This document provides an overview of the data science process for predicting the NBA MVP winner for the upcoming season. It discusses framing the question, collecting relevant stats data from basketball-reference.com, cleaning and formatting the data, exploring it with Python libraries like Pandas and NumPy, building and evaluating decision tree and random forest models, and discussing ways to improve the model's performance, such as modifying feature selection.
The outcome of the Academy Award for Best Picture surprised us all. But, could that have been predicted? In this practical workshop you'll use a dataset that contains previous Oscar winners to build a prediction model to guess the winner for Best Picture. You'll get an introduction to a data scientist's tools and methods, including an overview of basic machine learning concepts. Unlike this year's Oscars, our model will predict only one winner!
If you are curious what is ML all about, this is a gentle introduction to Machine Learning and Deep Learning. This includes questions such as why ML/Data Analytics/Deep Learning ? Intuitive Understanding o how they work and some models in detail. At last I share some useful resources to get started.
Introduction to machine learning. Basics of machine learning. Overview of machine learning. Linear regression. logistic regression. cost function. Gradient descent. sensitivity, specificity. model selection.
Module 1 introduction to machine learningSara Hooker
We believe in building technical capacity all over the world.
We are building and teaching an accessible introduction to machine learning for students passionate about the power of data to do good.
Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data.
To learn more about our work, visit www.deltanalytics.org
A Beginner's Guide to Machine Learning with Scikit-LearnSarah Guido
Given at the PyData NYC 2013 conference (http://vimeo.com/79517341), and will be given at PyTennessee 2014.
Scikit-learn is one of the most well-known machine learning Python modules in existence. But how does it work, and what, for that matter, is machine learning? For those with programming experience but who are new to machine learning, this talk gives a beginner-level overview of how machine learning can be useful, important machine learning concepts, and how to implement them with scikit-learn. We’ll use real world data to look at supervised and unsupervised machine learning algorithms and why scikit-learn is useful for performing these tasks.
The outcome of the Academy Award for Best Picture surprised us all. But, could that have been predicted? In this practical workshop you'll use a dataset that contains previous Oscar winners to build a prediction model to guess the winner for Best Picture. You'll get an introduction to a data scientist's tools and methods, including an overview of basic machine learning concepts. Unlike this year's Oscars, our model will predict only one winner!
If you are curious what is ML all about, this is a gentle introduction to Machine Learning and Deep Learning. This includes questions such as why ML/Data Analytics/Deep Learning ? Intuitive Understanding o how they work and some models in detail. At last I share some useful resources to get started.
Introduction to machine learning. Basics of machine learning. Overview of machine learning. Linear regression. logistic regression. cost function. Gradient descent. sensitivity, specificity. model selection.
Module 1 introduction to machine learningSara Hooker
We believe in building technical capacity all over the world.
We are building and teaching an accessible introduction to machine learning for students passionate about the power of data to do good.
Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data.
To learn more about our work, visit www.deltanalytics.org
A Beginner's Guide to Machine Learning with Scikit-LearnSarah Guido
Given at the PyData NYC 2013 conference (http://vimeo.com/79517341), and will be given at PyTennessee 2014.
Scikit-learn is one of the most well-known machine learning Python modules in existence. But how does it work, and what, for that matter, is machine learning? For those with programming experience but who are new to machine learning, this talk gives a beginner-level overview of how machine learning can be useful, important machine learning concepts, and how to implement them with scikit-learn. We’ll use real world data to look at supervised and unsupervised machine learning algorithms and why scikit-learn is useful for performing these tasks.
The term Machine Learning was coined by Arthur Samuel in 1959, an american pioneer in the field of computer gaming and artificial intelligence and stated that “ it gives computers the ability to learn without being explicitly programmed” And in 1997, Tom Mitchell gave a “ well-Posed” mathematical and relational definition that “ A Computer Program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E”.
Machine learning is needed for tasks that are too complex for humans to code directly. So instead, we provide a large amount of data to a machine learning algorithm and let the algorithm work it out by exploring that data and searching for a model that will achieve what the programmers have set it out to achieve.
Module 9: Natural Language Processing Part 2Sara Hooker
Delta Analytics is a 501(c)3 non-profit in the Bay Area. We believe that data is powerful, and that anybody should be able to harness it for change. Our teaching fellows partner with schools and organizations worldwide to work with students excited about the power of data to do good.
Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data.
To learn more about our mission or provide feedback, take a look at www.deltanalytics.org. If you would like to use this material to further our mission of improving access to machine learning. Education please reach out to inquiry@deltanalytics.org .
Module 8: Natural language processing Pt 1Sara Hooker
Delta Analytics is a 501(c)3 non-profit in the Bay Area. We believe that data is powerful, and that anybody should be able to harness it for change. Our teaching fellows partner with schools and organizations worldwide to work with students excited about the power of data to do good.
Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data.
To learn more about our mission or provide feedback, take a look at www.deltanalytics.org. If you would like to use this material to further our mission of improving access to machine learning. Education please reach out to inquiry@deltanalytics.org .
Data Science Job ready #DataScienceInterview Question and Answers 2022 | #Dat...Rohit Dubey
How Much Do Data Scientists Make?
The demand and salary for data scientists tend to be higher than most other ITES jobs. Experience is one of the key factors in determining the salary range of a data science professional.
According to Glassdoor, a Data Scientist in the United States earns an annual average of USD 117,212, and the same site reports that Data Scientists in India make a yearly average of ₹1,000,000.
Data Scientist Career Path
Data Science is currently considered one of the most lucrative careers available. Companies across all major industries/sectors have data scientist requirements to help them gain valuable insights from big data. There is a sharp growth in demand for highly skilled data science professionals who can straddle the business and IT worlds.
The career path to becoming a data scientist isn’t clearly defined since this is a relatively new profession. People from different backgrounds like mathematics, statistics, computer science or economics, end up in data science.
The major designations for data science professionals are:
Data Analyst
Data Scientist (entry-level)
Associate data scientist
Data Scientist (senior-level)
Product Manager
Lead data scientist
Director/VP/SVP
That was all about Data Scientist Job Description.
Become a Data Scientist Today!
In this write-up, we covered the Data Scientist job description in detail. Irrespective of which location you are in, there is no dearth of jobs for skillful data scientists. A career in data science is a rewarding journey to embark on, especially in the finance, retail, and e-commerce sectors. Jobs are also available with Government departments, universities and research institutes, telecoms, transports, the list goes on.
This video covers
Introductory Questions
Data Science Introduction
Data Science Technical Interview QnA :
#Excel
#SQL
#Python3
#MachineLearning
#DataAnalyticstechnical Interview
#DataScienceProjects
#coder #statistics #datamining #dataanalyst #code #engineering #linux #codinglife #cloudcomputing #businessintelligence #robotics #softwaredeveloper #automation #cloud #neuralnetworks #sql #science #softwareengineer #digitaltransformation #computer #daysofcode #coders #bigdataanalytics #programminglife #dataviz #html #digitalmarketing #devops #datasciencetraining #dataprotection
#rohitdubey
#teachtechtoe
#datascience #datasciencetraining #datasciencejobs #datasciencecourse #datasciencenigeria #datasciencebootcamp #datascienceworkshop #datasciencecareers #datasciencestudent #datascienceproject #datascienceforall #datasciencetraininginpatelnagar#datasciencetrainingindelhi
How to Use Artificial Intelligence by Microsoft Product ManagerProduct School
The talk focused on the Fundamentals of Product Management, leveraging the speaker's personal experiences in the AI field. It covered core Product Manager topics such as managing customer needs, business goals & technology feasibility, the holy trinity of the Product Manager discipline, delve into data analyses, rapid experimentation, and execution, and finally, explored the challenges of customer privacy, bias, and inclusivity in AI products.
Slides covered during Analytics Boot Camp conducted with the help of IBM, Venturesity. Special credits to Kumar Rishabh (Google) and Srinivas Nv Gannavarapu (IBM)
Machine Learning Foundations for Professional ManagersAlbert Y. C. Chen
20180804@Taiwan AI Academy, Hsinchu
6 hour lecture for those new to machine learning, to grasps the concepts, advantages and limitations of various classical machine learning methods. More importantly, to learn the skills to break down large complicated AI projects into manageable pieces, where features and functionalities could be added incrementally and annotated data accumulated. Take home message: machine learning is always a delicate balance between model complexity M and number of data N so that the trained classifier generalizes well and does not overfit.
Sample Codes: https://github.com/davegautam/dotnetconfsamplecodes
Presentation on How you can get started with ML.NET. If you are existing .NET Stack Developer and Wanna use the same technology into Machine Learning, this slide focuses on how you can use ML.NET for Machine Learning.
Enhancing Enterprise Search with Machine Learning - Simon Hughes, Dice.comSimon Hughes
In the talk I describe two approaches for improve the recall and precision of an enterprise search engine using machine learning techniques. The main focus is improving relevancy with ML while using your existing search stack, be that Luce, Solr, Elastic Search, Endeca or something else.
Using Net Promoter Score (NPS) to Increase Course EngagementLambda Solutions
A core activity of measuring how Learners engage with your course is measuring their reaction to it. A popular technique to measure customer experience is Net Promoter Score (NPS). Most organizations struggle to effectively structure an NPS survey, which overwhelms or makes it extraordinarily hard to use the data to make improvements.
In this webinar, we explore best practices in creating NPS surveys, analyzing the data, and applying lean learning analytics techniques to use the feedback to continuously improve your courses.
Tune in!
YouTube: https://youtu.be/lkga98m0few
( ** Data Analyst Master's Program: https://www.edureka.co/masters-program/data-analyst-certification ** )
This PPT will provide you with a crisp description of the Job Description of a Data Analyst's Job, the skills required to become one, Resume requirements and the salary trends of a fresher as well as an experienced Data Analyst.
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
The term Machine Learning was coined by Arthur Samuel in 1959, an american pioneer in the field of computer gaming and artificial intelligence and stated that “ it gives computers the ability to learn without being explicitly programmed” And in 1997, Tom Mitchell gave a “ well-Posed” mathematical and relational definition that “ A Computer Program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E”.
Machine learning is needed for tasks that are too complex for humans to code directly. So instead, we provide a large amount of data to a machine learning algorithm and let the algorithm work it out by exploring that data and searching for a model that will achieve what the programmers have set it out to achieve.
Module 9: Natural Language Processing Part 2Sara Hooker
Delta Analytics is a 501(c)3 non-profit in the Bay Area. We believe that data is powerful, and that anybody should be able to harness it for change. Our teaching fellows partner with schools and organizations worldwide to work with students excited about the power of data to do good.
Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data.
To learn more about our mission or provide feedback, take a look at www.deltanalytics.org. If you would like to use this material to further our mission of improving access to machine learning. Education please reach out to inquiry@deltanalytics.org .
Module 8: Natural language processing Pt 1Sara Hooker
Delta Analytics is a 501(c)3 non-profit in the Bay Area. We believe that data is powerful, and that anybody should be able to harness it for change. Our teaching fellows partner with schools and organizations worldwide to work with students excited about the power of data to do good.
Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data.
To learn more about our mission or provide feedback, take a look at www.deltanalytics.org. If you would like to use this material to further our mission of improving access to machine learning. Education please reach out to inquiry@deltanalytics.org .
Data Science Job ready #DataScienceInterview Question and Answers 2022 | #Dat...Rohit Dubey
How Much Do Data Scientists Make?
The demand and salary for data scientists tend to be higher than most other ITES jobs. Experience is one of the key factors in determining the salary range of a data science professional.
According to Glassdoor, a Data Scientist in the United States earns an annual average of USD 117,212, and the same site reports that Data Scientists in India make a yearly average of ₹1,000,000.
Data Scientist Career Path
Data Science is currently considered one of the most lucrative careers available. Companies across all major industries/sectors have data scientist requirements to help them gain valuable insights from big data. There is a sharp growth in demand for highly skilled data science professionals who can straddle the business and IT worlds.
The career path to becoming a data scientist isn’t clearly defined since this is a relatively new profession. People from different backgrounds like mathematics, statistics, computer science or economics, end up in data science.
The major designations for data science professionals are:
Data Analyst
Data Scientist (entry-level)
Associate data scientist
Data Scientist (senior-level)
Product Manager
Lead data scientist
Director/VP/SVP
That was all about Data Scientist Job Description.
Become a Data Scientist Today!
In this write-up, we covered the Data Scientist job description in detail. Irrespective of which location you are in, there is no dearth of jobs for skillful data scientists. A career in data science is a rewarding journey to embark on, especially in the finance, retail, and e-commerce sectors. Jobs are also available with Government departments, universities and research institutes, telecoms, transports, the list goes on.
This video covers
Introductory Questions
Data Science Introduction
Data Science Technical Interview QnA :
#Excel
#SQL
#Python3
#MachineLearning
#DataAnalyticstechnical Interview
#DataScienceProjects
#coder #statistics #datamining #dataanalyst #code #engineering #linux #codinglife #cloudcomputing #businessintelligence #robotics #softwaredeveloper #automation #cloud #neuralnetworks #sql #science #softwareengineer #digitaltransformation #computer #daysofcode #coders #bigdataanalytics #programminglife #dataviz #html #digitalmarketing #devops #datasciencetraining #dataprotection
#rohitdubey
#teachtechtoe
#datascience #datasciencetraining #datasciencejobs #datasciencecourse #datasciencenigeria #datasciencebootcamp #datascienceworkshop #datasciencecareers #datasciencestudent #datascienceproject #datascienceforall #datasciencetraininginpatelnagar#datasciencetrainingindelhi
How to Use Artificial Intelligence by Microsoft Product ManagerProduct School
The talk focused on the Fundamentals of Product Management, leveraging the speaker's personal experiences in the AI field. It covered core Product Manager topics such as managing customer needs, business goals & technology feasibility, the holy trinity of the Product Manager discipline, delve into data analyses, rapid experimentation, and execution, and finally, explored the challenges of customer privacy, bias, and inclusivity in AI products.
Slides covered during Analytics Boot Camp conducted with the help of IBM, Venturesity. Special credits to Kumar Rishabh (Google) and Srinivas Nv Gannavarapu (IBM)
Machine Learning Foundations for Professional ManagersAlbert Y. C. Chen
20180804@Taiwan AI Academy, Hsinchu
6 hour lecture for those new to machine learning, to grasps the concepts, advantages and limitations of various classical machine learning methods. More importantly, to learn the skills to break down large complicated AI projects into manageable pieces, where features and functionalities could be added incrementally and annotated data accumulated. Take home message: machine learning is always a delicate balance between model complexity M and number of data N so that the trained classifier generalizes well and does not overfit.
Sample Codes: https://github.com/davegautam/dotnetconfsamplecodes
Presentation on How you can get started with ML.NET. If you are existing .NET Stack Developer and Wanna use the same technology into Machine Learning, this slide focuses on how you can use ML.NET for Machine Learning.
Enhancing Enterprise Search with Machine Learning - Simon Hughes, Dice.comSimon Hughes
In the talk I describe two approaches for improve the recall and precision of an enterprise search engine using machine learning techniques. The main focus is improving relevancy with ML while using your existing search stack, be that Luce, Solr, Elastic Search, Endeca or something else.
Using Net Promoter Score (NPS) to Increase Course EngagementLambda Solutions
A core activity of measuring how Learners engage with your course is measuring their reaction to it. A popular technique to measure customer experience is Net Promoter Score (NPS). Most organizations struggle to effectively structure an NPS survey, which overwhelms or makes it extraordinarily hard to use the data to make improvements.
In this webinar, we explore best practices in creating NPS surveys, analyzing the data, and applying lean learning analytics techniques to use the feedback to continuously improve your courses.
Tune in!
YouTube: https://youtu.be/lkga98m0few
( ** Data Analyst Master's Program: https://www.edureka.co/masters-program/data-analyst-certification ** )
This PPT will provide you with a crisp description of the Job Description of a Data Analyst's Job, the skills required to become one, Resume requirements and the salary trends of a fresher as well as an experienced Data Analyst.
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
From Data to Artificial Intelligence with the Machine Learning Canvas — ODSC ...Louis Dorard
The creation and deployment of predictive models that are at the core of artificially intelligent systems, is now being largely automated. However, formalizing the right machine learning problem that will leverage data to make applications and products more intelligent — and to create value — remains a challenge.
The Machine Learning Canvas is used by teams of managers, scientists and engineers to align their activities by providing a visual framework that helps specify the key aspects of AI systems: value proposition, data to learn from, usage of predictions, constraints, and measures of performance. In this presentation, we’ll motivate the usage of the MLC, we'll explain its structure, how to fill it in, and we’ll go over some example applications.
This presentation discusses decision trees as a machine learning technique. This introduces the problem with several examples: cricket player selection, medical C-Section diagnosis and Mobile Phone price predictor. It discusses the ID3 algorithm and discusses how the decision tree is induced. The definition and use of the concepts such as Entropy, Information Gain are discussed.
Euro Cup fans worldwide can book Euro 2024 Tickets from our online platform www.worldwideticketsandhospitality. Fans can book Croatia vs Italy Tickets on our website at discounted prices.
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Euro Cup fans worldwide can book Euro 2024 Tickets from our online platform www.worldwideticketsandhospitality. Fans can book Croatia Vs Italy Tickets on our website at discounted prices.
Narrated Business Proposal for the Philadelphia Eaglescamrynascott12
Slide 1:
Welcome, and thank you for joining me today. We will explore a strategic proposal to enhance parking and traffic management at Lincoln Financial Field, aiming to improve the overall fan experience and operational efficiency. This comprehensive plan addresses existing challenges and leverages innovative solutions to create a smoother and more enjoyable experience for our fans.
Slide 2:
Picture this: It’s a crisp fall afternoon, driving towards Lincoln Financial Field. The atmosphere is electric—tailgaters grilling, fans in Eagles jerseys creating a sea of green and white. The air buzzes with camaraderie and anticipation. You park, join the throng, and make your way to your seat. The stadium roars as the Eagles take the field, sending chills down your spine. Each play is a thrilling dance of strategy and skill. This is what being an Eagles fan is all about—the joy, the pride, and the shared experience.
Slide 3:
But now, the day is marred by frustration. The excitement wanes as you struggle to find a parking spot. The congestion is overwhelming, and tempers flare. The delays mean you miss the pre-game excitement, the tailgate camaraderie, and even the opening kick-off. After the game, the joy of victory or the shared solace of defeat is overshadowed by the stress of navigating out of the parking lot. The gridlock, honking horns, and endless waiting drain the energy and joy from what should have been an unforgettable experience.
Our proposal aims to eliminate these frustrations, ensuring that from arrival to departure, your experience is extraordinary. Efficient parking and smooth traffic flow are key to maintaining the high spirits and excitement that make game days special.
Slide 4:
The Philadelphia Eagles are not just a premier NFL team; they are an integral part of the community, hosting games, concerts, and various events at Lincoln Financial Field. Our state-of-the-art stadium is designed to provide a world-class experience for every attendee. Whether it's the thrill of game day, the excitement of a live concert, or the camaraderie of community events, we pride ourselves on delivering a fan-first experience and maintaining operational excellence across all our activities. Our commitment to our fans and community is unwavering, and we continuously strive to enhance every aspect of their experience, ensuring they leave with unforgettable memories.
Slide 5:
Recent trends show an increasing demand for efficient event logistics. Our customer feedback has consistently highlighted frustrations with parking and traffic. Surveys indicate that a significant number of fans are dissatisfied with the current parking situation. Comparisons with other venues like Citizens Bank Park and Wells Fargo Center reveal that we lag in terms of parking efficiency and convenience. These insights underscore the urgent need for innovation to meet and exceed fan expectations.
Slide 6:
As we delve into the intricacies of our operations, one glaring issue emer
Turkey's Euro 2024 Squad Overview and Transfer Speculation.docxEuro Cup 2024 Tickets
Vincenzo Montella has announced a preliminary 35-man squad for Turkey ahead of the UEFA Euro 2024, which includes three Serie A players, Hakan Calhanoglu, Kenan Yildiz, and Zeki Celik
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Portugal Vs Czechia- Ronaldo feels 'proud' of new UEFA Euro 2024 record.docx
Predicting the NBA MVP
1. Predicting the NBA MVP with Data Science
bit.ly/nba-la
CrossCamp.us Events
2. About us
We train developers and data
scientists through 1-on-1
mentorship and career prep
3. About me
• Alex Nussbacher
• Lead Data Science Instructor at Thinkful
• Data scientist at Uber, focus on consumption
economics and economics of choice 🤔
4. What’s your background?
• I have a software background
• I have a math or stats background
• None of the above
5. Data Science Process
• Frame the question.
• Collect the raw data.
• Process the data.
• Explore the data.
• Communicate results.
7. Collect the Data
• What kind of data do we need?
• Individual stats
• Team stats and success
• Past winners and voting records
• All data from basketball-reference.com
8. Process the data
• How’s the data “dirty” and how can we fix it?
• User input, redundancies, missing data…
• Formatting: adapt the data to meet certain
specifications.
• Cleaning: detecting and correcting corrupt
or inaccurate records.
9. Explore the data
• What are the meaningful patterns in the
data?
• How meaningful is each data point for our
predictions?
10. Goals
• Introduction to a data scientist's tools and
methods:
• Jupyter notebooks, numpy, pandas,
sklearn…
• Overview of basic machine learning concepts:
• Data formatting and cleaning, Decision
trees, Overfitting, Random Forests…
11. Jupyter Notebooks
• One of data scientist’s everyday tools.
• Find the links in our classroom tool.
• Contains cells with code.
12. NumPy
• The fundamental package for scientific
computing with Python.
• Provides powerful multi-dimensional array
objects.
• Many methods for fast operations on arrays.
13. Pandas
• Fundamental high-level building block for
doing practical, real world data analysis in
Python.
• Built on top of NumPy.
• Offers data structures and operations for
manipulating numerical tables and time
series.
14. Scikit-learn
• Python module for machine learning.
• Provides a large menu of libraries for
scientific computation, such as integration,
interpolation, signal processing, linear
algebra, statistics, etc.
16. Understanding your data
• .head(n) method: Returns first n rows.
• .value_counts() method: Returns the counts
of unique values in the DataFrame.
17. Training Set
• We loaded in our data as a training set.
• This is because we’re going to use this data
to build, or train, our model
• It consists of every year for which we have
data on NBA MVP voting, from the 1955-56
season onward
19. Formatting your Data
• We need to put our data in the easiest to use
format
• No blanks allowed
• Numeric strings (like win loss record) need to
have the numbers extracted and typed as
integers
• Factors, or categories, need to be changed to
dummies, which report a 0 or 1 to show if that
value is present
20. Decision Trees
• It breaks down a dataset into smaller and
smaller subsets.
• The final result is a model with a tree
structure that has:
• Decision nodes: ask a question and have
two or more branches.
• Leaf nodes: represent a classification or
decision.
21.
22. Classification vs Regression
• Classification — Predict categories.
• Identifying group membership.
• Regression — Predict values.
• Involves estimating or predicting a
response.
25. Regression
• Regression — Predict values.
• Involves estimating or predicting a
response.
• This is what we’ll be doing. Predicting
vote share…
26. Creating your first Decision Tree
You will use the scikit-learn and numpy libraries
to build your first decision tree. We will need the
following to build a decision tree
• Response (y): A one-dimensional array or
series containing the target from the train
data.
• Inputs (X): A multidimensional pandas data
frame containing the features/predictors from
the train data.
28. Importances and Score
• .feature_importances_ attribute: tells us
how important the features are for the final
result.
• .score() method: returns the mean accuracy
of our fitting.
31. CLASS IMBALANCE
• We have what is called a class imbalance
problem.
• The outcome of not being MVP is much much
more common than being the MVP,
• So our model is ‘accurate’ if it just tells
everyone they’re not going to be MVP
33. Looking at our results
• We seem to be doing a decent job of
identifying players who are great players
• But the ordering isn’t perfect
• And we have a lot of people who are scored
as equivalent
• Also note this seems to be a year with a lot of
great performers this year
34. Let’s improve it!
• We have options for improving the model
• Firstly, we can look at our feature list and
select a smaller but more effective list of
features
• We could also choose a better type of
model…
36. Modify the feature list
• We put a lot of features into our model
• Trimming it down to a smaller list could
improve the efficiency of our trees and
possibly improve accuracy as well
37. Overfitting
• Resulting model too tied to the training set.
• It doesn’t generalize to new data, which is the
point of prediction.
38. Random Forest Classifier
• Random Forest Classifiers use many
Decision Trees to build a classifier.
• We introduce a bit of randomness.
• Each Tree can uses a subset of the data to
give a different answer (a vote). The final
classification is the most common amongst
the Trees.
44. What’s going on?
• Our model is giving good weight to major
statistical categories and position, but not
enough to team record…
• How could you fix continue to improve???
50. More about Thinkful
• Anyone who’s committed can learn to code
• 1-on-1 mentorship is the best way to learn
• Flexibility! Learn anywhere, anytime, & at your
own pace
51. Our Program
You’ll learn concepts, practice with drills, and build
capstone projects — all guided by a personal mentor
53. Data Science Syllabus
• Managing data with SQL and Python
• Modeling with both supervised and unsupervised
models
• Data visualization and communicating with data
• Technical interviews + career services
54. Special Introductory Offer
• Prep course for 50% off —
$250 instead of $500
• Covers math, stats,
Python, and data science
toolkit
• Option to continue into full
program
• Talk to me (or email
noel@thinkful.com) if
you’re interested
Editor's Notes
80-20 rule: that 80% of a typical data science project is sourcing cleaning and preparing the data, while the remaining 20% is actual data analysis. Surprisingly time-consuming task. What we’re seeing now is increased number of data analysts who work on cleaning data to free up data scientist time.
Let's start with loading in the training and testing set into your Python environment.
You will use the training set to build your model, and the test set to validate it.
The data is stored as csv files. You can load this data with the read_csv() method from the Pandas library.
Before starting with the actual analysis, it's important to understand the structure of your data.
the decision tree algorithm starts with all the data at the root node and scans all the variables for the best one to split on. Once a variable is chosen, you do the split and go down one level (or one node) and repeat.
Famous example is Iris data set. Flowers have four features, sepal length and width, petal width and length
If we plot it out across two dimensions, we can see that the setosa is in red, versicolor in green and virginia in blue. Imagine each of these dots represent a training point, something I’ve told a computer about. Then I show that computer the gray dot and ask what it is. What should the computer predict?
Imaging this same concept is taking place in three dimensions. Or more! The more data we have, the better we can teach the computer how to do various things.
In January, 2016 Thinkful became the first online bootcamp to publish a jobs report. And now we’re the first one to use a 3rd-party auditor to ensure our data is accurate and our methods are applied as advertised.
We’ve seen 92% of our graduates land jobs as developers within 4 months of graduation.
Our students generally move into full-time, salaried positions as developers or engineers. They work at startups and also larger, more established companies in several industries.
We published the report because we want to give students the tools they need to make an informed decision about the programming school they attend.
Education requires trust, and transparency builds it. Until now students choosing a bootcamp must take a leap of faith that schools are honest, their numbers up to date, and the results accurate. That's not sustainable and we hope it stops.
We want to make sure our students have the tools they need to make an informed decision on which programming school they attend.
Feel free to take a look on our website if you’d like to see all the data and the audit report.
1. Job placement stats. Audited stats. We are the only bootcamp that publishes monthly job stats. One of only bootcamps in the nation that has these stats verified by a third party.
2. 32% of flexible bootcamps. Whenever a student withdraws. Overall the most common reason is there are changes in schedule or financial ability changes. We try to address first one. Over 60% are full-time. Outside of our control. Full-time is 85% grad rate. In Atlanta, we’ve yet to have someone drop out.
