The document discusses building a machine learning model for resume classification using natural language processing techniques. It explores the dataset of resumes and profiles, performs text preprocessing, feature engineering, and builds various classification models to accurately classify resumes. The best performing model is random forest classification, which achieves 100% accuracy on the test data with no errors, overfitting, or misclassifications.
In this project, we dive into building a Machine learning model for Resume Classification using Python and basic Natural language processing techniques. We would be using Python's libraries to implement various NLP (natural language processing) techniques like tokenization, lemmatization, parts of speech tagging, etc.
A resume classification technology needs to be implemented in order to make it easy for the companies to process the huge number of resumes that are received by the organizations. This technology converts an unstructured form of resume data into a structured data format. The resumes received are in the form of documents from which the data needs to be extracted first such that the text can be classified or predicted based on the requirements. A resume classification analyzes resume data and extracts the information into the machine-readable output. It helps automatically store, organize, and analyze the resume data to find out the candidate for the particular job position and requirements. This thus helps the organizations eliminate the error-prone and time-consuming process of going through thousands of resumes manually and aids in improving the recruiters’ efficiency.
The basic data analysis process performed such as data collection, data cleaning, exploratory data analysis, data visualization, and model building. The dataset consists of two columns, namely, Role Applied and Resume, where the ‘role applied’ column is the domain field of the industry, and the ‘resume’ column consists of the text extracted from the resume document for each domain and industry.
The aim of this project is achieved by performing the various data analysis methods and using the Machine Learning models and Natural Language Processing which will help in classifying the categories of the resume and building the Resume Classification Model.
High level introduction to text mining analytics, which covers the building blocks or most commonly used techniques of text mining along with useful additional references/links where required for background/literature and R codes to get you started.
Top 100+ Google Data Science Interview Questions.pdfDatacademy.ai
Data science interviews can be particularly difficult due to the many proficiencies that you'll have to demonstrate (technical skills, problem solving, communication) and the generally high bar to entry for the industry.we Provide Top 100+ Google Data Science Interview Questions : All You Need to know to Crack it
visit by :-https://www.datacademy.ai/google-data-science-interview-questions/
Explainable AI - making ML and DL models more interpretableAditya Bhattacharya
Abstract –
Although industries have started to adopt AI and Machine Learning in almost every sector to solve complex business problems, but are these models always trustworthy? Machine Learning models are not any oracle but rather are scientific methods and mathematical models which best describes the data. But science is all about explaining complex natural phenomena in the simplest way possible! So, can we make ML and DL models more interpretable, so that any business user can understand these models and trust the results of these models?
In order to find out the answer, please join me in this session, in which I will take about concepts of Explainable AI and discuss its necessity and principles which help us demystify black-box AI models. I will be discussing about popular approaches like Feature Importance, Key Influencers, Decomposition trees used in classical Machine Learning interpretable. We will discuss about various techniques used for Deep Learning model interpretations like Saliency Maps, Grad-CAMs, Visual Attention Maps and finally go through more details about frameworks like LIME, SHAP, ELI5, SKATER, TCAV which helps us to make Machine Learning and Deep Learning models more interpretable, trustworthy and useful!
This is an introduction to text analytics for advanced business users and IT professionals with limited programming expertise. The presentation will go through different areas of text analytics as well as provide some real work examples that help to make the subject matter a little more relatable. We will cover topics like search engine building, categorization (supervised and unsupervised), clustering, NLP, and social media analysis.
1 Exploratory Data Analysis (EDA) by Melvin Ott, PhD.docxhoney725342
1
Exploratory Data Analysis (EDA)
by Melvin Ott, PhD
September, 2017
Introduction
The Masters in Predictive Analytics program at Northwestern University offers
graduate courses that cover predictive modeling using several software products
such as SAS, R and Python. The Predict 410 course is one of the core courses and
this section focuses on using Python.
Predict 410 will follow a sequence in the assignments. The first assignment will ask
you to perform an EDA(See Ratner1 Chapters 1&2) for the Ames Housing Data
dataset to determine the best single variable model. It will be followed by an
assignment to expand to a multivariable model. Python software for boxplots,
scatterplots and more will help you identify the single variable. However, it is easy
to get lost in the programming and lose sight of the objective. Namely, which of
the variable choices best explain the variability in the response variable?
(You will need to be familiar with the data types and level of measurement. This
will be critical in determining the choice of when to use a dummy variable for model
building. If this topic is new to you review the definitions at Types of Data before
reading further.)
This report will help you become familiar with some of the tools for EDA and allow
you to interact with the data by using links to a software product, Shiny, that will
demonstrate and interact with you to produce various plots of the data. Shiny is
located on a cloud server and will allow you to make choices in looking at the plots
for the data. Study the plots carefully. This is your initial EDA tool and leads to
your model building and your overall understanding of predictive analytics.
Single Variable Linear Regression EDA
1. Become Familiar With the Data
2
Identify the variables that are categorical and the variables that are quantitative.
For the Ames Housing Data, you should review the Ames Data Description pdf file.
2. Look at Plots of the Data
For the variables that are quantitative, you should look at scatter plots vs the
response variable saleprice. For the categorical variables, look at boxplots vs
saleprice. You have sample Python code to help with the EDA and below are some
links that will demonstrate the relationships for the a different building_prices
dataset.
For the boxplots with Shiny:
Click here
For the scatterplots with Shiny:
Click here
3. Begin Writing Python Code
Start with the shell code and improve on the model provided.
http://melvin.shinyapps.io/SboxPlot
http://melvin.shinyapps.io/SScatter/
http://melvin.shinyapps.io/SScatter/
3
Single Variable Logistic Regression EDA
1. Become Familiar With the Data
In 411 you will have an introduction to logistic regression and again will ask you to
perform an EDA. See the file credit data for more info. Make sure you recognize
which variables are quantitative and which are catego ...
In this project, we dive into building a Machine learning model for Resume Classification using Python and basic Natural language processing techniques. We would be using Python's libraries to implement various NLP (natural language processing) techniques like tokenization, lemmatization, parts of speech tagging, etc.
A resume classification technology needs to be implemented in order to make it easy for the companies to process the huge number of resumes that are received by the organizations. This technology converts an unstructured form of resume data into a structured data format. The resumes received are in the form of documents from which the data needs to be extracted first such that the text can be classified or predicted based on the requirements. A resume classification analyzes resume data and extracts the information into the machine-readable output. It helps automatically store, organize, and analyze the resume data to find out the candidate for the particular job position and requirements. This thus helps the organizations eliminate the error-prone and time-consuming process of going through thousands of resumes manually and aids in improving the recruiters’ efficiency.
The basic data analysis process performed such as data collection, data cleaning, exploratory data analysis, data visualization, and model building. The dataset consists of two columns, namely, Role Applied and Resume, where the ‘role applied’ column is the domain field of the industry, and the ‘resume’ column consists of the text extracted from the resume document for each domain and industry.
The aim of this project is achieved by performing the various data analysis methods and using the Machine Learning models and Natural Language Processing which will help in classifying the categories of the resume and building the Resume Classification Model.
High level introduction to text mining analytics, which covers the building blocks or most commonly used techniques of text mining along with useful additional references/links where required for background/literature and R codes to get you started.
Top 100+ Google Data Science Interview Questions.pdfDatacademy.ai
Data science interviews can be particularly difficult due to the many proficiencies that you'll have to demonstrate (technical skills, problem solving, communication) and the generally high bar to entry for the industry.we Provide Top 100+ Google Data Science Interview Questions : All You Need to know to Crack it
visit by :-https://www.datacademy.ai/google-data-science-interview-questions/
Explainable AI - making ML and DL models more interpretableAditya Bhattacharya
Abstract –
Although industries have started to adopt AI and Machine Learning in almost every sector to solve complex business problems, but are these models always trustworthy? Machine Learning models are not any oracle but rather are scientific methods and mathematical models which best describes the data. But science is all about explaining complex natural phenomena in the simplest way possible! So, can we make ML and DL models more interpretable, so that any business user can understand these models and trust the results of these models?
In order to find out the answer, please join me in this session, in which I will take about concepts of Explainable AI and discuss its necessity and principles which help us demystify black-box AI models. I will be discussing about popular approaches like Feature Importance, Key Influencers, Decomposition trees used in classical Machine Learning interpretable. We will discuss about various techniques used for Deep Learning model interpretations like Saliency Maps, Grad-CAMs, Visual Attention Maps and finally go through more details about frameworks like LIME, SHAP, ELI5, SKATER, TCAV which helps us to make Machine Learning and Deep Learning models more interpretable, trustworthy and useful!
This is an introduction to text analytics for advanced business users and IT professionals with limited programming expertise. The presentation will go through different areas of text analytics as well as provide some real work examples that help to make the subject matter a little more relatable. We will cover topics like search engine building, categorization (supervised and unsupervised), clustering, NLP, and social media analysis.
1 Exploratory Data Analysis (EDA) by Melvin Ott, PhD.docxhoney725342
1
Exploratory Data Analysis (EDA)
by Melvin Ott, PhD
September, 2017
Introduction
The Masters in Predictive Analytics program at Northwestern University offers
graduate courses that cover predictive modeling using several software products
such as SAS, R and Python. The Predict 410 course is one of the core courses and
this section focuses on using Python.
Predict 410 will follow a sequence in the assignments. The first assignment will ask
you to perform an EDA(See Ratner1 Chapters 1&2) for the Ames Housing Data
dataset to determine the best single variable model. It will be followed by an
assignment to expand to a multivariable model. Python software for boxplots,
scatterplots and more will help you identify the single variable. However, it is easy
to get lost in the programming and lose sight of the objective. Namely, which of
the variable choices best explain the variability in the response variable?
(You will need to be familiar with the data types and level of measurement. This
will be critical in determining the choice of when to use a dummy variable for model
building. If this topic is new to you review the definitions at Types of Data before
reading further.)
This report will help you become familiar with some of the tools for EDA and allow
you to interact with the data by using links to a software product, Shiny, that will
demonstrate and interact with you to produce various plots of the data. Shiny is
located on a cloud server and will allow you to make choices in looking at the plots
for the data. Study the plots carefully. This is your initial EDA tool and leads to
your model building and your overall understanding of predictive analytics.
Single Variable Linear Regression EDA
1. Become Familiar With the Data
2
Identify the variables that are categorical and the variables that are quantitative.
For the Ames Housing Data, you should review the Ames Data Description pdf file.
2. Look at Plots of the Data
For the variables that are quantitative, you should look at scatter plots vs the
response variable saleprice. For the categorical variables, look at boxplots vs
saleprice. You have sample Python code to help with the EDA and below are some
links that will demonstrate the relationships for the a different building_prices
dataset.
For the boxplots with Shiny:
Click here
For the scatterplots with Shiny:
Click here
3. Begin Writing Python Code
Start with the shell code and improve on the model provided.
http://melvin.shinyapps.io/SboxPlot
http://melvin.shinyapps.io/SScatter/
http://melvin.shinyapps.io/SScatter/
3
Single Variable Logistic Regression EDA
1. Become Familiar With the Data
In 411 you will have an introduction to logistic regression and again will ask you to
perform an EDA. See the file credit data for more info. Make sure you recognize
which variables are quantitative and which are catego ...
Objective of the Project
Tweet sentiment analysis gives businesses insights into customers and competitors. In this project, we combined several text preprocessing techniques with machine learning algorithms. Neural network, Random Forest and Logistic Regression models were trained on the Sentiment140 twitter data set. We then predicted the sentiment of a hold-out test set of tweets. We used both Python and PySpark (local Spark Context) to program different parts of the pre-processing and modelling.
Case Study 2 SCADA WormProtecting the nation’s critical infra.docxwendolynhalbert
Case Study 2: SCADA Worm
Protecting the nation’s critical infrastructure is a major security challenge within the U.S. Likewise, the responsibility for protecting the nation’s critical infrastructure encompasses all sectors of government, including private sector cooperation. Search on the Internet for information on the SCADA Worm, such as the article located athttp://www.theregister.co.uk/2010/09/22/stuxnet_worm_weapon/.
Write a three to five (3-5) page paper in which you:
1. Describe the impact and the vulnerability of the SCADA / Stuxnet Worm on the critical infrastructure of the United States.
2. Describe the methods to mitigate the vulnerabilities, as they relate to the seven (7) domains.
3. Assess the levels of responsibility between government agencies and the private sector for mitigating threats and vulnerabilities to our critical infrastructure.
4. Assess the elements of an effective IT Security Policy Framework, and how these elements, if properly implemented, could prevent or mitigate and attack similar to the SCADA / Stuxnet Worm.
5. Use at least three (3) quality resources in this assignment. Note: Wikipedia and similar Websites do not qualify as quality resources.
Your assignment must follow these formatting requirements:
· Be typed, double spaced, using Times New Roman font (size 12), with one-inch margins on all sides; citations and references must follow APA or school-specific format. Check with your professor for any additional instructions.
· Include a cover page containing the title of the assignment, the student’s name, the professor’s name, the course title, and the date. The cover page and the reference page are not included in the required assignment page length.
The specific course learning outcomes associated with this assignment are:
· Identify the role of an information systems security (ISS) policy framework in overcoming business challenges.
· Compare and contrast the different methods, roles, responsibilities, and accountabilities of personnel, along with the governance and compliance of security policy framework.
· Describe the different ISS policies associated with the user domain.
· Analyze the different ISS policies associated with the IT infrastructure.
· Use technology and information resources to research issues in security strategy and policy formation.
· Write clearly and concisely about Information Systems Security Policy topics using proper writing mechanics and technical style conventions.
DataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseDegreeGender1GrStudents: Copy the Student Data file data values into this sheet to assist in doing your weekly assignments.1601.053573485805.70METhe ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? 226.80.866315280703.90MBNote: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.334.71.120313075513.61FB457.91.01657 ...
Building an Immersive, Interactive Customer Experience using AI and Augmented...Amazon Web Services
Artificial Intelligence and Augmented Reality (AR) are quickly becoming mainstream digital strategies to add new immersive experiences across industries from video games to e-commerce, and to increase user accessibility. In this session, we will explore how we can get started on using AWS AI services with AR/VR capabilities of Amazon Sumerian to build a new type of visually rich, engaging mobile application to increase brand interaction and delight your customers. Come and join us to learn how you can get started creating your very first AI and AR powered app!
Recurrent Neural Networks hold great promise as general sequence learning algorithms. As such, they are a very promising tool for text analysis. However, outside of very specific use cases such as handwriting recognition and recently, machine translation, they have not seen wide spread use. Why has this been the case?
In this presentation, we will first introduce RNNs as a concept. Then we will sketch how to implement them and cover the tricks necessary to make them work well. With the basics covered, we will investigate using RNNs as general text classification and regression models, examining where they succeed and where they fail compared to more traditional text analysis models. A straightforward open-source Python and Theano library for training RNNs with a scikit-learn style interface will be introduced and we’ll see how to use it through a tutorial on a real world text dataset
Explore the power of Natural Language Processing (NLP) and Data Science in uncovering valuable insights from Flipkart product reviews. This presentation delves into the methodology, tools, and techniques used to analyze customer sentiments, identify trends, and extract actionable intelligence from a vast sea of textual data. From understanding customer preferences to improving product offerings, discover how NLP Data Science is revolutionizing the way businesses leverage consumer feedback on Flipkart. Visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
What is pattern recognition (lecture 4 of 6)Randa Elanwar
In this series I intend to simplify a beautiful branch of computer science that we as humans use it in everyday life without knowing. Pattern recognition is a sub-branch of the computer vision research and is tightly related to digital signal processing research as well as machine learning and artificial intelligence.
Rubric Name Copy of General Grading Rubric for Projects .docxjoellemurphey
Rubric Name: Copy of General Grading Rubric for Projects
Criteria
Equivalent to
an A
Equivalent to a
B
Equivalent to a C
Equivalent to
an F
Overall content of paper, analysis,
presentation, or project. Value: 20
points
20 points
Content of the
paper addresses
all information
required by
assignment,
demonstrates
critical thinking
skills,
sophisticated
analysis and
other
perspectives.
Points
available: 18-20
17.9 points
Content of the
paper addresses
most or all
information
required by
assignments and
demonstrates
critical thinking
skills, critical
analysis and
other
perspectives.
Points
available: 16-
17.9
15.9 points
Content of the paper
addresses a majority of
the information required
by the assignment and
demonstrates some
critical thinking skills,
critical analysis and
other perspectives.
Points available: 14-15.9
11.9 points
Content of the
paper addresses
a minimal
amount of the
information
required by the
assignment and
demonstration of
critical thinking
skills, critical
analysis and
other
perspectives is
lacking.
Points
available: 0-11.9
Overall content of paper, analysis,
presentation, or project Value: 20
points
20 points
Application of
theory and
knowledge is
very evident.
Use of topic-
specific
terminology is
correct in all
instances.
Organization is
relevant to topic,
clear and
understandable
with logical
flow.
Points
available: 18-20
17.9 points
Comprehensive
understanding of
theory and
knowledge is
shown. Use of
topic-specific
terminology has
only minor errors.
Minor mistakes in
organization and
style.
Points
available: 16-
17.9
15.9 points
Some understanding of
theory and knowledge is
shown. Topic-specific
terminology is mostly
correct. Organization is
mostly relevant, clear,
and logical.
Points available: 14-15.9
11.9 points
Understanding of
theory and
knowledge is
lacking in
significant
respects. Multiple
mistakes in topic-
specific
terminology.
Lacks relevance,
is unclear,
difficult to
understand, or
logic is missing.
Points
available: 0-11.9
Responsiveness to Project
Description e.g.; application of
theory and knowledge to given
facts Terminology, Organization,
etc. Value: 10 points
10 points
Assignment is
formatted exactly
as required, all
required citations
and references
are
presentand APA
standards are
8.9 points
Assignment is
formatted as
required with
minor/
inconsequential
deviations,
resource
requirements are
met, citations and
references are
7.9 points
Assignment mostly
formatted as required
but missing some
required elements/
sources or some APA
errors are evident.
Points available: 7-7.9
5.9 points
Assignment is
missing major
elements, lacks
required
sources or APA
is not followed
however a
different citation
followed in every
respect.
Points
available: 9-10
presen ...
Law firms & lawyers - rid the manual review of text documents, correspondence, etc. Text Analytics of unstructured documents signals potential knowledge that brings relevance & helps win cases. Moreover, use of text analytics helps offer small firms the same advantage that big firms have. As the information can be used to strengthen solutions and provide advice to attorneys, courtrooms will also benefit from more informed, better prepared legal teams and swift action, keeping long years of litigation away!
Embark on a transformative journey into the world of data science with Tsofttech Institution's comprehensive Data Science Excellence program. In today's data-driven world, harnessing the power of data is essential for making informed decisions and driving innovation.
Course Highlights:
Practical Learning: Our hands-on approach allows you to gain practical experience by working on real-world data science projects. You'll learn to extract insights, analyze trends, and make data-driven decisions.
Cutting-Edge Curriculum: Stay at the forefront of data science with a curriculum that covers the latest tools and techniques, including data analysis, machine learning, data visualization, and more.
Expert Instructors: Learn from seasoned data scientists and industry experts who will guide you through the intricacies of data analysis and modeling, providing valuable insights and mentorship.
Personalized Learning: Our flexible course modules cater to learners of all levels, whether you're a beginner or an experienced professional. We tailor your learning experience to meet your specific needs and goals.
Certification: Receive a prestigious certification upon completing the program, validating your data science skills and boosting your career prospects.
Key Topics Covered:
Data Cleaning and Preprocessing
Exploratory Data Analysis
Machine Learning Algorithms
Predictive Analytics
Data Visualization
Big Data Technologies
Deep Learning
Natural Language Processing (NLP)
Business Analytics
Capstone Projects
Open the doors to a world of opportunities with a solid foundation in data science from Tsofttech Institution. Whether you aim to drive business decisions, conduct advanced research, or seek career growth, our program equips you with the skills needed to excel in this dynamic field.
Join us today and start your journey towards Data Science Excellence at Tsofttech Institution!
Final presentation for Chuck Eesley's 'venture lab' Stanford online course on technology entrepreneurship.
The slides present a cloud based data analytics approach to organizational complexity reduction.
This was presented to software developers with the goal of introducing them to basic machine learning workflow, code snippets, possibilities and state-of-the-art in NLP and give some clues on where to get started.
Objective of the Project
Tweet sentiment analysis gives businesses insights into customers and competitors. In this project, we combined several text preprocessing techniques with machine learning algorithms. Neural network, Random Forest and Logistic Regression models were trained on the Sentiment140 twitter data set. We then predicted the sentiment of a hold-out test set of tweets. We used both Python and PySpark (local Spark Context) to program different parts of the pre-processing and modelling.
Case Study 2 SCADA WormProtecting the nation’s critical infra.docxwendolynhalbert
Case Study 2: SCADA Worm
Protecting the nation’s critical infrastructure is a major security challenge within the U.S. Likewise, the responsibility for protecting the nation’s critical infrastructure encompasses all sectors of government, including private sector cooperation. Search on the Internet for information on the SCADA Worm, such as the article located athttp://www.theregister.co.uk/2010/09/22/stuxnet_worm_weapon/.
Write a three to five (3-5) page paper in which you:
1. Describe the impact and the vulnerability of the SCADA / Stuxnet Worm on the critical infrastructure of the United States.
2. Describe the methods to mitigate the vulnerabilities, as they relate to the seven (7) domains.
3. Assess the levels of responsibility between government agencies and the private sector for mitigating threats and vulnerabilities to our critical infrastructure.
4. Assess the elements of an effective IT Security Policy Framework, and how these elements, if properly implemented, could prevent or mitigate and attack similar to the SCADA / Stuxnet Worm.
5. Use at least three (3) quality resources in this assignment. Note: Wikipedia and similar Websites do not qualify as quality resources.
Your assignment must follow these formatting requirements:
· Be typed, double spaced, using Times New Roman font (size 12), with one-inch margins on all sides; citations and references must follow APA or school-specific format. Check with your professor for any additional instructions.
· Include a cover page containing the title of the assignment, the student’s name, the professor’s name, the course title, and the date. The cover page and the reference page are not included in the required assignment page length.
The specific course learning outcomes associated with this assignment are:
· Identify the role of an information systems security (ISS) policy framework in overcoming business challenges.
· Compare and contrast the different methods, roles, responsibilities, and accountabilities of personnel, along with the governance and compliance of security policy framework.
· Describe the different ISS policies associated with the user domain.
· Analyze the different ISS policies associated with the IT infrastructure.
· Use technology and information resources to research issues in security strategy and policy formation.
· Write clearly and concisely about Information Systems Security Policy topics using proper writing mechanics and technical style conventions.
DataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseDegreeGender1GrStudents: Copy the Student Data file data values into this sheet to assist in doing your weekly assignments.1601.053573485805.70METhe ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? 226.80.866315280703.90MBNote: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.334.71.120313075513.61FB457.91.01657 ...
Building an Immersive, Interactive Customer Experience using AI and Augmented...Amazon Web Services
Artificial Intelligence and Augmented Reality (AR) are quickly becoming mainstream digital strategies to add new immersive experiences across industries from video games to e-commerce, and to increase user accessibility. In this session, we will explore how we can get started on using AWS AI services with AR/VR capabilities of Amazon Sumerian to build a new type of visually rich, engaging mobile application to increase brand interaction and delight your customers. Come and join us to learn how you can get started creating your very first AI and AR powered app!
Recurrent Neural Networks hold great promise as general sequence learning algorithms. As such, they are a very promising tool for text analysis. However, outside of very specific use cases such as handwriting recognition and recently, machine translation, they have not seen wide spread use. Why has this been the case?
In this presentation, we will first introduce RNNs as a concept. Then we will sketch how to implement them and cover the tricks necessary to make them work well. With the basics covered, we will investigate using RNNs as general text classification and regression models, examining where they succeed and where they fail compared to more traditional text analysis models. A straightforward open-source Python and Theano library for training RNNs with a scikit-learn style interface will be introduced and we’ll see how to use it through a tutorial on a real world text dataset
Explore the power of Natural Language Processing (NLP) and Data Science in uncovering valuable insights from Flipkart product reviews. This presentation delves into the methodology, tools, and techniques used to analyze customer sentiments, identify trends, and extract actionable intelligence from a vast sea of textual data. From understanding customer preferences to improving product offerings, discover how NLP Data Science is revolutionizing the way businesses leverage consumer feedback on Flipkart. Visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
What is pattern recognition (lecture 4 of 6)Randa Elanwar
In this series I intend to simplify a beautiful branch of computer science that we as humans use it in everyday life without knowing. Pattern recognition is a sub-branch of the computer vision research and is tightly related to digital signal processing research as well as machine learning and artificial intelligence.
Rubric Name Copy of General Grading Rubric for Projects .docxjoellemurphey
Rubric Name: Copy of General Grading Rubric for Projects
Criteria
Equivalent to
an A
Equivalent to a
B
Equivalent to a C
Equivalent to
an F
Overall content of paper, analysis,
presentation, or project. Value: 20
points
20 points
Content of the
paper addresses
all information
required by
assignment,
demonstrates
critical thinking
skills,
sophisticated
analysis and
other
perspectives.
Points
available: 18-20
17.9 points
Content of the
paper addresses
most or all
information
required by
assignments and
demonstrates
critical thinking
skills, critical
analysis and
other
perspectives.
Points
available: 16-
17.9
15.9 points
Content of the paper
addresses a majority of
the information required
by the assignment and
demonstrates some
critical thinking skills,
critical analysis and
other perspectives.
Points available: 14-15.9
11.9 points
Content of the
paper addresses
a minimal
amount of the
information
required by the
assignment and
demonstration of
critical thinking
skills, critical
analysis and
other
perspectives is
lacking.
Points
available: 0-11.9
Overall content of paper, analysis,
presentation, or project Value: 20
points
20 points
Application of
theory and
knowledge is
very evident.
Use of topic-
specific
terminology is
correct in all
instances.
Organization is
relevant to topic,
clear and
understandable
with logical
flow.
Points
available: 18-20
17.9 points
Comprehensive
understanding of
theory and
knowledge is
shown. Use of
topic-specific
terminology has
only minor errors.
Minor mistakes in
organization and
style.
Points
available: 16-
17.9
15.9 points
Some understanding of
theory and knowledge is
shown. Topic-specific
terminology is mostly
correct. Organization is
mostly relevant, clear,
and logical.
Points available: 14-15.9
11.9 points
Understanding of
theory and
knowledge is
lacking in
significant
respects. Multiple
mistakes in topic-
specific
terminology.
Lacks relevance,
is unclear,
difficult to
understand, or
logic is missing.
Points
available: 0-11.9
Responsiveness to Project
Description e.g.; application of
theory and knowledge to given
facts Terminology, Organization,
etc. Value: 10 points
10 points
Assignment is
formatted exactly
as required, all
required citations
and references
are
presentand APA
standards are
8.9 points
Assignment is
formatted as
required with
minor/
inconsequential
deviations,
resource
requirements are
met, citations and
references are
7.9 points
Assignment mostly
formatted as required
but missing some
required elements/
sources or some APA
errors are evident.
Points available: 7-7.9
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Law firms & lawyers - rid the manual review of text documents, correspondence, etc. Text Analytics of unstructured documents signals potential knowledge that brings relevance & helps win cases. Moreover, use of text analytics helps offer small firms the same advantage that big firms have. As the information can be used to strengthen solutions and provide advice to attorneys, courtrooms will also benefit from more informed, better prepared legal teams and swift action, keeping long years of litigation away!
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Final presentation for Chuck Eesley's 'venture lab' Stanford online course on technology entrepreneurship.
The slides present a cloud based data analytics approach to organizational complexity reduction.
This was presented to software developers with the goal of introducing them to basic machine learning workflow, code snippets, possibilities and state-of-the-art in NLP and give some clues on where to get started.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
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Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
1. RESUME
CLASSIFICATION
1 . ) M R . M O I N D A L V I
2 . ) M R . Z O H E B K A Z I
3 . ) M R . S O U D A L H O D A
5 . ) M R . A N A N D J A G D A L E
6 . ) M R . S W A P N I L W A D K A R
7 . ) M R . N A G E N D R A P
4 . ) S N E H A L L A W A N D E
2. B U S I N E S S O B J E C T I V E -
The document classification solution should significantly reduce the manual human effort in the HRM. It
should achieve a higher level of accuracy and automation with minimal human intervention
Abstract:
A resume is a brief summary of your skills and experience. Companies recruiters and HR teams have a tough time scanning
thousands of qualified resumes. Spending too many labor hours segregating candidates resume's manually is a waste of a
company's time, money, and productivity. Recruiters, therefore, use resume classification in order to streamline the resume
and applicant screening process. NLP technology allows recruiters to electronically gather, store, and organize large
quantities of resumes. Once acquired, the resume data can be easily searched through and analyzed.
Resumes are an ideal example of unstructured data. Since there is no widely accepted resume layout, each resume may have
its own style of formatting, different text blocks and different category titles. Building a resume classification and gathering
text from it is no easy task as there are so many kinds of layouts of resumes that you could imagine
3. I N T R O D U C T I O N :
In this project we dive into building a Machine learning model for Resume Classification using Python and basic Natural language
processing techniques. We would be using Python's libraries to implement various NLP (natural language processing) techniques like tokenization,
lemmatization, parts of speech tagging, etc.
A resume classification technology needs to be implemented in order to make it easy for the companies to process the huge number of
resumes that are received by the organizations. This technology converts an unstructured form of resume data into a structured data format. The
resumes received are in the form of documents from which the data needs to be extracted first such that the text can be classified or predicted based
on the requirements. A resume classification analyzes resume data and extracts the information into the machine readable output. It helps
automatically store, organize, and analyze the resume data to find out the candidate for the particular job position and requirements. This thus helps
the organizations eliminate the error-prone and time-consuming process of going through thousands of resumes manually and aids in improving the
recruiters’ efficiency.
The basic data analysis process is performed such as data collection, data cleaning, exploratory data analysis, data visualization,
and model building. The dataset consists of two columns, namely, Role Applied and Resume, where ‘role applied’ column is the domain field of
the industry and ‘resume’ column consists of the text extracted from the resume document for each domain and industry.
The aim of this project is achieved by performing the various data analytical methods and using the Machine Learning models and
Natural Language Processing which will help in classifying the categories of the resume and building the Resume Classification Model.
4. E X P L O R A T O R Y D A T A A N A L Y S I S :
5. E X P L O R A T O R Y D A T A A N A L Y S I S :
In this project we have total 9 types of Profiles in the Resumes, and the most of them are for Workday Profile.
6. E X P L O R A T O R Y D A T A A N A L Y S I S :
Extracting Text from different Resume files and creating a data-frame with Column of Text from
Resumes And Profile for which each of it Applied for.
13. F E A T U R E E N G I N E E R I N G :
Converting Extracted Above Data into a Data-Frame
To use this as Features (Predictors, Attributes or Input) for Model to Predict the different Classes
14. Text pre-processing includes converting to lowercase, removing spaces, html links, emails, symbols, numbers,
stop-words, tokenization and lemmatization.
Removing All Unwanted Character’s
Word Tokenization - Tokenization is essentially splitting a phrase, sentence, paragraph, or an entire text document into
smaller units, such as individual words or terms. Each of these smaller units are called tokens.
Removing Stop-words - A stop word is a commonly used word (such as “the”, “a”, “an”, “in”) that a search engine has been
programmed to ignore, both when indexing entries for searching and when retrieving them as the result of a search query.
T E X T P R E - P R O C E S S I N G :
16. Before Applying Porter Stemming
The Porter stemming algorithm (or 'Porter stemmer') is a process for removing the commoner morphological and
inflectional endings from words in English.
T E X T P R E - P R O C E S S I N G :
After Applying Porter Stemming
17. E X P L O R A T O R Y D A T A A N A L Y S I S :
19. E X P L O R A T O R Y D A T A A N A L Y S I S :
10 most common words used in each Profile Resumes
20. E X P L O R A T O R Y D A T A A N A L Y S I S :
21. E X P L O R A T O R Y D A T A A N A L Y S I S :
22. E X P L O R A T O R Y D A T A A N A L Y S I S :
23. E X P L O R A T O R Y D A T A A N A L Y S I S :
24. E X P L O R A T O R Y D A T A A N A L Y S I S :
25. E X P L O R A T O R Y D A T A A N A L Y S I S :
Classes in the Data-Frame
Plotting Classes for Insights
There are Total 4 Classes in the Data Frame which means this a Multiclass Classification Problem.
Imbalance found in the dataset we can use Oversampling Techniques.
26. E X P L O R A T O R Y D A T A A N A L Y S I S :
10 Most Common Words Used in Different Classes
29. F E A T U R E E N G I N E E R I N G :
Problems with imbalanced data classification
If explained it in a very simple manner, the main problem with imbalanceddataset prediction is how accurately are we actually predicting both
majorityand minority class?
•SMOTE: Synthetic Minority Oversampling Technique
SMOTE is an oversampling technique where the synthetic samples are generated for the minority class. This algorithm helps to
overcome the overfitting problem posed by random oversampling. It focuses on the feature space to generate new instances with
the help of interpolation between the positive instances that lie together.
30. T R A I N T E S T S P L I T :
Problems with Random Data Splitting
If explained it in a very simple manner, the main problem is random splitting the data the ratio of the classes does not reflect on training and
testing. Due to random splitting one class can be heavily sampled in training and creating majorityand minority class issue ( ImbalancedData)
which will give rise to bad scores on test data and overall performance and misclassification.
•Stratified Samling:
In stratifiedSampling the ratio of all the classes is maintained on both training and testing data thus this type of Split results in good accuracy
and overall model building performance.
31. F E A T U R E E N G I N E E R I N G :
Before Oversampling After Oversampling
Sometimes when the records of a certain class are much more than the other class, our classifier may get biased towards the
prediction. Thus our traditional approach of classification and model accuracy calculation is not useful in the case of the
imbalanced dataset
32. F E A T U R E E N G I N E E R I N G :
Sometimes when the records of a certain class are much more than the other class, our classifier may get biased towards the
prediction. In this case, the confusion matrix for the classification problem shows how well our model classifies the target
classes and we arrive at the accuracy of the model from the confusion matrix.
33. M O D E L B U I L D I N G :
If we do random sampling to split the dataset into training set and test set. Then we might get a
majority of one of the class in training and minority of other in testing. If we train our model
obviously we will be getting bad evaluation scores.
Stratified sampling is the solution to maintain the ratio of all classes in both training as well as in
testing data
34. M O D E L B U I L D I N G :
The solution for the first problem where we were able to get different accuracy scores for different
random state parameter values is to use K-Fold Cross-Validation. But K-Fold Cross Validation also
suffers from the second problem i.e. random sampling.
The solution for both the first and second problems is to use Stratified K-Fold Cross-Validation.
Stratified k-fold cross-validation is the same as just k-fold cross-validation, But Stratified k-fold cross-
validation, it does stratified sampling instead of random sampling.
35. M O D E L E V A L U A T I O N :
Accuracy on Test Data
Precision on Test Data
Recall Score on Test Data
F1-Score on Test Data
37. Random Forest Classification Model has 100% Accuracy on Test as well on Training Dataset.
0% Error . 100% Recall , Precision and F1-Score. No Overfitting, Underfitting or any Misclassification
M O D E L S E L E C T I O N :