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Anomaly Detection: Algorithms, Explanations, Applications
 
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Anomaly detection is important for data cleaning, cybersecurity, and robust AI systems. This talk will review recent work in our group on (a) benchmarking existing algorithms, (b) developing a theoretical understanding of their behavior, (c) explaining anomaly "alarms" to a data analyst, and (d) interactively re-ranking candidate anomalies in response to analyst feedback. Then the talk will describe two applications: (a) detecting and diagnosing sensor failures in weather networks and (b) open category detection in supervised learning. See more at https://www.microsoft.com/en-us/research/video/anomaly-detection-algorithms-explanations-applications/
Views: 17434 Microsoft Research
Data Mining Classification and Prediction ( in Hindi)
 
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A tutorial about classification and prediction in Data Mining .
Views: 39569 Red Apple Tutorials
Brian Kent: Density Based Clustering in Python
 
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PyData NYC 2015 Clustering data into similar groups is a fundamental task in data science. Probability density-based clustering has several advantages over popular parametric methods like K-Means, but practical usage of density-based methods has lagged for computational reasons. I will discuss recent algorithmic advances that are making density-based clustering practical for larger datasets. Clustering data into similar groups is a fundamental task in data science applications such as exploratory data analysis, market segmentation, and outlier detection. Density-based clustering methods are based on the intuition that clusters are regions where many data points lie near each other, surrounded by regions without much data. Density-based methods typically have several important advantages over popular model-based methods like K-Means: they do not require users to know the number of clusters in advance, they recover clusters with more flexible shapes, and they automatically detect outliers. On the other hand, density-based clustering tends to be more computationally expensive than parametric methods, so density-based methods have not seen the same level of adoption by data scientists. Recent computational advances are changing this picture. I will talk about two density-based methods and how new Python implementations are making them more useful for larger datasets. DBSCAN is by far the most popular density-based clustering method. A new implementation in Dato's GraphLab Create machine learning package dramatically speeds up DBSCAN computation by taking advantage of GraphLab Create's multi-threaded architecture and using an algorithm based on the connected components of a similarity graph. The density Level Set Tree is a method first proposed theoretically by Chaudhuri and Dasgupta in 2010 as a way to represent a probability density function hierarchically, enabling users to use all density levels simultaneous, rather than choosing a specific level as with DBSCAN. The Python package DeBaCl implements a modification of this method and a tool for interactively visualizing the cluster hierarchy. Slides available here: https://speakerdeck.com/papayawarrior/density-based-clustering-in-python Notebooks: http://nbviewer.ipython.org/github/papayawarrior/public_talks/blob/master/pydata_nyc_dbscan.ipynb http://nbviewer.ipython.org/github/papayawarrior/public_talks/blob/master/pydata_nyc_DeBaCl.ipynb
Views: 16542 PyData
Final Year Projects | Information-Theoretic Outlier Detection for Large-Scale Categorical Data
 
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Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 281 Clickmyproject
NEW - Fraud and Anomaly Detection using Oracle Advanced Analytics Part 1 Concepts
 
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This is Part 1 of my Fraud and Anomaly Detection using Oracle Advanced Analytics presentations and demos series. Hope you enjoy! www.twitter.com/CharlieDataMine
Views: 6347 Charles Berger
Final Year Projects | Information-Theoretic Outlier Detection for Large-Scale Categorical Data
 
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Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-778-1155 +91 958-553-3547 +91 967-774-8277 Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected] chat: http://support.elysiumtechnologies.com/support/livechat/chat.php
Views: 48 myproject bazaar
EXCEL PRO TIP: Outlier Detection
 
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For access to all pro tips, along with Excel project files, PDF slides, quizzes and 1-on-1 support, upgrade to the full course (75% OFF): https://courses.excelmaven.com/p/microsoft-excel-pro-tips FULL COURSE DESCRIPTION: This course is NOT an introduction to Excel. It's not about 101-style deep dives, or about showing off cheesy, impractical "hacks". It's about featuring some of Excel's most powerful and effective tools, and sharing them through crystal clear demos and unique, real-world case studies. We'll cover 75+ tools & techniques, organized into six categories: -Productivity -Formatting -Formulas -Visualization -PivotTables -Analytics Demos are self-contained and ranked by difficulty, so you can explore the content freely and master these tools and techniques in quick, bite-sized lessons. Full course includes LIFETIME access to: -10+ hours of high-quality video content -Downloadable PDF eBook -Excel project files (including data sets & solutions) -1-on-1 expert support -100% satisfaction guarantee (no questions asked!) Happy analyzing! -Chris (Founder, Excel Maven)
Views: 238 Excel Maven
mining data streams
 
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Subscribe today and give the gift of knowledge to yourself or a friend mining data streams
Views: 534 slideshow this
IDS Detection Methods /Techniques : Signature Based IDS and Anomaly Based IDS in Hindi
 
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📚📚📚📚📚📚📚📚 GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING 🎓🎓🎓🎓🎓🎓🎓🎓 SUBJECT :- Discrete Mathematics (DM) Theory Of Computation (TOC) Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) 💡💡💡💡💡💡💡💡 EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. 💡💡💡💡💡💡💡💡 THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. 🙏🙏🙏🙏🙏🙏🙏🙏 YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING 📚📚📚📚📚📚📚📚
Views: 2628 5 Minutes Engineering
Final Year Projects | Distributed Strategies for Mining Outliers in Large Data Sets
 
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Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-778-1155 +91 958-553-3547 +91 967-774-8277 Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected] chat: http://support.elysiumtechnologies.com/support/livechat/chat.php
Views: 80 myproject bazaar
How to Use the Outliers Function in Excel
 
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See more: http://www.ehow.com/tech/
Views: 66456 eHowTech
data mining in banking
 
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-- Created using Powtoon -- Free sign up at http://www.powtoon.com/youtube/ -- Create animated videos and animated presentations for free. PowToon is a free tool that allows you to develop cool animated clips and animated presentations for your website, office meeting, sales pitch, nonprofit fundraiser, product launch, video resume, or anything else you could use an animated explainer video. PowToon's animation templates help you create animated presentations and animated explainer videos from scratch. Anyone can produce awesome animations quickly with PowToon, without the cost or hassle other professional animation services require.
Views: 105 nurul husna
Data mining final paper review
 
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This is the final presentation for the data mining final paper review please watch it Thank you
Views: 244 Akhil Kumar Mandoji
Application of Data Mining in Business Management | Basic Concepts of Data Mining
 
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There is a huge amount of data available in the Information Industry. This data is of no use until it is converted into useful information. It is necessary to analyze this huge amount of data and extract useful information from it. Extraction of information is not the only process we need to perform; data mining also involves other processes such as Data Cleaning, Data Integration, Data Transformation, Data Mining, Pattern Evaluation and Data Presentation. Once all these processes are over, we would be able to use this information in many applications such as Fraud Detection, Market Analysis, Production Control, Science Exploration, etc. What is Data Mining? Data Mining is defined as extracting information from huge sets of data. In other words, we can say that data mining is the procedure of mining knowledge from data. The information or knowledge extracted so can be used for any of the following applications − Market Analysis Fraud Detection Customer Retention Production Control Science Exploration Data Mining Applications Data mining is highly useful in the following domains − Market Analysis and Management Corporate Analysis & Risk Management Fraud Detection Apart from these, data mining can also be used in the areas of production control, customer retention, science exploration, sports, astrology, and Internet Web Surf-Aid 🧐 What we are going to Cover in the Video: 🧐 0:00 - 4: 35 Introduction to Data Mining 4:36 - 7:09 What is Data / Data vs. Information 7:09 - 9:13 What is Data Mining 10:00 -11: 00 Data Mining Process 9:14 - 11:45 Why Data Mining 12:04 - 14: 00 Application of data mining
Views: 516 UpDegree
Data Cleaning Part-2 Handling Ratio Data
 
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This video discusses the Basics Operations of Data Cleaning. Uni-variate Analysis is performed in SPSS, Excel and R-Studio. Datafile used in this video: https://goo.gl/aeDT2m PPT used in this video: https://goo.gl/JUBJgB
Views: 233 Neeraj Kaushik
Data Mining with Weka (1.5: Using a filter )
 
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Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 5: Using a filter http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/IGzlrn https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 70623 WekaMOOC
Machine Learning for Real-Time Anomaly Detection in Network Time-Series Data - Jaeseong Jeong
 
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Real-time anomaly detection plays a key role in ensuring that the network operation is under control, by taking actions on detected anomalies. In this talk, we discuss a problem of the real-time anomaly detection on a non-stationary (i.e., seasonal) time-series data of several network KPIs. We present two anomaly detection algorithms leveraging machine learning techniques, both of which are able to adaptively learn the underlying seasonal patterns in the data. Jaeseong Jeong is a researcher at Ericsson Research, Machine Learning team. His research interests include large-scale machine learning, telecom data analytics, human behavior predictions, and algorithms for mobile networks. He received the B.S., M.S., and Ph.D. degrees from Korea Advanced Institute of Science and Technology (KAIST) in 2008, 2010, and 2014, respectively.
Views: 16336 RISE SICS
INFO 320 Merck CRISP Model PPT Slideshow
 
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Research and slides were completed by Hiep Bach, Scott Hiatt, Allan Kolb, and Maurice Wilson
Views: 26 maurice wilson
How kNN algorithm works
 
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In this video I describe how the k Nearest Neighbors algorithm works, and provide a simple example using 2-dimensional data and k = 3. This presentation is available at: http://prezi.com/ukps8hzjizqw/?utm_campaign=share&utm_medium=copy
Views: 444018 Thales Sehn Körting
mod01lec01
 
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Views: 41582 Data Mining - IITKGP
The Basic Concept of Data Warehouse | What is DATA WAREHOUSING | Why #datawarehouse is important
 
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Data Warehousing a concept of storing Transformed data into a location where you can run your reports to make important business decisions. Many organizations use data warehouse to analyze sales, marketing etc. data to make important decisions. ETL is the tool that is used to transformed data from the initial load. If you have liked the video, please subscribe. Read our blogs - www.sqlultra.com Follow me on LinkedIn - https://www.linkedin.com/in/iqbalsqlexpert/
Views: 268 SQL ULTRA
box plot analysis in data mining MS Excel
 
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Box plot www.cs.gsu.edu/~cscyqz/courses/dm/slides/ch02.ppt How to do Box-plot Analysis in MS Excel, How to perform Boxplot analysis in Excel with Details Graph Generation and applying Whisker Line on it. calculating Quirtile on given range of data. calculate minium & Maximum .
Views: 1196 Sweven Developers
Advanced Data Mining with Weka (3.6: Application: Functional MRI Neuroimaging data)
 
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Advanced Data Mining with Weka: online course from the University of Waikato Class 3 - Lesson 6: Application: Functional MRI Neuroimaging data http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/8yXNiM https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 1449 WekaMOOC
Data Mining with Weka (2.2: Training and testing)
 
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Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 2: Training and testing http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/D3ZVf8 https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 76938 WekaMOOC
Dealing with Class Imbalance using Thresholding
 
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Author: Rumi Ghosh, Robert Bosch LLC. Abstract: We propose thresholding as an approach to deal with class imbalance. We define the concept of thresholding as a process of determining a decision boundary in the presence of a tunable parameter. The threshold is the maximum value of this tunable parameter where the conditions of a certain decision are satisfied. We show that thresholding is applicable not only for linear classifiers but also for non-linear classifiers. We show that this is the implicit assumption for many approaches to deal with class imbalance in linear classifiers. We then extend this paradigm beyond linear classification and show how non-linear classification can be dealt with under this umbrella framework of thresholding. The proposed method can be used for outlier detection in many real-life scenarios like in manufacturing. In advanced manufacturing units, where the manufacturing process has matured over time, the number of instances (or parts) of the product that need to be rejected (based on a strict regime of quality tests) becomes relatively rare and are defined as outliers. How to detect these rare parts or outliers beforehand? How to detect combination of conditions leading to these outliers? These are the questions motivating our research. This paper focuses on prediction of outliers and conditions leading to outliers using classification. We address the problem of outlier detection using classification. The classes are good parts (those passing the quality tests) and bad parts (those failing the quality tests and can be considered as outliers). The rarity of outliers transforms this problem into a class-imbalanced classification problem. More on http://www.kdd.org/kdd2016/ KDD2016 Conference is published on http://videolectures.net/
Views: 2275 KDD2016 video
Normal Distribution - Explained Simply (part 1)
 
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*** IMPROVED VERSION of this video here: https://youtu.be/tDLcBrLzBos I describe the standard normal distribution and its properties with respect to the percentage of observations within each standard deviation. I also make reference to two key statistical demarcation points (i.e., 1.96 and 2.58) and their relationship to the normal distribution. Finally, I mention two tests that can be used to test normal distributions for statistical significance. normal distribution, normal probability distribution, standard normal distribution, normal distribution curve, bell shaped curve
Views: 1130262 how2stats
Data Mining with Weka (3.2: Overfitting)
 
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Data Mining with Weka: online course from the University of Waikato Class X - Lesson X: Overfitting http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/1LRgAI https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 27973 WekaMOOC
Decision Tree Solved | Id3 Algorithm (concept and numerical) | Machine Learning (2019)
 
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Decision Tree is a supervised learning method used for classification and regression. It is a tree which helps us by assisting us in decision-making! Decision tree builds classification or regression models in the form of a tree structure. It breaks down a data set into smaller and smaller subsets and simultaneously decision tree is incrementally developed. The final tree is a tree with decision nodes and leaf nodes. A decision node has two or more branches. Leaf node represents a classification or decision. We cannot do more split on leaf nodes. The topmost decision node in a tree which corresponds to the best predictor called root node. Decision trees can handle both categorical and numerical data. #codewrestling #decisiontree #machinelearning #id3 Common terms used with Decision trees: Root Node: It represents entire population or sample and this further gets divided into two or more homogeneous sets. Splitting: It is a process of dividing a node into two or more sub-nodes. Decision Node: When a sub-node splits into further sub-nodes, then it is called decision node. Leaf/ Terminal Node: Nodes do not split is called Leaf or Terminal node. Pruning: When we remove sub-nodes of a decision node, this process is called pruning. You can say opposite process of splitting. Branch / Sub-Tree: A sub section of entire tree is called branch or sub-tree. Parent and Child Node: A node, which is divided into sub-nodes is called parent node of sub-nodes whereas sub-nodes are the child of parent node. How does Decision Tree works ? Decision tree is a type of supervised learning algorithm (having a pre-defined target variable) that is mostly used in classification problems. It works for both categorical and continuous input and output variables. In this technique, we split the population or sample into two or more homogeneous sets (or sub-populations) based on most significant splitter / differentiator in input variables. Advantages of Decision Tree: 1. Easy to Understand: Decision tree output is very easy to understand even for people from non-analytical background. It does not require any statistical knowledge to read and interpret them. Its graphical representation is very intuitive and users can easily relate their hypothesis. 2. Useful in Data exploration: Decision tree is one of the fastest way to identify most significant variables and relation between two or more variables. With the help of decision trees, we can create new variables / features that has better power to predict target variable. It can also be used in data exploration stage. For e.g., we are working on a problem where we have information available in hundreds of variables, there decision tree will help to identify most significant variable. 3 Decision trees implicitly perform variable screening or feature selection. 4. Decision trees require relatively little effort from users for data preparation. 5. Less data cleaning required: It requires less data cleaning compared to some other modeling techniques. It is not influenced by outliers and missing values to a fair degree. 6. Data type is not a constraint: It can handle both numerical and categorical variables. Can also handle multi-output problems. ID3 Algorithm Key Factors: Entropy- It is the measure of randomness or ‘impurity’ in the dataset. Information Gain: It is the measure of decrease in entropy after the dataset is split. Ask me A Question: [email protected] Music: https://www.bensound.com For Decision Trees slides comment below 😀
Views: 1573 Code Wrestling
Advanced Data Mining with Weka (1.6: Application: Infrared data from soil samples)
 
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Advanced Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 6: Infrared data from soil samples http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/JyCK84 https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 2078 WekaMOOC
Crime Data Analysis Using Kmeans Clustering Technique
 
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Introduction Data Mining deals with the discovery of hidden knowledge, unexpected patterns and new rules from large databases. Crime analyses is one of the important application of data mining. Data mining contains many tasks and techniques including Classification, Association, Clustering, Prediction each of them has its own importance and applications It can help the analysts to identify crimes faster and help to make faster decisions. The main objective of crime analysis is to find the meaningful information from large amount of data and disseminates this information to officers and investigators in the field to assist in their efforts to apprehend criminals and suppress criminal activity. In this project, Kmeans Clustering is used for crime data analysis. Kmeans Algorithm The algorithm is composed of the following steps: It randomly chooses K points from the data set. Then it assigns each point to the group with closest centroid. It again recalculates the centroids. Assign each point to closest centroid. The process repeats until there is no change in the position of centroids. Example of KMEANS Algorithm Let’s imagine we have 5 objects (say 5 people) and for each of them we know two features (height and weight). We want to group them into k=2 clusters. Our dataset will look like this: First of all, we have to initialize the value of the centroids for our clusters. For instance, let’s choose Person 2 and Person 3 as the two centroids c1 and c2, so that c1=(120,32) and c2=(113,33). Now we compute the Euclidean distance between each of the two centroids and each point in the data.
Views: 1417 E2MATRIX RESEARCH LAB
Data Mining with Weka (3.5: Pruning decision trees)
 
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Data Mining with Weka: online course from the University of Waikato Class 3 - Lesson 5: Pruning decision trees http://weka.waikato.ac.nz/ Slides (PDF): https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 39525 WekaMOOC
R tutorial: Introduction to cleaning data with R
 
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Learn more about cleaning data with R: https://www.datacamp.com/courses/cleaning-data-in-r Hi, I'm Nick. I'm a data scientist at DataCamp and I'll be your instructor for this course on Cleaning Data in R. Let's kick things off by looking at an example of dirty data. You're looking at the top and bottom, or head and tail, of a dataset containing various weather metrics recorded in the city of Boston over a 12 month period of time. At first glance these data may not appear very dirty. The information is already organized into rows and columns, which is not always the case. The rows are numbered and the columns have names. In other words, it's already in table format, similar to what you might find in a spreadsheet document. We wouldn't be this lucky if, for example, we were scraping a webpage, but we have to start somewhere. Despite the dataset's deceivingly neat appearance, a closer look reveals many issues that should be dealt with prior to, say, attempting to build a statistical model to predict weather patterns in the future. For starters, the first column X (all the way on the left) appears be meaningless; it's not clear what the columns X1, X2, and so forth represent (and if they represent days of the month, then we have time represented in both rows and columns); the different types of measurements contained in the measure column should probably each have their own column; there are a bunch of NAs at the bottom of the data; and the list goes on. Don't worry if these things are not immediately obvious to you -- they will be by the end of the course. In fact, in the last chapter of this course, you will clean this exact same dataset from start to finish using all of the amazing new things you've learned. Dirty data are everywhere. In fact, most real-world datasets start off dirty in one way or another, but by the time they make their way into textbooks and courses, most have already been cleaned and prepared for analysis. This is convenient when all you want to talk about is how to analyze or model the data, but it can leave you at a loss when you're faced with cleaning your own data. With the rise of so-called "big data", data cleaning is more important than ever before. Every industry - finance, health care, retail, hospitality, and even education - is now doggy-paddling in a large sea of data. And as the data get bigger, the number of things that can go wrong do too. Each imperfection becomes harder to find when you can't simply look at the entire dataset in a spreadsheet on your computer. In fact, data cleaning is an essential part of the data science process. In simple terms, you might break this process down into four steps: collecting or acquiring your data, cleaning your data, analyzing or modeling your data, and reporting your results to the appropriate audience. If you try to skip the second step, you'll often run into problems getting the raw data to work with traditional tools for analysis in, say, R or Python. This could be true for a variety of reasons. For example, many common algorithms require variables to be arranged into columns and for missing values to be either removed or replaced with non-missing values, neither of which was the case with the weather data you just saw. Not only is data cleaning an essential part of the data science process - it's also often the most time-consuming part. As the New York Times reported in a 2014 article called "For Big-Data Scientists, ‘Janitor Work’ Is Key Hurdle to Insights", "Data scientists ... spend from 50 percent to 80 percent of their time mired in this more mundane labor of collecting and preparing unruly digital data, before it can be explored for useful nuggets." Unfortunately, data cleaning is not as sexy as training a neural network to identify images of cats on the internet, so it's generally not talked about in the media nor is it taught in most intro data science and statistics courses. No worries, we're here to help. In this course, we'll break data cleaning down into a three step process: exploring your raw data, tidying your data, and preparing your data for analysis. Each of the first three chapters of this course will cover one of these steps in depth, then the fourth chapter will require you to use everything you've learned to take the weather data from raw to ready for analysis. Let's jump right in!
Views: 36234 DataCamp
Data-Mining Partnership for Library Operations, ARL Membership Meeting, Oct. 2012
 
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Data-Mining Partnership for Library Operations: An ARL Research Library Leadership Fellows Project, presented by Scott Britton and John Renaud at the 161st ARL Membership Meeting in Washington, DC, October 2012. For slides from this meeting, visit the ARL website: http://www.arl.org/resources/pubs/mmproceedings/161mm-proceedings.shtml
Mean Median and Mode: Understanding and Calculating Measures of Central Tendency
 
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http://youstudynursing.com/ Research eBook on Amazon: http://amzn.to/1hB2eBd Check out the links below and SUBSCRIBE for more youtube.com/user/NurseKillam For help with Research - Get my eBook "Research terminology simplified: Paradigms, axiology, ontology, epistemology and methodology" here: http://www.amazon.com/dp/B00GLH8R9C Related Videos: http://www.youtube.com/playlist?list=PLs4oKIDq23AdTCF0xKCiARJaBaSrwP5P2 Connect with me on Facebook Page: https://www.facebook.com/NursesDeservePraise Twitter: @NurseKillam https://twitter.com/NurseKillam Facebook: https://www.facebook.com/laura.killam LinkedIn: http://ca.linkedin.com/in/laurakillam Measures of central tendency include descriptive statistics including the mean, median and mode that are used to describe what the average person or response in a particular study is like. It is important as a research consumer to understand how these statistics are calculated and used to summarize and organize information in a study. Before talking about these measures of central tendency, it is important to know what a normal distribution is. The best measure of central tendency depends on a number of things including weather data has a normal distribution or not. The theoretical concept of a normal distribution is covered in more depth in another video, but simply put it is the idea that when data are gathered from interval or ratio level measures and plotted on a graph it will resemble a normal curve. The three measures of central tendency described in this video would all fall at the same midline point on a normal distribution curve. However, if data are not normally distributed certain measures may be better than others. The appropriateness of each measure is also influenced by the level of measurement used in the study. Throughout this video I will have examples of how to calculate the mean, median and mode on the screen. These examples will use the data I made up for a fake study about hours students spend watching online videos and reading for studying purposes. In statistics, mean is synonymous with the average. Whether it is true or not you could try remembering that the average girl can be mean when they want to be. Or, if you can remember what the other two are so you can figure this one out through the process of elimination. You may remember how to calculate averages from math class. To calculate the mean or average of a group of numbers, first add all the numbers. Then, divide by the number of values. The mean or average is the most common, best known and most widely used measure to describe the center of a frequency distribution. The mean is influenced by all data in a Study. For this reason, it works best for symmetrical distributions of data where there are no outliers or extremes. However, the larger the data set the smaller the influence of any extreme scores will be. The mean is the most commonly use measure because it is considered the most reliable measure of central tendency when making inferences from a sample population. However, it is only appropriate for interval and ratio level data. The Median is the value in the middle of a set of data. One way to remember that median means middle is to try associating it with the word medium. Median and medium sound sort of similar. They also both start with the letters MED. A medium pizza or a medium coffee is typically the size in the middle range at a store. If there is an even number of values simply divide the two numbers in the middle by 2. Unlike the Mean, the mode is not influenced by extreme values in a data set. Therefore, it is a good measure to use when distributions are not symmetrical. If a researcher is working with data that are not normally distributed and wants to know what the typical score is the median is likely the best measure to use. In this situation both the mean and median would likely be reported. The median is limited because it is not algebraically defined. Instead it is simply the point in the middle of the data set. While it is useful for ordinal, interval and ratio levels of measurement it cannot be used for nominal data. The Mode is the most frequent value, number or category in a set of data. One way to remember this definition is that Mode sounds like Most. Both mode and most start with the letters MO. The mode is the only measure of central tendency you can use for nominal data. While it can be used for all levels of measurement, it is considered unstable since fluctuations are likely between sample populations. Sometimes there is no mode. If all scores are different the mode does not exist. Sometimes there are multiple modes. If several values occur with equal frequency there are several modes. Unfortunately the mode can't be used for any further calculations in the study -- it can only help to describe the central tendency of the population.
Views: 136594 NurseKillam
Multivariate Statistical Anlaysis in Water Quality
 
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Multivariate statistical techniques are the application of statistics to simultaneous observations and can include the analysis of more than one outcome (dependent) variable. Good multivariate analysis starts with exploratory and graphical analyses to reveal potential relations in the data and to highlight potential outliers. First, this presentation will discuss how to extend univariate and bivariate methods for graphical analysis to multivariate data, as well as methods unique to multivariate data. Second, multivariate outlier detection will be presented. Third, there will be a brief discussion of multivariate statistical analysis methods, such as multiple regression, principal component analysis, and cluster analysis, including examples and suggestions as to when one might want to use these techniques.
Applied Machine Learning 2019 - Lecture 11 - Imbalanced data
 
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Undersampling, oversampling, SMOTE, Easy Ensembles Class website with slides and more materials: https://www.cs.columbia.edu/~amueller/comsw4995s19/schedule/
Views: 540 Andreas Mueller
Build A Complete Project In Machine Learning | Credit Card Fraud Detection | Eduonix
 
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Look what we have for you! Another complete project in Machine Learning! In today's tutorial, we will be building a Credit Card Fraud Detection System from scratch! It is going to be a very interesting project to learn! It is one of the 10 projects from our course 'Projects in Machine Learning' which is currently running on Kickstarter. For this project, we will be using the several methods of Anomaly detection with Probability Densities. We will be implementing the two major algorithms namely, 1. A local out wire factor to calculate anomaly scores. 2. Isolation forced algorithm. To get started we will first build a dataset of over 280,000 credit card transactions to work on! You can access the source code of this tutorial here: https://github.com/eduonix/creditcardML Learn It Up! Summer’s Hottest Learning Sale Is Here! Pick Any Sun-sational Course & Get Other Absolutely FREE! Link: http://bit.ly/summer-bogo-2019 Want to learn Machine learning in detail? Then try our course Machine Learning For Absolute Beginners at just $10. New Machine Learning Project Course for Beginners - http://bit.ly/2V8edMT You can even check FREE course on Predict Board Game Reviews with Machine Learning on http://bit.ly/2Wm2uKW Kickstarter Campaign on AI and ML E-Degree is Launched. Back this Campaign and Explore all the Courses with over 58 Hours of Learning. Link- http://bit.ly/aimledegree Thank you for watching! We’d love to know your thoughts in the comments section below. Also, don’t forget to hit the ‘like’ button and ‘subscribe’ to ‘Eduonix Learning Solutions’ for regular updates. https://goo.gl/BCmVLG Follow Eduonix on other social networks: ■ Facebook: http://bit.ly/2nL2p59 ■ Linkedin: http://bit.ly/2nKWhKa ■ Instagram: http://bit.ly/2nL8TRu | @eduonix ■ Twitter: http://bit.ly/2eKnxq8
Advanced Data Mining with Weka (2.2: Weka’s MOA package)
 
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Advanced Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 2: Weka’s MOA package http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/4vZhuc https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 3109 WekaMOOC
Data Mining with Weka (5.2: Pitfalls and pratfalls)
 
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Data Mining with Weka: online course from the University of Waikato Class 5 - Lesson 2: Pitfalls and pratfalls http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/5DW24X https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 12540 WekaMOOC
Fraud detection using machine learning & deep learning (Rubén Martínez) CyberCamp 2016 (English)
 
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Conference: Fraud detection using machine learning & deep learning The goal of this presentation is to go over several Machine Learning and Deep Learning techniques so as to detect fraud. Some of the algorithms and technologies that we intend to explain are, for instance, graphs, Neo4J, Apache Spark or Deep Learning libraries such as H2O. Rubén Martínez Sánchez: Computer Engineer by UPM and Master in Data Science. I have developed courses such as the title of Project Development with UML and Java also taught by UPM, CEH, Intel vPro, Cloudera Developer Training for Apache Hadoop, Cloudera Developer Training for Apache Spark, Introduction to Big Data with Apache Spark (Databricks) or Principles of Functional Programming in Scala among others. I have worked as a security auditor in StackOverflow as well as professor of the Postgraduate in Computer Security and Systems Hacking in the Polytechnic University School of Mataró or the online superior title of Computer Security and Hacking Systems Ethics of the Rey Juan Carlos University. I have also participated as a co-author of Ra-Ma publishing houses such as Hacking and Web Page Security (MundoHacker), Hacking and Internet Security Ed. 2011, etc. I am currently working on intelligent chatbots using Deep Learning. CyberCamp is the major cybersecurity event that INCIBE organises on a yearly basis for the purpose of identifying, appealing, managing and contributing to the creation of talent in cybersecurity that can be transferred to the private sector according to its demands. This initiative is one of the tasks that the Trust in the Digital Sphere Plan, included in the Spain’s Digital Agenda, has requested INCIBE to carry out. LEÓN - 2016 DECEMBER 1st, 2nd, 3rd and 4th.
Views: 5225 INCIBE
Data Mining with Weka (5.4: Summary)
 
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Data Mining with Weka: online course from the University of Waikato Class 5 - Lesson 4: Summary http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/5DW24X https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 11730 WekaMOOC
Adam Ashenfelter - Finding the Oddballs (Machine Learning Prague 2016)
 
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Finding the Oddballs www.mlprague.com Slides: http://www.slideshare.net/mlprague/adam-ashenfelter-finding-the-oddballs
Views: 1074 Jiří Materna
Orange Data Mining tool
 
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For more information visit orange.biolab.si
Views: 9015 Deeksha Acharya
Lecture 15.3 — Anomaly Detection Algorithm — [ Machine Learning | Andrew Ng | Stanford University ]
 
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. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
13. Classification
 
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MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016 View the complete course: http://ocw.mit.edu/6-0002F16 Instructor: John Guttag Prof. Guttag introduces supervised learning with nearest neighbor classification using feature scaling and decision trees. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 43733 MIT OpenCourseWare
Introduction to R Data Analysis: Data Cleaning
 
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Data Cleaning and Dates using lubridate, dplyr, and plyr
Views: 47943 John Muschelli
Analytics Case Study: Predicting Probability of Churn in a Telecom Firm| Data Science
 
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In this video you will learn how to predict Churn Probability by building a Logistic Regression Model. This is a data science case study for beginners as to how to build a statistical model in telecom industry and use it in production Contact [email protected] for Analytics study packs and mentor-ship/consulting Get all our videos & study packs. Check http://analyticuniversity.com/ ANalytics Study Pack : https://analyticsuniversity.com Analytics University on Twitter : https://twitter.com/AnalyticsUniver Analytics University on Facebook : https://www.facebook.com/AnalyticsUniversity Logistic Regression in R: https://goo.gl/S7DkRy Logistic Regression in SAS: https://goo.gl/S7DkRy Logistic Regression Theory: https://goo.gl/PbGv1h Time Series Theory : https://goo.gl/54vaDk Time ARIMA Model in R : https://goo.gl/UcPNWx Survival Model : https://goo.gl/nz5kgu Data Science Career : https://goo.gl/Ca9z6r Machine Learning : https://goo.gl/giqqmx Data Science Case Study : https://goo.gl/KzY5Iu Big Data & Hadoop & Spark: https://goo.gl/ZTmHOA
Views: 52477 Analytics University