The document discusses feature extraction and selection as important steps in machine learning. It notes that better features often lead to better algorithms. It then describes five clusters identified through clustering analysis. Each cluster contains individuals (male or female) with certain average demographic characteristics like age, location, income, and whether they have accounts or loans. The document emphasizes that feature extraction and selection are underrated but important for machine learning.
The document discusses clustering and nearest neighbor algorithms for deriving knowledge from data at scale. It provides an overview of clustering techniques like k-means clustering and discusses how they are used for applications such as recommendation systems. It also discusses challenges like class imbalance that can arise when applying these techniques to large, real-world datasets and evaluates different methods for addressing class imbalance. Additionally, it discusses performance metrics like precision, recall, and lift that can be used to evaluate models on large datasets.
The document discusses a lecture on deriving knowledge from data at scale. It outlines topics that will be covered, including forecasting techniques, introducing the Weka data mining tool, decision trees, and doing hands-on exercises with decision trees in Weka. The lecture objectives are also listed, which are to gain familiarity with Weka, understand decision trees, and get experience applying decision trees in Weka if time permits.
The document discusses machine learning and data science concepts. It begins with an introduction to machine learning and the machine learning process. It then provides an overview of select machine learning algorithms and concepts like bias/variance, generalization, underfitting and overfitting. It also discusses ensemble methods. The document then shifts to discussing time series, functions for manipulating time series, and laying the foundation for time series prediction and forecasting. It provides examples of applying techniques like median filtering to smooth time series data. Overall, the document provides a high-level introduction and overview of key machine learning and time series concepts.
Roger S. Barga discusses his experience in data science and predictive analytics projects across multiple industries. He provides examples of predictive models built for customer segmentation, predictive maintenance, customer targeting, and network intrusion prevention. Barga also outlines a sample predictive analytics project for a real estate client to predict whether they can charge above or below market rates. The presentation emphasizes best practices for building predictive models such as starting small, leveraging third-party tools, and focusing on proxy metrics that drive business outcomes.
The document discusses an agenda for a lecture on deriving knowledge from data at scale. The lecture will include a course project check-in, a thought exercise on data transformation, and a deeper dive into ensembling techniques. It also provides tips on gaining experience and intuition for data science, including becoming proficient in tools, deeply understanding algorithms, and focusing on specific data types through hands-on practice of experiments. Attribute selection techniques like filters, wrappers and embedded methods are also covered. Finally, the document discusses support vector machines and handling missing values in data.
The document discusses various machine learning concepts like model overfitting, underfitting, missing values, stratification, feature selection, and incremental model building. It also discusses techniques for dealing with overfitting and underfitting like adding regularization. Feature engineering techniques like feature selection and creation are important preprocessing steps. Evaluation metrics like precision, recall, F1 score and NDCG are discussed for classification and ranking problems. The document emphasizes the importance of feature engineering and proper model evaluation.
This document outlines an agenda for a data science boot camp covering various machine learning topics over several hours. The agenda includes discussions of decision trees, ensembles, random forests, data modelling, and clustering. It also provides examples of data leakage problems and discusses the importance of evaluating model performance. Homework assignments involve building models with Weka and identifying the minimum attributes needed to distinguish between red and white wines.
This document discusses various techniques for machine learning when labeled training data is limited, including semi-supervised learning approaches that make use of unlabeled data. It describes assumptions like the clustering assumption, low density assumption, and manifold assumption that allow algorithms to learn from unlabeled data. Specific techniques covered include clustering algorithms, mixture models, self-training, and semi-supervised support vector machines.
The document discusses clustering and nearest neighbor algorithms for deriving knowledge from data at scale. It provides an overview of clustering techniques like k-means clustering and discusses how they are used for applications such as recommendation systems. It also discusses challenges like class imbalance that can arise when applying these techniques to large, real-world datasets and evaluates different methods for addressing class imbalance. Additionally, it discusses performance metrics like precision, recall, and lift that can be used to evaluate models on large datasets.
The document discusses a lecture on deriving knowledge from data at scale. It outlines topics that will be covered, including forecasting techniques, introducing the Weka data mining tool, decision trees, and doing hands-on exercises with decision trees in Weka. The lecture objectives are also listed, which are to gain familiarity with Weka, understand decision trees, and get experience applying decision trees in Weka if time permits.
The document discusses machine learning and data science concepts. It begins with an introduction to machine learning and the machine learning process. It then provides an overview of select machine learning algorithms and concepts like bias/variance, generalization, underfitting and overfitting. It also discusses ensemble methods. The document then shifts to discussing time series, functions for manipulating time series, and laying the foundation for time series prediction and forecasting. It provides examples of applying techniques like median filtering to smooth time series data. Overall, the document provides a high-level introduction and overview of key machine learning and time series concepts.
Roger S. Barga discusses his experience in data science and predictive analytics projects across multiple industries. He provides examples of predictive models built for customer segmentation, predictive maintenance, customer targeting, and network intrusion prevention. Barga also outlines a sample predictive analytics project for a real estate client to predict whether they can charge above or below market rates. The presentation emphasizes best practices for building predictive models such as starting small, leveraging third-party tools, and focusing on proxy metrics that drive business outcomes.
The document discusses an agenda for a lecture on deriving knowledge from data at scale. The lecture will include a course project check-in, a thought exercise on data transformation, and a deeper dive into ensembling techniques. It also provides tips on gaining experience and intuition for data science, including becoming proficient in tools, deeply understanding algorithms, and focusing on specific data types through hands-on practice of experiments. Attribute selection techniques like filters, wrappers and embedded methods are also covered. Finally, the document discusses support vector machines and handling missing values in data.
The document discusses various machine learning concepts like model overfitting, underfitting, missing values, stratification, feature selection, and incremental model building. It also discusses techniques for dealing with overfitting and underfitting like adding regularization. Feature engineering techniques like feature selection and creation are important preprocessing steps. Evaluation metrics like precision, recall, F1 score and NDCG are discussed for classification and ranking problems. The document emphasizes the importance of feature engineering and proper model evaluation.
This document outlines an agenda for a data science boot camp covering various machine learning topics over several hours. The agenda includes discussions of decision trees, ensembles, random forests, data modelling, and clustering. It also provides examples of data leakage problems and discusses the importance of evaluating model performance. Homework assignments involve building models with Weka and identifying the minimum attributes needed to distinguish between red and white wines.
This document discusses various techniques for machine learning when labeled training data is limited, including semi-supervised learning approaches that make use of unlabeled data. It describes assumptions like the clustering assumption, low density assumption, and manifold assumption that allow algorithms to learn from unlabeled data. Specific techniques covered include clustering algorithms, mixture models, self-training, and semi-supervised support vector machines.
The document discusses various topics related to deriving knowledge from data at scale. It begins with definitions of a data scientist from different sources, noting that data scientists obtain, explore, model and interpret data using hacking, statistics and machine learning. It also discusses challenges of having enough data scientists. Other topics discussed include important ideas for data science like interdisciplinary work, algorithms, coding practices, data strategy, causation vs. correlation, and feedback loops. Building predictive models is also discussed with steps like defining objectives, accessing and understanding data, preprocessing, and evaluating models.
This document discusses the unrealized power of data and predictive analytics. It begins by highlighting how predictive analytics can be used for forecasting, targeting customers, fraud detection, risk assessment, customer churn prediction, and price elasticity analysis. It then provides examples of predictive analytics in action in various industries like healthcare, education, law enforcement, and human resources. The document emphasizes that predictive analytics must become simpler to use and be integrated into business processes. It outlines the data science process and importance of data wrangling. Finally, it discusses Microsoft's CloudML Studio and Data Lab products for building predictive models using machine learning algorithms and analyzing customer data to predict things like equipment failures and customer churn.
This document discusses streaming data processing and the adoption of scalable frameworks and platforms for handling streaming or near real-time analysis and processing over the next few years. These platforms will be driven by the needs of large-scale location-aware mobile, social and sensor applications, similar to how Hadoop emerged from large-scale web applications. The document also references forecasts of over 50 billion intelligent devices by 2015 and 275 exabytes of data per day being sent across the internet by 2020, indicating challenges around data of extreme size and the need for rapid processing.
This document discusses the past, present, and future of machine learning. It outlines how machine learning has evolved from early attempts at neural networks and expert systems to today's deep learning techniques powered by large datasets and distributed computing. The document argues that machine learning and predictive analytics will be core capabilities that impact many industries and applications going forward, including personalized insurance, fraud detection, equipment monitoring, and more. Intelligence from machine learning will become "ambient" and help solve hard problems by extracting value from big data.
This document appears to be lecture slides for a course on deriving knowledge from data at scale. It covers many topics related to building machine learning models including data preparation, feature selection, classification algorithms like decision trees and support vector machines, and model evaluation. It provides examples applying these techniques to a Titanic passenger dataset to predict survival. It emphasizes the importance of data wrangling and discusses various feature selection methods.
Machine Learning has become a must to improve insight, quality and time to market. But it's also been called the 'high interest credit card of technical debt' with challenges in managing both how it's applied and how its results are consumed.
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.
Heuristic design of experiments w meta gradient searchGreg Makowski
Once you have started learning about predictive algorithms, and the basic knowledge discovery in databases process, what is the next level of detail to learn for a consulting project?
* Give examples of the many model training parameters
* Track results in a "model notebook"
* Use a model metric that combines both accuracy and generalization to rank models
* How to strategically search over the model training parameters - use a gradient descent approach
* One way to describe an arbitrarily complex predictive system is by using sensitivity analysis
H2O World - Top 10 Data Science Pitfalls - Mark LandrySri Ambati
H2O World 2015 - Mark Landry
Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
This document provides an overview of machine learning techniques that can be applied in finance, including exploratory data analysis, clustering, classification, and regression methods. It discusses statistical learning approaches like data mining and modeling. For clustering, it describes techniques like k-means clustering, hierarchical clustering, Gaussian mixture models, and self-organizing maps. For classification, it mentions discriminant analysis, decision trees, neural networks, and support vector machines. It also provides summaries of regression, ensemble methods, and working with big data and distributed learning.
Two hour lecture I gave at the Jyväskylä Summer School. The purpose of the talk is to give a quick non-technical overview of concepts and methodologies in data science. Topics include a wide overview of both pattern mining and machine learning.
See also Part 2 of the lecture: Industrial Data Science. You can find it in my profile (click the face)
Data Science Training | Data Science For Beginners | Data Science With Python...Simplilearn
This Data Science presentation will help you understand what is Data Science, who is a Data Scientist, what does a Data Scientist do and also how Python is used for Data Science. Data science is an interdisciplinary field of scientific methods, processes, algorithms and systems to extract knowledge or insights from data in various forms, either structured or unstructured, similar to data mining. This Data Science tutorial will help you establish your skills at analytical techniques using Python. With this Data Science video, you’ll learn the essential concepts of Data Science with Python programming and also understand how data acquisition, data preparation, data mining, model building & testing, data visualization is done. This Data Science tutorial is ideal for beginners who aspire to become a Data Scientist.
This Data Science presentation will cover the following topics:
1. What is Data Science?
2. Who is a Data Scientist?
3. What does a Data Scientist do?
This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you’ll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jumpstart your career with this interactive, hands-on course.
Why learn Data Science?
Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. A data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.
You can gain in-depth knowledge of Data Science by taking our Data Science with python certification training course. With Simplilearn’s Data Science certification training course, you will prepare for a career as a Data Scientist as you master all the concepts and techniques. Those who complete the course will be able to:
1. Gain an in-depth understanding of data science processes, data wrangling, data exploration, data visualization, hypothesis building, and testing. You will also learn the basics of statistics.
Install the required Python environment and other auxiliary tools and libraries
2. Understand the essential concepts of Python programming such as data types, tuples, lists, dicts, basic operators and functions
3. Perform high-level mathematical computing using the NumPy package and its largelibrary of mathematical functions.
Learn more at: https://www.simplilearn.com
Machine Learning and Real-World ApplicationsMachinePulse
This presentation was created by Ajay, Machine Learning Scientist at MachinePulse, to present at a Meetup on Jan. 30, 2015. These slides provide an overview of widely used machine learning algorithms. The slides conclude with examples of real world applications.
Ajay Ramaseshan, is a Machine Learning Scientist at MachinePulse. He holds a Bachelors degree in Computer Science from NITK, Suratkhal and a Master in Machine Learning and Data Mining from Aalto University School of Science, Finland. He has extensive experience in the machine learning domain and has dealt with various real world problems.
From Raw Data to Deployed Product. Fast & Agile with CRISP-DMMichał Łopuszyński
The document summarizes the Cross Industry Standard Process for Data Mining (CRISP-DM), which is the most popular methodology for data-centric projects. It walks through each step of the CRISP-DM process, including business understanding, data understanding, data preparation, modelling, evaluation, and deployment. For each step, it provides examples and highlights important dos and don'ts, such as thoroughly understanding the problem and data quality before modelling, automating repetitive data preparation tasks, and guarding against overfitting and data leakage during evaluation. The overall document serves as a guide to successfully applying the CRISP-DM process from raw data to deployed product.
The document provides guidance on building an end-to-end machine learning project to predict California housing prices using census data. It discusses getting real data from open data repositories, framing the problem as a supervised regression task, preparing the data through cleaning, feature engineering, and scaling, selecting and training models, and evaluating on a held-out test set. The project emphasizes best practices like setting aside test data, exploring the data for insights, using pipelines for preprocessing, and techniques like grid search, randomized search, and ensembles to fine-tune models.
This document summarizes Michał Łopuszyński's presentation on using an agile approach based on the CRISP-DM methodology for data mining projects. It discusses the key phases of CRISP-DM including business understanding, data understanding, data preparation, modelling, evaluation, and deployment. For each phase, it provides examples of best practices and challenges, with an emphasis on spending sufficient time on data understanding and preparation, developing models with the deployment context in mind, and carefully evaluating results against business objectives.
Random Forest Tutorial | Random Forest in R | Machine Learning | Data Science...Edureka!
This Edureka Random Forest tutorial will help you understand all the basics of Random Forest machine learning algorithm. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts, learn random forest analysis along with examples. Below are the topics covered in this tutorial:
1) Introduction to Classification
2) Why Random Forest?
3) What is Random Forest?
4) Random Forest Use Cases
5) How Random Forest Works?
6) Demo in R: Diabetes Prevention Use Case
You can also take a complete structured training, check out the details here: https://goo.gl/AfxwBc
Slides from my presentation at the Data Intelligence conference in Washington DC (6/23/2017). See this link for the abstract: http://www.data-intelligence.ai/presentations/36
A Practical-ish Introduction to Data ScienceMark West
In this talk I will share insights and knowledge that I have gained from building up a Data Science department from scratch. This talk will be split into three sections:
1. I'll begin by defining what Data Science is, how it is related to Machine Learning and share some tips for introducing Data Science to your organisation.
2. Next up well run through some commonly used Machine Learning algorithms used by Data Scientists, along with examples for use cases where these algorithms can be applied.
3. The final third of the talk will be a demonstration of how you can quickly get started with Data Science and Machine Learning using Python and the Open Source scikit-learn Library.
This document provides an overview of becoming a data scientist. It defines a data scientist and lists common job titles. It discusses the functions of a data scientist like devising business strategies, descriptive/predictive analytics, and data mining. Examples are provided of customer churn analysis and market basket analysis. The skills, aptitudes, and educational paths to become a data scientist are also outlined.
This document contains a resume for Kajul Verma, an IT professional with 4 years of experience as a Product Implementation Engineer. They have a Bachelor's degree in Information Technology and expertise in technologies like Java, JavaScript, HTML5, CSS3, AngularJS, Linux, Windows, Apache Tomcat. They are seeking new opportunities and their experience includes managing ERP projects, designing marketing campaigns, troubleshooting code issues, and training clients on web technologies.
The document discusses various topics related to deriving knowledge from data at scale. It begins with definitions of a data scientist from different sources, noting that data scientists obtain, explore, model and interpret data using hacking, statistics and machine learning. It also discusses challenges of having enough data scientists. Other topics discussed include important ideas for data science like interdisciplinary work, algorithms, coding practices, data strategy, causation vs. correlation, and feedback loops. Building predictive models is also discussed with steps like defining objectives, accessing and understanding data, preprocessing, and evaluating models.
This document discusses the unrealized power of data and predictive analytics. It begins by highlighting how predictive analytics can be used for forecasting, targeting customers, fraud detection, risk assessment, customer churn prediction, and price elasticity analysis. It then provides examples of predictive analytics in action in various industries like healthcare, education, law enforcement, and human resources. The document emphasizes that predictive analytics must become simpler to use and be integrated into business processes. It outlines the data science process and importance of data wrangling. Finally, it discusses Microsoft's CloudML Studio and Data Lab products for building predictive models using machine learning algorithms and analyzing customer data to predict things like equipment failures and customer churn.
This document discusses streaming data processing and the adoption of scalable frameworks and platforms for handling streaming or near real-time analysis and processing over the next few years. These platforms will be driven by the needs of large-scale location-aware mobile, social and sensor applications, similar to how Hadoop emerged from large-scale web applications. The document also references forecasts of over 50 billion intelligent devices by 2015 and 275 exabytes of data per day being sent across the internet by 2020, indicating challenges around data of extreme size and the need for rapid processing.
This document discusses the past, present, and future of machine learning. It outlines how machine learning has evolved from early attempts at neural networks and expert systems to today's deep learning techniques powered by large datasets and distributed computing. The document argues that machine learning and predictive analytics will be core capabilities that impact many industries and applications going forward, including personalized insurance, fraud detection, equipment monitoring, and more. Intelligence from machine learning will become "ambient" and help solve hard problems by extracting value from big data.
This document appears to be lecture slides for a course on deriving knowledge from data at scale. It covers many topics related to building machine learning models including data preparation, feature selection, classification algorithms like decision trees and support vector machines, and model evaluation. It provides examples applying these techniques to a Titanic passenger dataset to predict survival. It emphasizes the importance of data wrangling and discusses various feature selection methods.
Machine Learning has become a must to improve insight, quality and time to market. But it's also been called the 'high interest credit card of technical debt' with challenges in managing both how it's applied and how its results are consumed.
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.
Heuristic design of experiments w meta gradient searchGreg Makowski
Once you have started learning about predictive algorithms, and the basic knowledge discovery in databases process, what is the next level of detail to learn for a consulting project?
* Give examples of the many model training parameters
* Track results in a "model notebook"
* Use a model metric that combines both accuracy and generalization to rank models
* How to strategically search over the model training parameters - use a gradient descent approach
* One way to describe an arbitrarily complex predictive system is by using sensitivity analysis
H2O World - Top 10 Data Science Pitfalls - Mark LandrySri Ambati
H2O World 2015 - Mark Landry
Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
This document provides an overview of machine learning techniques that can be applied in finance, including exploratory data analysis, clustering, classification, and regression methods. It discusses statistical learning approaches like data mining and modeling. For clustering, it describes techniques like k-means clustering, hierarchical clustering, Gaussian mixture models, and self-organizing maps. For classification, it mentions discriminant analysis, decision trees, neural networks, and support vector machines. It also provides summaries of regression, ensemble methods, and working with big data and distributed learning.
Two hour lecture I gave at the Jyväskylä Summer School. The purpose of the talk is to give a quick non-technical overview of concepts and methodologies in data science. Topics include a wide overview of both pattern mining and machine learning.
See also Part 2 of the lecture: Industrial Data Science. You can find it in my profile (click the face)
Data Science Training | Data Science For Beginners | Data Science With Python...Simplilearn
This Data Science presentation will help you understand what is Data Science, who is a Data Scientist, what does a Data Scientist do and also how Python is used for Data Science. Data science is an interdisciplinary field of scientific methods, processes, algorithms and systems to extract knowledge or insights from data in various forms, either structured or unstructured, similar to data mining. This Data Science tutorial will help you establish your skills at analytical techniques using Python. With this Data Science video, you’ll learn the essential concepts of Data Science with Python programming and also understand how data acquisition, data preparation, data mining, model building & testing, data visualization is done. This Data Science tutorial is ideal for beginners who aspire to become a Data Scientist.
This Data Science presentation will cover the following topics:
1. What is Data Science?
2. Who is a Data Scientist?
3. What does a Data Scientist do?
This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you’ll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jumpstart your career with this interactive, hands-on course.
Why learn Data Science?
Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. A data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.
You can gain in-depth knowledge of Data Science by taking our Data Science with python certification training course. With Simplilearn’s Data Science certification training course, you will prepare for a career as a Data Scientist as you master all the concepts and techniques. Those who complete the course will be able to:
1. Gain an in-depth understanding of data science processes, data wrangling, data exploration, data visualization, hypothesis building, and testing. You will also learn the basics of statistics.
Install the required Python environment and other auxiliary tools and libraries
2. Understand the essential concepts of Python programming such as data types, tuples, lists, dicts, basic operators and functions
3. Perform high-level mathematical computing using the NumPy package and its largelibrary of mathematical functions.
Learn more at: https://www.simplilearn.com
Machine Learning and Real-World ApplicationsMachinePulse
This presentation was created by Ajay, Machine Learning Scientist at MachinePulse, to present at a Meetup on Jan. 30, 2015. These slides provide an overview of widely used machine learning algorithms. The slides conclude with examples of real world applications.
Ajay Ramaseshan, is a Machine Learning Scientist at MachinePulse. He holds a Bachelors degree in Computer Science from NITK, Suratkhal and a Master in Machine Learning and Data Mining from Aalto University School of Science, Finland. He has extensive experience in the machine learning domain and has dealt with various real world problems.
From Raw Data to Deployed Product. Fast & Agile with CRISP-DMMichał Łopuszyński
The document summarizes the Cross Industry Standard Process for Data Mining (CRISP-DM), which is the most popular methodology for data-centric projects. It walks through each step of the CRISP-DM process, including business understanding, data understanding, data preparation, modelling, evaluation, and deployment. For each step, it provides examples and highlights important dos and don'ts, such as thoroughly understanding the problem and data quality before modelling, automating repetitive data preparation tasks, and guarding against overfitting and data leakage during evaluation. The overall document serves as a guide to successfully applying the CRISP-DM process from raw data to deployed product.
The document provides guidance on building an end-to-end machine learning project to predict California housing prices using census data. It discusses getting real data from open data repositories, framing the problem as a supervised regression task, preparing the data through cleaning, feature engineering, and scaling, selecting and training models, and evaluating on a held-out test set. The project emphasizes best practices like setting aside test data, exploring the data for insights, using pipelines for preprocessing, and techniques like grid search, randomized search, and ensembles to fine-tune models.
This document summarizes Michał Łopuszyński's presentation on using an agile approach based on the CRISP-DM methodology for data mining projects. It discusses the key phases of CRISP-DM including business understanding, data understanding, data preparation, modelling, evaluation, and deployment. For each phase, it provides examples of best practices and challenges, with an emphasis on spending sufficient time on data understanding and preparation, developing models with the deployment context in mind, and carefully evaluating results against business objectives.
Random Forest Tutorial | Random Forest in R | Machine Learning | Data Science...Edureka!
This Edureka Random Forest tutorial will help you understand all the basics of Random Forest machine learning algorithm. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts, learn random forest analysis along with examples. Below are the topics covered in this tutorial:
1) Introduction to Classification
2) Why Random Forest?
3) What is Random Forest?
4) Random Forest Use Cases
5) How Random Forest Works?
6) Demo in R: Diabetes Prevention Use Case
You can also take a complete structured training, check out the details here: https://goo.gl/AfxwBc
Slides from my presentation at the Data Intelligence conference in Washington DC (6/23/2017). See this link for the abstract: http://www.data-intelligence.ai/presentations/36
A Practical-ish Introduction to Data ScienceMark West
In this talk I will share insights and knowledge that I have gained from building up a Data Science department from scratch. This talk will be split into three sections:
1. I'll begin by defining what Data Science is, how it is related to Machine Learning and share some tips for introducing Data Science to your organisation.
2. Next up well run through some commonly used Machine Learning algorithms used by Data Scientists, along with examples for use cases where these algorithms can be applied.
3. The final third of the talk will be a demonstration of how you can quickly get started with Data Science and Machine Learning using Python and the Open Source scikit-learn Library.
This document provides an overview of becoming a data scientist. It defines a data scientist and lists common job titles. It discusses the functions of a data scientist like devising business strategies, descriptive/predictive analytics, and data mining. Examples are provided of customer churn analysis and market basket analysis. The skills, aptitudes, and educational paths to become a data scientist are also outlined.
This document contains a resume for Kajul Verma, an IT professional with 4 years of experience as a Product Implementation Engineer. They have a Bachelor's degree in Information Technology and expertise in technologies like Java, JavaScript, HTML5, CSS3, AngularJS, Linux, Windows, Apache Tomcat. They are seeking new opportunities and their experience includes managing ERP projects, designing marketing campaigns, troubleshooting code issues, and training clients on web technologies.
Reliance Field Services is a nationwide network that provides field representatives to facilitate the collection industry. Their main objective is to perform nationwide field services for clients to maximize collection results. They meet customer needs like improving contact and stressing urgency to debtors. Their services include general field calls where representatives convey urgent messages to debtors and leave "doorknockers". They also provide additional services like insurance loss reports, property preservation, and loss mitigation campaigns.
Dokumen ini merupakan rincian pelaksanaan anggaran Satuan Kerja Perangkat Daerah Kecamatan Kerajaan Kabupaten Pakpak Bharat tahun 2014. Terdapat rincian belanja tidak langsung sebesar Rp1,9 triliun, belanja langsung Rp631 miliar, dan defisit Rp2,5 triliun. Rencana pelaksanaan anggaran dibagi per triwulan dengan alokasi terbesar pada triwulan pertama.
Participantes Concurso Miradas DeGatitos 2013 Homenaje al Día del Animaldegatitos
El concurso Miradas 2013 contó con la participación de fotógrafos aficionados y profesionales de todo el país. Se presentaron más de 500 fotografías de paisajes, retratos, flora y fauna silvestre, entre otros temas. Los ganadores recibieron premios en efectivo y sus fotos fueron exhibidas en una muestra itinerante.
Slides jose falck zepeda nas study economics december 2016 original submttedJose Falck Zepeda
This presentations summarizes the economic impacts of GE crops as included in Chapter 6 of the National Academies of Sciences, Engineering and Medicine report on genetically engineered crops released May 2016.
Las fundaciones rescatan perros callejeros y maltratados para darlos en adopción, pero la cantidad de rescates supera a las adopciones. Para adoptar, las familias deben asegurar el bienestar del perro proporcionando atención médica, alimento y un espacio seguro acorde con su tamaño, y estar dispuestas a asumir las responsabilidades de un animal de compañía.
Neev uses a scrum based Agile Development methodology, a proven Extended Delivery Center model of engagement - all designed to ensure high quality, timely deliverables.
El documento presenta los fundamentos teóricos del aprendizaje construccionista desarrollado por Seymour Papert, el aprendizaje por proyectos, el aprendizaje colaborativo, el pensamiento crítico y creativo, el trabajo integral y el desarrollo de competencias digitales. Estos enfoques promueven que los estudiantes construyan el conocimiento de manera activa a través de la realización de proyectos y el uso de herramientas tecnológicas, y desarrollen habilidades para trabajar en equipo y pensar de manera
Single Instruction Multiple Data (SIMD) is an approach to improve performance by replicating data paths rather than control. Vector processors apply the same operation to all elements of a vector in parallel. The ILLIAC IV was an early SIMD computer from 1972 with 64 processing elements. Vector processors store vectors in registers and apply the same instruction to all elements simultaneously. The Cray-1 was an influential vector supercomputer from 1978 that used vector registers and optimized memory access for vectors. Vectorization improves performance by performing the same operation on multiple data elements with a single instruction.
DSM system
Shared memory
On chip memory
Bus based multiprocessor
Working through cache
Write through cache
Write once protocol
Ring based multiprocessor
Protocol used
Similarities and differences b\w ring based and bus based
Dokumen tersebut membahas tentang budidaya tanaman sayuran, meliputi metode konvensional dan hidroponik, teknik budidaya mencakup persiapan lahan, pembibitan, penanaman, pemupukan, pengendalian hama dan penyakit, pemangkasan, pengajiran, penyiraman, penyiangan, pemanenan dan pasca panen, serta biaya produksi untuk budidaya cabe sebagai contoh.
- Big data is growing rapidly in both commercial and scientific databases. Data mining is commonly used to extract useful information from large datasets. It helps with customer service, hypothesis formation, and more.
- Recent technological advances are generating large amounts of medical and genomic data. Data mining offers potential solutions for automated analysis of patient histories, gene function prediction, and drug discovery. Traditional techniques may be unsuitable due to data enormity, dimensionality, and heterogeneity.
- Data mining involves tasks like classification, association rule mining, clustering, and outlier detection. Various machine learning algorithms are applied including decision trees, naive Bayes, and neural networks.
This document provides an overview of key aspects of data preparation and processing for data mining. It discusses the importance of domain expertise in understanding data. The goals of data preparation are identified as cleaning missing, noisy, and inconsistent data; integrating data from multiple sources; transforming data into appropriate formats; and reducing data through feature selection, sampling, and discretization. Common techniques for each step are outlined at a high level, such as binning, clustering, and regression for handling noisy data. The document emphasizes that data preparation is crucial and can require 70-80% of the effort for effective real-world data mining.
Anomaly detection (or Outlier analysis) is the identification of items, events or observations which do not conform to an expected pattern or other items in a dataset. It is used is applications such as intrusion detection, fraud detection, fault detection and monitoring processes in various domains including energy, healthcare and finance.
In this workshop, we will discuss the core techniques in anomaly detection and discuss advances in Deep Learning in this field.
Through case studies, we will discuss how anomaly detection techniques could be applied to various business problems. We will also demonstrate examples using R, Python, Keras and Tensorflow applications to help reinforce concepts in anomaly detection and best practices in analyzing and reviewing results.
What you will learn:
Anomaly Detection: An introduction
Graphical and Exploratory analysis techniques
Statistical techniques in Anomaly Detection
Machine learning methods for Outlier analysis
Evaluating performance in Anomaly detection techniques
Detecting anomalies in time series data
Case study 1: Anomalies in Freddie Mac mortgage data
Case study 2: Auto-encoder based Anomaly Detection for Credit risk with Keras and Tensorflow
Data mining aims to discover useful patterns from large datasets. It involves applying machine learning, statistical, and visualization techniques to extract knowledge from data. Common data mining tasks include classification, clustering, association rule mining, and anomaly detection. Data mining has applications in many domains like marketing, fraud detection, and science. However, privacy and ethical issues also need consideration with widespread use of data mining.
Machine Learning 2 deep Learning: An IntroSi Krishan
The document provides an introduction to machine learning and deep learning. It discusses that machine learning involves making computers learn patterns from data without being explicitly programmed, while deep learning uses neural networks with many layers to perform end-to-end learning from raw data without engineered features. Deep learning has achieved remarkable success in applications involving computer vision, speech recognition, and natural language processing due to its ability to learn representations of the raw data. The document outlines popular deep learning models like convolutional neural networks and recurrent neural networks and provides examples of applications in areas such as image classification and prediction of heart attacks.
Data mining is the process of analyzing large amounts of data to discover hidden patterns and relationships. It involves several steps including data preparation, modeling, evaluation, and deployment. A standard process like CRISP-DM provides guidelines and documentation to make the data mining process reliable and repeatable. Data mining can be used for applications like forecasting, classification, clustering, association analysis, and sequencing to help organizations in areas such as fraud detection, customer relationship management, and risk management.
What Is Data Science? Data Science Course - Data Science Tutorial For Beginne...Edureka!
This Edureka Data Science course slides will take you through the basics of Data Science - why Data Science, what is Data Science, use cases, BI vs Data Science, Data Science tools and Data Science lifecycle process. This is ideal for beginners to get started with learning data science.
You can read the blog here: https://goo.gl/OoDCxz
You can also take a complete structured training, check out the details here: https://goo.gl/AfxwBc
Data Science for Fundraising: Build Data-Driven Solutions Using R - Rodger De...Rodger Devine
Although the non-profit industry has advanced using CRMs and donor databases, it has not fully explored the data stored in those databases. Meanwhile, data scientists, in the for-profit industry, using sophisticated tools, have generated data-driven results and effective solutions for several challenges in their organizations. Regardless of your skill level, you can equip yourself and help your organization succeed with these data science techniques using R.
Post Graduate Admission Prediction SystemIRJET Journal
This document presents a post graduate admission prediction system built using machine learning algorithms. The system analyzes factors like GRE scores, TOEFL scores, undergraduate GPA, research experience etc. to predict the universities a student is likely to get admission in. Various machine learning models like multiple linear regression, random forest regression, support vector machine and logistic regression are implemented and evaluated on an admission prediction dataset. Logistic regression achieved the highest accuracy of 97%. A web application called PostPred is developed using the logistic regression model to help students predict suitable universities to apply to based on their profile.
Graphs are commonly used for (1) master data management to support complex non-hierarchical relationships between entities, (2) network and IT operations management to analyze dependencies in real-time across large connected systems, and (3) fraud detection by connecting related entities to uncover organized fraud rings. Example use cases include an insurer improving access to customer data, a social network powering recommendations by connecting users and interests, and a telecom enabling real-time authentication by modeling identity and access permissions as a graph.
Delve into the realm of predictive modeling for loan approval. Learn how data science is revolutionizing the lending industry, making the loan approval process faster, more accurate, and fairer. Discover the key factors that influence loan decisions and how predictive modelling is shaping the future of lending. visit https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/ for more data science insights
- The document describes a project to predict customer churn for a telecom company using classification algorithms. It analyzes a dataset of 3333 customers to identify variables that contribute to churn and builds models using KNN and C4.5.
- The C4.5 model achieved higher accuracy (94.9%) than KNN (87.1%) on the test data. Key variables for predicting churn were found to be day minutes, customer service calls, and international plan.
- The model can help the telecom company prevent churn by focusing retention efforts on at-risk customers identified through these important variables.
This Machine Learning Algorithms presentation will help you learn you what machine learning is, and the various ways in which you can use machine learning to solve a problem. At the end, you will see a demo on linear regression, logistic regression, decision tree and random forest. This Machine Learning Algorithms presentation is designed for beginners to make them understand how to implement the different Machine Learning Algorithms.
Below topics are covered in this Machine Learning Algorithms Presentation:
1. Real world applications of Machine Learning
2. What is Machine Learning?
3. Processes involved in Machine Learning
4. Type of Machine Learning Algorithms
5. Popular Algorithms with a hands-on demo
- Linear regression
- Logistic regression
- Decision tree and Random forest
- N Nearest neighbor
What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
- - - - - - - -
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
- - - - - - -
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
- - - - - -
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
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Classification and Clustering Analysis using Weka Ishan Awadhesh
This Term Paper demonstrates the classification and clustering analysis on Bank Data using Weka. Classification Analysis is used to determine whether a particular customer would purchase a Personal Equity PLan or not while Clustering Analysis is used to analyze the behavior of various customer segments.
Two objectives for this project include recommending marketing campaign strategies and predicting where a new guest will book their first travel destinations.
Join us for the Best Selenium certification course at Edux factor and enrich your carrier.
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Predictive Model and Record Description with Segmented Sensitivity Analysis (...Greg Makowski
Describing a predictive data mining model can provide a competitive advantage for solving business problems with a model. The SSA approach can also provide reasons for the forecast for each record. This can help drive investigations into fields and interactions during a data mining project, as well as identifying "data drift" between the original training data, and the current scoring data. I am working on open source version of SSA, first in R.
LOAN APPROVAL PRDICTION SYSTEM USING MACHINE LEARNING.Souma Maiti
This document describes a machine learning model for predicting loan approvals. It discusses collecting loan application data and preprocessing the data which includes cleaning, feature selection, and scaling. Various machine learning algorithms are trained on the data including logistic regression, decision trees, random forest, support vector machines, and gradient boosting. Their accuracies are compared and random forest is found to perform best. The optimal model is deployed with a user interface created using Streamlit. The system aims to automate and improve the loan approval process for banks.
This document provides an overview of signals and signal extraction methodology. It begins with defining a signal as a pattern that is indicative of an impending business outcome. Examples of signals in different industries are provided. The document then outlines a 9-step methodology for extracting signals from data, including defining the business problem, building a data model, conducting univariate and correlation analysis, building predictive models, creating a business narrative, and identifying actions and ROI. R commands for loading, manipulating, and analyzing data in R are also demonstrated. The key points are that signals can provide early warnings for business outcomes and the outlined methodology is a rigorous approach for extracting meaningful signals from data.
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
State of Artificial intelligence Report 2023kuntobimo2016
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
2. Deriving Knowledge from Data at Scale
Feature extraction and selection are the most important but underrated step
of machine learning. Better features are better than better algorithms…
5. Deriving Knowledge from Data at Scale
Lecture Objectives
homework
There is an order or workflow
that takes place here, don’t lose
the forest in the trees…
7. Deriving Knowledge from Data at Scale
• Cluster 0 – It contains a cluster of Females with an average age of 37 who live in inner city and
possess saving account number and current account number. They are unmarried and do not have
any mortgage or pep. The average monthly income is 23,300.
• Cluster 1 - It contains a cluster of Females with an average age of 44 who live in rural area and
possess saving account number and current account number. They are married and do not have
any mortgage or pep. The average monthly income is 27,772.
• Cluster 2 - It contains a cluster of Females with an average age of 48 who live in inner city and
possess current account number but no saving account number. They are unmarried and do not
have mortgage but do have pep. The average monthly income is 27,668.
• Cluster 3 - It contains a cluster of Females with an average age of 39 who live in town and possess
saving account number and current account number. They are married and do not have any
mortgage or pep. The average monthly income is 24,047.
• Cluster 4 - It contains a cluster of Males with an average age of 39 who live in inner city and
possess current account number but no saving account number. They are married and have
mortgage and pep. The average monthly income is 26,359.
• Cluster 5 - It contains a cluster of Males with an average age of 47 who live in inner city and
possess saving account number and current account number. They are unmarried and do not have
mortgage but do have pep. The average monthly income is 35,419.
16. Deriving Knowledge from Data at Scale
No Prob Target CustID Age
1 0.97 Y 1746 …
2 0.95 N 1024 …
3 0.94 Y 2478 …
4 0.93 Y 3820 …
5 0.92 N 4897 …
… … … …
99 0.11 N 2734 …
100 0.06 N 2422
Use a model to assign score (probability) to each instance
Sort instances by decreasing score
Expect more targets (hits) near the top of the list
3 hits in top 5% of
the list
If there 15 targets
overall, then top 5
has 3/15=20% of
targets
17. Deriving Knowledge from Data at Scale
40% of responses for
10% of cost
Lift factor = 4
80% of responses for
40% of cost
Lift factor = 2
Model
Random
22. Deriving Knowledge from Data at Scale
to impact…
1. Build our predictive model in WEKA Explorer;
2. Use our model to score (predict) which new customers to
target in our upcoming advertising campaign;
• ARFF file manipulation (hacking), all too common pita…
• Excel manipulation to join model output with our customers list
3. Compute the lift chart to assess business impact of our
predictive model on the advertising campaign
• How are Lift charts built, of all the charts and/or performance
measures from a model this one is ‘on you’ to construct;
• Where is the business ‘bang for the buck’?
26. Deriving Knowledge from Data at Scale
You can’t turn data lead into
modeling gold – we’re data
scientists, not data alchemists…
27. Deriving Knowledge from Data at Scale
Motivation: Real world examples
Example (1)
Lesson: Correct data transformation is important!
28. Deriving Knowledge from Data at Scale
Motivation: Real world examples
Example (2): KDD Cup 2001
Lesson: A model that uses lots of features can turn out to be
very sub-optimal, however well it is designed!
29. Deriving Knowledge from Data at Scale
Motivation: Real world examples
Example (3)
Lesson: Feature selection can be crucial even when the
number of features is small!
30. Deriving Knowledge from Data at Scale
Motivation: Real world examples
Example (4)
Lesson: Variations of the same ML method can give vastly
different performances!
33. Deriving Knowledge from Data at Scale
Global competitions
1½ weeks 70.8%
Competition closes 77%
State of the art 70%
Predicting HIV viral load
Improved by 10%
34. Deriving Knowledge from Data at Scale
Mismatch between those with data and
those with the skills to analyse it
Crowdsourcing
35. Deriving Knowledge from Data at Scale
Forecast Error
(MASE)
Existing model
Tourism Forecasting Competition
Aug 9 2 weeks
later
1 month
later
Competition
End
36. Deriving Knowledge from Data at Scale
• neural networks
• logistic regression
• support vector machine
• decision trees
• ensemble methods
• adaBoost
• Bayesian networks
• genetic algorithms
• random forest
• Monte Carlo methods
• principal component analysis
• Kalman filter
• evolutionary fuzzy modeling
Users apply different techniques
37. Deriving Knowledge from Data at Scale
VicRoads has an algorithm they use to forecast travel time on Melbourne freeways (taking into
account time, weather, accidents, etc). Their current model is inaccurate and somewhat
useless. They want to do better (or at least find out about whether it’s possible to do better).
52. Deriving Knowledge from Data at Scale
VicRoads has an algorithm they use to forecast travel time on Melbourne freeways (taking into
account time, weather, accidents etc). Their current model is inaccurate and somewhat useless.
They want to do better (or at least find out about whether it’s possible to do better).
57. Deriving Knowledge from Data at Scale
Homework Week 6
Monday Sept. 21st
Upload to site…
http://blog.kaggle.com/category/dojo/
Content is 10 pages of interview on how the team(s) built their models, some have multiple interviews;
You will review at least 10 interviews, bounce around do not go sequentially.
1) What model(s) did they use, 2) insights they had that influenced modeling, 3) what feature creation and
selection, 4) other observations. I will cons all these together and upload as shared document on our site.
61. Deriving Knowledge from Data at Scale
https://www.kaggle.com/c/springleaf-marketing-response
not
Determine whether to send a direct mail piece to a customer
71. Deriving Knowledge from Data at Scale
Data
Acquisition
Data
Exploration
Pre-
processing
Feature and
Target
construction
Train/ Test
split
Feature
selection
Model
training
Model
scoring
Model
scoring
Evaluation
Evaluation
Compare
metrics
72. Deriving Knowledge from Data at Scale
• Data preparation step is by far the most time consuming step
0
10
20
30
40
50
60
70
Understanding
of Domain
Understanding
of Data
Preparation of
Data
Data Mining Evaluation of
Results
Deployment of
Results
KDDM steps
relative effort [%] Cabena et al. estimates
Shearer estimates
Cios and Kurgan estimates
75. Deriving Knowledge from Data at Scale
1. Do you have domain knowledge?
2. Are your features commensurate?
3. Do you suspect interdependence of features?
4. Do you need to prune the input variables
5. Do you need to assess features individually
6. Do you need a predictor?
7. Do you suspect your data is “dirty”
8. Do you know what to try first?
9. Do you have new ideas, time, computational resources, and enough examples?
10. Do you want a stable solution
84. Deriving Knowledge from Data at Scale
1. Unique values
2. Most frequent values
3. Highest and lowest values
4. Location and dispersion – gini, statistical test for dispersion
5. Quartiles
85. Deriving Knowledge from Data at Scale
1. Missing values
2. Outliers
3. Coding
4. Constraints
86. Deriving Knowledge from Data at Scale
Missing values – UCI machine learning repository, 31 of 68 data sets
reported to have missing values. “Missing” can mean many things…
MAR: "Missing at Random":
– usually best case
– usually not true
Non-randomly missing
Presumed normal, so not measured
Causally missing
– attribute value is missing because of other attribute values (or because of
the outcome value!)
90. Deriving Knowledge from Data at Scale
Outliers – may indicate ‘bad data’ or it may represent
something scientifically interesting in the data…
Simple working definition: an outlier is an element of a data sequence
S that is inconsistent with expectations, based on the majority of other
elements of S.
Sources of outliers
• Measurement errors
• Other uninteresting anomalous data
• Surprising observations that may be important
91. Deriving Knowledge from Data at Scale
Outliers – may indicate ‘bad data’ or it may represent
something scientifically interesting in the data…
Simple working definition: an outlier is an element of a data sequence
S that is inconsistent with expectations, based on the majority of other
elements of S.
Sources of outliers
• Insurance company sees niche of sports car enthusiasts, married boomers
with kids and second family car. Low risk, lower rate to attract. Simple case
where outlier carries meaning for modeling…
92. Deriving Knowledge from Data at Scale
Outliers can distort the regression results. When an outlier is
included in the analysis, it pulls the regression line towards
itself. This can result in a solution that is more accurate for the
outlier, but less accurate for all the other cases in the data set.
Outliers – may indicate ‘bad data’ or it may represent
something scientifically interesting in the data…
93. Deriving Knowledge from Data at Scale
Identify outliers
• Question origin, domain knowledge invaluable
• Dispersion – "spread" of a data set, departure from central tendency, use a box plot…
Deal with outliers
• Winsorize – Set all outliers to a specified percentile of the data. Not
equivalent to trimming, which simply excludes data. In a Winsorized
estimator, extreme values are instead replaced by certain percentiles (the
trimmed minimum and maximum). Same as clipping in signal processing.
Outliers – may indicate ‘bad data’ or it may represent
something scientifically interesting in the data…
94. Deriving Knowledge from Data at Scale
Identify outliers
• Question origin, domain knowledge invaluable
• Dispersion – "spread" of a data set, departure from central tendency, use a box plot…
Deal with outliers
• Include – Robust statistics, a convenient way to summarize results when
they include a small proportion of outliers. A hot topic for research, see
NIPS 2010 Workshop, Robust Statistical learning (robustml).
Outliers – may indicate ‘bad data’ or it may represent
something scientifically interesting in the data…
95. Deriving Knowledge from Data at Scale
• Entity integrity
• Referential integrity
• Type checking
• Format
• Bounds checking
Constraints
96. Deriving Knowledge from Data at Scale
• weka.filters.unsupervised.instance.RemoveMisclassified
• weka.filters.unsupervised.instance.RemovePercentage
• weka.filters.unsupervised.instance.RemoveRange
• weka.filters.unsupervised.instance.RemoveWithValues
• weka.filters.unsupervised.instance.Resample
98. Deriving Knowledge from Data at Scale
Simple Definition
feature selection problem
Feature extraction
11 .
{ ,..., ,..., } { ,..., ,..., }j mi n i i if selection
f f f f f f
F
F‘ F F‘
1 1 1 1 1.
{ ,..., ,..., } { ( ,..., ),..., ( ,..., ),..., ( ,..., )}i n n j n m nf extraction
f f f g f f g f f g f f
100. Deriving Knowledge from Data at Scale
3 types of methods
Filter Methods
Wrapper Methods
Embedded Methods
decision trees, random forests
101. Deriving Knowledge from Data at Scale
Most learning methods implicitly do feature selection:
• Decision Trees: use info gain or gain ratio to decide what attributes to use as
tests. Many features don’t get used.
• neural nets: backprop learns strong connections to some inputs, and near-
zero connections to other inputs.
• kNN, MBL (any similarity based learning): weights in Weighted Euclidean
Distance determine how important each feature is. Weights near zero mean
feature is not used.
• SVMs: maximum margin hyperplane may focus on important features,
ignore irrelevant features.
So why do we need feature selection?
Data Integration
102. Deriving Knowledge from Data at Scale
Curse of Dimensionality
exponentially
In many cases the information lost by
discarding variables is made up for by a
more accurate mapping/sampling in the
lower-dimensional space !
103. Deriving Knowledge from Data at Scale
Feature Selection and Engineering
Optimality?
This deserves a deeper treatment, which we will cover next week with
hands-on exercises in class…
104. Deriving Knowledge from Data at Scale
Numerical data
• Binning – a mapping to discrete categories;
• Recenter – shift by c where max, min, avg and median shift, the range and
standard deviation will not shift;
• Rescale – multiply everything by d, all measures change;
• Standard ND – recenter, make mean 0, divide all previous values by SD
Character data
• Lower case
• Spellcheck
• Data extraction (e.g. regular expressions)
Coding – shape and enrich…
110. Deriving Knowledge from Data at Scale
Household income
$10.000 $200.000
very
low
low average high very
high
111. Deriving Knowledge from Data at Scale
Less features, more discrimination ability
concept hierarchies
112. Deriving Knowledge from Data at Scale
• Equal-width (distance) partitioning
uniform grid
• Equal-depth (frequency) partitioning
• Class label based partitioning