VTU technical seminar 8Th Sem on Scikit-learn

VTU final year technical seminar

1. TECHNICAL SEMINAR ON
UNVEILING THE POWER OF
SCIKIT-LEARN
BY:
NAME : AMARNATH
USN : 1SK20CS003

2. TABLE OF CONTENT:
Abstact
1.Intruduction
1.1 Background of the study
1.2 Problem statement
1.3 Objective of the study
1.4 Scope of the study
1.5 A scikit-learn workflow
2.Review of the Literature
3.Result and Discussion
4.Conclusion and scope for the Future Work

3. Abstract
Scikit-Learn is a robust machine learning library in Python. Scikit-
Learn plays a pivotal role in simplifying complex machine learning
tasks, offering a wide array of algorithms and tools for data
preprocessing, model training, and evaluation. The abstract delves
into the significance of Scikit-Learn in the context of modern data-
driven applications and outlines the key topics that will be covered,
including its history, core components, popular algorithms, and
future developments.

4. 1.Introduction
Scikit-Learn, also referred to assklearn, is an open-source Python
machine learning library. t's built on top on NumPy(Python library for
numerical computing) and Matplotlib(Python library for data
visualization).
1.1Backgroundofthestudy
•Rising data volumes in diverse fields call for powerful and
accessible tools to harness data's potential.
•Machine learning revolutionizes data analysis, enabling data-driven
insights and decisions.
•However, implementing ML algorithms from scratch can be a
daunting task, requiring significant expertise and computational
resources.
•This is where scikit-learn steps in. Developed in Python, a widely
adopted programming language for data science, scikit-learn offers
a user-friendly and comprehensive library specifically designed for
machine learning tasks.

5. 1.2 Research problem
“The vast amount of data generated today presents a unique
challenge: how to extract meaningful insights that can inform
decision-making across various domains. This research problem
lies at the heart of machine learning (ML) –transforming raw data
into actionable knowledge”

6. 1.3 Objective of the study
The objective of this study is to comprehensively explore Scikit-Learn, a
prominent machine learning library in Python, with the following goals:
•Grasp Machine Learning Fundamentals:Understand core concepts
and how scikit-learn simplifies the process.
•Navigate scikit-learn'sToolkit:Learn key functionalities for data prep,
model selection, and evaluation.
•Understanding Core Features: Gain an in-depth understanding of
Scikit-Learn'score features, functionalities, and capabilities.
•Exploring Algorithms:Explore the wide range of machine learning
algorithms offered by Scikit-Learn for tasks such as regression,
classification, clustering, and dimensionality reduction

7. 1.4 scope of the study
•Exploring Scikit-Learn'sFeatures: Analyzing the range of
algorithms, tools, and utilities offered by Scikit-Learn for machine
learning tasks.
•Essential Tools:Master data preprocessing, model selection,
training, and evaluation for project success
•.Algorithmic Exploration:Understand strengths and
applications of various algorithms relevant to project goals
(classification, regression, clustering).
•Handling Big Data:Exploring how Scikit-Learn can handle
large-scale datasets and its scalability in distributed computing
environments.

8. 1.5 Scikit-learn workflow

9. SI
no
Title and
Published
year
Author Methodology merits demerits
01Predictive
Model for
Classificatio
n of Power
System
Faults using
Machine
Learning
IEE2019
Tilottama
Goswami,
Uponika Barman
Roy,
The task of
classification
of faults is
implemented
using
supervised
machine
learning
algorithms in
Python and
scikit-learn
SVM
performed
excellent
giving a
performance
with 91.6%
test accuracy
for the
generated
dataset.
need for
more data to
make the
training
more robust
and the
scope for
future work
in identifying
the exact
location of
faults for a
more
reliable
power
system.
2.Review of the Literature

10. SI
no
Title and
Published
year
Author Methodology merits demerits
02Detecting
Fake News
using
Machine
Learning
and Deep
Learning
Algorithms ,
IEEE 2019
Abdullah-All-
Tanvir, Ehesas
Mia Mahir,
SaimaAkhter,
Mohammad
RezwanulHuq
Support Vector
Machine (SVM),
Naïve Bayes,
Logistic
Regression, Long
short-term memory
(LSTM), and
Recurrent Neural
Network
The study
provides a
detailed
comparison
of various
machine
learning
algorithms for
fake news
detection.
The current
approach does
not incorporate
domain
knowledge
features or
entity-
relationship
analysis.

11. SI
no
Title and
Published
year
Author Methodology merits demerits
03Stratification
of Parkinson
Disease
using python
scikit-learn
ML library,
IEEE 2019
Ashish Kolte,
Bodireddy
Mahitha, and
Dr. N V
Ganapathi
Raju.
The study involves
data collection
from the UCI
repository, data
pre-processing,
feature selection,
model building
using various
classifiers, and
model evaluation
with metrics like
accuracy,
precision, and
recall.
The paper
highlights the
use of
machine
learning
techniques
for accurate
Parkinson’s
disease
prediction,
which can aid
in early
diagnosis and
treatment.
need for more
accurate
results and
classification of
datasets with
more
dependent
features.

12. SI
no
Title and
Published
year
Author Methodology merits demerits
04Stratification
of Parkinson
Disease
using python
scikit-learn
ML library,
IEEE 2019
Ashish Kolte,
Bodireddy
Mahitha, and
Dr. N V
Ganapathi
Raju.
The study involves
data collection
from the UCI
repository, data
pre-processing,
feature selection,
model building
using various
classifiers, and
model evaluation
with metrics like
accuracy,
precision, and
recall.
The paper
highlights the
use of
machine
learning
techniques
for accurate
Parkinson’s
disease
prediction,
which can aid
in early
diagnosis and
treatment.
need for more
accurate
results and
classification of
datasets with
more
dependent
features.

13. SI
no
Title and
Published
year
Author Methodology merits demerits
03Apply Scikit-
Learn in
Python to
Analyze
Driver
Behavior
Based on
OBD Data
IEEE 2018
Chi-Pan
Hwang, Mu-
Song Chen,
Chih-Min Shih,
Hsing-Yu
Chen, Wen Kai
Liu
The research of
this paper has
focused on the
application layer in
the cloud
computing
platform, Python
has been adopted
to as the main
development tool
accompanying with
the Scikit-learn
Enables
chronic
collection of
driving
information
for Big Data
analysis..
Relies on
continuous
data
streaming,
which may
pose
challenges in
data
management.

14. Results and Discussion
•Algorithm Performance: Scikit-Learn'salgorithms excelled in tasks
like classification and regression, yet faced challenges with high-
dimensional data in clustering.
•Real-World Applications: Successfully applied in finance for stock
prediction and healthcare for disease diagnosis, highlighting practical
usability.
•Model Evaluation: Utilized cross-validation to mitigate overfitting and
optimize model parameters using techniques like grid search.
•Scalability and Efficiency: Showcased scalability with moderately-
sized datasets but identified challenges with large-scale data,
suggesting potential optimizations.
•Challenges and Recommendations: Addressed challenges with
imbalanced data using resampling methods and proposed
enhancements for model interpretability in complex algorithms.

15. 4 Conclusion and scope for the Future Work
Scikit-Learn emerges as a powerful and versatile machine
learning library, showcasing strong algorithm performance across
various tasks.
Real-world applications in finance and healthcare demonstrate its
practical usability and impact in decision-making processes.
Model evaluation techniques and scalability considerations further
enhance its appeal for diverse machine learning projects

16. Future Plan of Work
•Enhanced Model Interpretability:Explore and implement
advanced techniques for improving model interpretability,
ensuring transparency and trustworthiness in model
predictions.
•Scalability Solutions:Investigate strategies and
optimizations for enhancing Scikit-Learn'sscalability to
handle large-scale datasets efficiently.
•Integration with Deep Learning:Explore opportunities for
integrating Scikit-Learn with deep learning frameworks to
leverage hybrid models and tackle complex problems
effectively.
•Community Collaboration:Foster collaboration with the
Scikit-Learn community to contribute

17. THANK YOU