much like humans do.

2. Objectives of the Internship

 The primary objectives of this internship were multifaceted, aimed at
holistic professional and technical growth. On a technical level, the main
goal was to gain hands-on experience in the complete end-to-end machine
learning workflow, from data collection to model deployment. This
included mastering various techniques for data processing, feature
engineering, and visualization, which are essential for building effective
models.
  A key objective was to develop proficiency in model design,
development, evaluation, and optimization, understanding how to select
appropriate algorithms and tune their parameters for maximum
performance. Beyond technical skills, the internship sought to develop
critical soft skills, such as professional communication, collaborative
teamwork, and problem-solving in a fast-paced work environment.

 A key objective was to transition from a theoretical understanding of

machine learning principles to a practical application in a professional
environment. This involved learning industry-standard best practices,
working with real-world datasets that are often messy and incomplete,
and adhering to project deadlines.

3. Machine Learning Concepts and Service

Models
 Supervised Learning: This is the most common machine learning
paradigm, where a model is trained on a labeled dataset. The two main
types of supervised learning are classification, which involves predicting
a categorical label (e.g., predicting if an email is spam or not), and
regression, which involves predicting a continuous value (e.g., predicting
house prices). Both projects undertaken during this internship were
examples of supervised learning. 

 Unsupervised Learning: In contrast to supervised learning, this

approach involves training a model on an unlabeled dataset. The goal is
to discover hidden patterns or intrinsic structures within the data. Key
applications include clustering, where similar data points are grouped
together, and dimensionality reduction, which simplifies complex data.

 Data Preprocessing and Feature Engineering: Before any model can

be trained, the data must be prepared. This is a crucial and time-consuming
step. Data preprocessing involves handling missing values, scaling features,
and encoding categorical variables. Feature engineering is the art and
science of creating new, more meaningful features from existing ones to
improve model performance.
 Model Evaluation: A model's success is not determined by its accuracy
alone. A range of metrics is used to evaluate performance, including precision,
recall, F1-score, and the confusion matrix for classification tasks, and Mean
Squared Error (MSE) or R-squared for regression tasks. Understanding these
metrics is vital for a comprehensive analysis of a model's effectiveness.

 Service Models: The internship introduced the use of machine learning as a

service (MLaaS) through platforms like Google Colab. This model provides
tools for building and deploying ML applications without the need for
managing underlying hardware. It abstractly provides computing resources
(CPU, GPU, and TPU), allowing developers to focus solely on the code.

4. Google Colab Platforms Used During the

Internship
The entire internship project was developed and executed using Google
Colaboratory (Colab), a free cloud-based platform that provides a powerful
environment for machine learning. Colab is a hosted Jupyter notebook service
that offers several key advantages for data science and AI development:

 Free GPU Access: Colab provides free access to powerful GPUs

(Graphical Processing Units) and TPUs (Tensor Processing Units), which
are essential for accelerating the training of computationally intensive
machine learning models. This eliminates the need for expensive local
hardware and democratizes access to high-performance computing. 

 Zero Configuration: Unlike setting up a local development

environment, Colab requires no configuration. All necessary libraries,
such as TensorFlow, Keras, PyTorch, Scikit-learn, Pandas, and NumPy,
are pre-installed and ready to use, allowing for a quick start to any
project.

 Collaboration: As a cloud-based platform, Colab facilitates seamless

collaboration. Multiple users can work on the same notebook
simultaneously, with changes synchronized in real-time. This feature was
 instrumental in my internship, as it allowed for easy code sharing with
my supervisor for feedback and debugging.

 Accessibility: All notebooks are stored in Google Drive, making them

accessible from any device with an internet connection. This provided the
flexibility to work from different locations and ensured my work was
always backed up.

5. Skills Acquired
Technical Skills
 Python Programming and Library Proficiency: I gained significant
proficiency in Python, the foundational language for the entire machine
learning workflow. This included extensive use of key libraries such as
Pandas for efficient data manipulation and analysis, NumPy for high-
performance numerical operations, and Scikit-learn for implementing a
variety of machine learning models. 

 Data Manipulation and Analysis: A core skill developed was the ability
to perform robust data preprocessing. This involved cleaning raw
datasets by handling missing values, managing outliers, and transforming
data types. I also became adept at feature engineering, which is the
process of creating new features from existing ones to improve model
performance, as demonstrated in both the memory leak detection and
customer churn projects.

 Model Development and Evaluation: I acquired practical experience in

the end-to-end model development lifecycle. This included selecting and
implementing appropriate machine learning algorithms, training models
on prepared datasets, and rigorously evaluating their performance using a
range of industry-standard metrics. I am now proficient in interpreting
accuracy, precision, recall, and F1-score to comprehensively assess a
model’s effectiveness.
  Cloud-Based Platform Expertise (Google Colab): The use of Google
Colab was central to my work. I became highly proficient in leveraging
this cloud-based platform for its key advantages, including its zero-
configuration setup, seamless collaboration features, and free access to
powerful GPUs and TPUs for accelerated model training. 

 Basic Model Deployment: For the customer churn project, I gained

initial exposure to model deployment by using the Flask framework to
create a simple web API. This provided a crucial understanding of how a
trained model can be integrated into a functional application for real-time
predictions.

Soft Skills
 Problem-Solving: I learned to approach complex, real-world problems
with a structured, analytical mindset. The challenges of dealing with
imbalanced datasets and computational resource constraints required me
to think critically and adapt my approach to find effective solutions. 

 Collaboration and Communication: Working with a supervisor on

shared Colab notebooks emphasized the importance of clear
communication. I learned to articulate my thought process, document my
code effectively, and discuss project progress and challenges in a
professional manner.

 Adaptability: The dynamic nature of the projects required me to quickly

learn and apply new algorithms and techniques. This experience fostered
a strong sense of adaptability and a proactive approach to continuous
learning.

 Time Management: The internship’s structured timeline and project

deadlines taught me to manage my time efficiently and prioritize tasks to
ensure that all project milestones were met on schedule .
6. Hands-On Projects and Task
6.1 Work 1: Memory Leak Detection and Analysis

 Project Overview: This project involved developing a predictive model

to identify and anticipate memory leaks in applications. Memory leaks,
where a program fails to release memory it no longer needs, can lead to
system performance degradation and eventual crashes. The goal was to
build a system that could analyze system logs and metrics to identify
abnormal memory usage patterns and trigger an alert before a full-blown
system crash occurred. This project was crucial for maintaining system
stability and performance.
 Technologies Used: The project was built primarily in Python, using the
Scikit-learn library for its robust machine learning algorithms. Pandas
and NumPy were used for data manipulation and numerical operations.
For data visualization, Matplotlib and Seaborn were employed to create
insightful plots of memory usage over time. The core algorithm used was
Isolation Forest, an unsupervised learning model well-suited for
anomaly detection.
 Project Implementation: The implementation began with acquiring log
data from a simulated environment. This data was then preprocessed to
extract relevant features such as memory usage over time, process IDs,
and application status. An Isolation Forest model was trained to identify
outliers that corresponded to known memory leak signatures. A key part
of the implementation was integrating a real-time alerting system that
would send notifications based on the model’s predictions. The model
was trained to output a "contamination" score, and any data point with a
score above a certain threshold was flagged as a potential memory leak.
 Challenges Faced: The most significant challenge was the highly
imbalanced nature of the dataset; memory leaks are rare events, making it
difficult to train a model to accurately identify them without a high rate of
false positives. This was overcome by a meticulous feature engineering
process and adjusting the model’s contamination parameter.
 Key Takeaways: This project highlighted the critical importance of a
deep understanding of the problem domain. A successful model isn't just
about the algorithm; it's about correctly preparing the data and
understanding the nuances of what constitutes an "anomaly" in the
context of memory usage.
6.2 Work 2: Customer Churn Prediction Model

 Project Overview: This project involved building a supervised

classification model to predict customer churn for a subscription-based
service. The objective was to identify customers who are at high risk of
canceling their subscriptions, allowing the business to take proactive
measures to retain them. This project was a great example of a business-
driven machine learning application.
 Technologies Used: The project was developed using Python, with
Pandas for data handling and Scikit-learn for model training. I
experimented with several classification models, including Logistic
Regression, Decision Trees, and Gradient Boosting. The final model was
deployed as a simple web API using Flask, a lightweight Python web
framework.
 Project Implementation: The project started with analyzing a dataset of
customer behavior and demographic information. The data was cleaned,
and features such as customer tenure, usage frequency, and support
interactions were engineered. Several classification models were trained
and evaluated. A Gradient Boosting Classifier proved to be the most
accurate. The final model was then deployed as a simple web API using
Flask, allowing for new customer data to be passed to the model for a
real-time churn prediction. This demonstrated a complete end-to-end
solution from data analysis to a functional application.
 Challenges Faced: The main challenge was the imbalance in the target
variable (churn vs. no churn), which was addressed by using stratified
sampling during the training and validation splits. Another challenge was
correctly interpreting the model's predictions and explaining the factors
contributing to churn.
 Key Takeaways: This work emphasized the business value of machine
learning. It's not just about building a technically sound model, but about
creating a tool that provides actionable insights. The deployment aspect
highlighted the importance of moving beyond a Jupyter notebook and
creating a functional, accessible application

7. Challenges Faced During the Internship

 Data Imbalance: Both projects involved working with imbalanced
datasets, where the class of interest (e.g., memory leaks, customer churn)
was significantly underrepresented. This made it difficult to train a model
that could accurately identify these rare events without a high rate of false
 positives. I addressed this by using techniques such as oversampling
(SMOTE) and adjusting class weights within the models, which helped
the algorithms focus more on the minority class.

 Computational Resources: While Google Colab provided free GPU

access, training complex models on large datasets still required careful
resource management. This taught me to write more efficient code,
optimize data loading processes, and understand the trade-offs between
model complexity and training time.

 Model Interpretability: While the models I built were highly accurate, a

significant challenge was explaining why they made certain predictions.
This is particularly important for business stakeholders who need to
understand the factors driving a model's output. I learned to use tools like
feature importance plots and partial dependence plots to interpret model
behavior and communicate findings effectively.

8. Conclusion and Future Recommendations

The industrial internship at Towards Technology was an profoundly
transformative experience that provided a solid and practical foundation for my
academic and professional journey. It successfully achieved all the stated
objectives, bridging the theoretical knowledge of Artificial Intelligence and
Data Science with real-world application.

I gained a deep appreciation for the entire machine learning lifecycle, from
the nuances of data preprocessing to the complexities of model deployment. The
projects undertaken provided hands-on experience in tackling authentic data
challenges and reinforced the importance of continuous learning and problem-
solving. Beyond the technical skills, the experience cultivated vital soft skills
such as communication, collaboration, and critical thinking.

The insights gained from this internship will serve as a strong foundation
for my future career. I am confident that the knowledge and skills I have
acquired during this internship will serve as a cornerstone for my future career. I
am deeply thankful for the opportunity and the guidance provided by the team
at Towards Technology.

Research Papers/Articles:
 Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel,
O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J.,
Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, E. (2011).
Scikit-learn: Machine Learning in Python. Journal of Machine Learning
Research, 12, 2825–2830.

 Liaw, A., & Wiener, M. (2002). Classification and Regression by

randomForest. R News, 2(3), 18–22.

 Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting

System. In Proceedings of the 22nd ACM SIGKDD International Conference on
Knowledge Discovery and Data Mining.

 Lison, P., & Kutlak, R. (2019). The Case for Data-Centric Machine Learning.
arXiv preprint arXiv:1905.10986.

 Fawcett, T. (2006). An Introduction to ROC Analysis. Pattern Recognition

Letters, 27(8), 861-874.

 Zheng, A. (2015). Feature Engineering for Machine Learning. O'Reilly

Media.

 Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT
Press. (For future recommendations section)
 WEEKLY REPORT

Date Day Work done

01/07/2025 Tuesday Explored the foundational concepts of Machine Learning,
understanding its role as a subset of AI.

02/07/2025 Learned about the different types of supervised learning,

1st Week

Wednesday
including classification and regression.

03/07/2025 Thursday Understood the importance of data collection and its direct impact
on model performance.

04/07/2025 Friday Started the critical process of data preprocessing, focusing on

cleaning and preparing datasets.

Date Day Work done

07/07/2025 Monday Learned various data visualization techniques to discover patterns and
insights in data.

08/07/2025 Tuesday Began the hands-on project of memory leak detection, setting up the
project environment.
2nd Week

09/07/2025 Wednesday  Acquired skills in using Pandas for efficient data

manipulation and analysis.

 Utilized NumPy to perform complex numerical operations on

the dataset.
10/07/2025 Thursday Began the model design phase, selecting an appropriate algorithm for
the project.
11/07/2025 Friday Focused on model evaluation, interpreting metrics like accuracy and
precision.

Date Day Work done

3rd Week

14/07/2025 Monday Understood how to optimize a model by fine-tuning its

hyperparameters.

15/07/2025 Tuesday Started the second hands-on project: customer churn prediction.
 16/07/2025 Wednesday Engaged in a deep dive into feature engineering, creating meaningful
features for the churn model.

17/07/2025 Thursday Used Google Colab's free GPU to accelerate the model training
process.

18/07/2025 Friday Explored different classification algorithms for the churn prediction
project, such as Logistic Regression.

Date Day Work done

21/07/2025 Monday Learned about the challenges of working with imbalanced datasets and
how to address them..

22/07/2025 Tuesday Gained experience in creating a basic web API using the Flask
framework for model deploymen
4th Week

23/07/2025 Wednesday Focused on making the model's predictions more interpretable for
business insights.

24/07/2025 Thursday Researched the concept of Explainable AI (XAI) to understand model

behavior.

25/07/2025 Friday  Faced the challenge of debugging model errors and identifying
root causes.

 Learned the importance of clear documentation for all code

and project steps.

Date Day Work done

28/07/2025 Monday Explored future recommendations for the project, such as real-time
integration.

29/07/2025 Tuesday Learned about the ethical considerations in the field of AI and
5th Week

data privacy.

30/07/2025 Wednesday Reflected on the soft skills acquired, including problem-solving and
communication.
31/07/2025 Thursday  Presented the final projects to the internship supervisor,
discussing the key takeaways.

 Completed the final draft of the internship report, concluding

the training.