Search results “Discriminant analysis in finance”
FRM: Altman's Z score for credit risk
Altman's Z is the most famous type of linear discriminant model: borrowers are classified into high or low default risk categories. It does not directly give a probability of default (PD), although we can map to the score to a credit rating and map the rating to a PD (so there is an indirect path from the score to the PD). Four drawbacks: 1. Not granular: only gives default/zone of ignorance/no default; 2. Constant factor weights (i.e., factor weights may be time varying); 3. Only considers five fundamental variables, ignores other variables; 4. No centralized database on defaulted business loans (not really an Altman's critique at all). For more financial risk videos, visit our website! http://www.bionicturtle.com
Views: 50287 Bionic Turtle
Machine Learning: LDA Explained. Simple Example of Linear Discriminant Analysis
In the third Machine Learning Tutorial, I explain what Linear Discriminant Analysis aims to achieve, how it does it and all the ideas, approaches of the LDA machine learning algorithm. I also show a simple Linear Discriminant Analysis example in python, going through it step by step. My visual LDA example will give you deep understanding into how LDA works and you can also see the Python code for Machine Learning! If you value my content, you can SUPPORT ME on: https://www.patreon.com/entiversal. Get amazing REWARDS(my code, design & much more) & help me create more! SUBSCRIBE FOR MY PODCASTS on your favorite platform! https://anchor.fm/entiversal http://www.stitcher.com/s?fid=179162&... Study Machine Learning. The Best Artificial Intelligence and ML Books on Amazon: Machine Learning for Absolute Beginners: US - http://amzn.to/2IADOYF UK - http://amzn.to/2G2pYjs Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems: US - http://amzn.to/2FNRL7V UK - http://amzn.to/2IzZjZN Learning From Data: US - https://amzn.to/2NFy8Os UK - http://amzn.to/2IBpXBp WATCH: How AI Works? Machine Learning Basics Explained! Simple Visual Example!: https://youtu.be/Xst1ILDvrjw Machine Learning Tutorial: Simple Example of Linear Regression & Neural Networks Basics: https://youtu.be/_R13EgM5sC8 Artificial Intelligence and Machine Learning are such a buzzwords, but what is the difference between them? How Artificial Intelligence works? What is Machine Learning and what does it do? What are the ideas behind Machine Learning Neural Networks and how Deep Learning works? I am sharing with you answers on all of these questions on beginner level with simple to understand explanations and examples, talking about what are neurons in machine learning, what do they actually do? We will start with simple Machine Learning algorithms, showing you visually short examples of Machine Learning Python scripts, explaining what happens on every step, different trade-offs and interesting facts. Come with me into the world of Machine Learning and Artificial Intelligence, it is exciting! Linear Discriminant Analysis is a linear transformation technique, which aims to project the feature space (dataset) into a lower dimensional subspace with better separation between classes and minimal loss of information. LDA is a supervised Machine Learning algorithm, which finds the best directions (vectors) in the data-space, on which when the data-points are projected, best separation is observed. We can also observe how much information each vector (weight direction) is containing, empowering us to get rid of redundant (unmeaningful) dimensions. Dimensional reduction is crucial, used for pre-processing of data to reduce further computation. LDA approach is to maximize the Fisher's Ratio, which results in the best trade-off between maximum distances between classes' means and minimal classes' variance. The optimal direction vectors are found by taking the partial derivative of the Fisher's Score to W (the optimal weight vector). This leads to an eigenvalue problem, where the eigenvectors are the wanted directions and eigenvalues are the measure of how much information each vector contains. By simply dot multiplying the input data and the optimal weight vector we apply LDA to the data and arrive at a solution! GET 2 FREE Audiobooks With A 30 Day Free Audible Trial On Amazon: US: https://amzn.to/2yiYdOH UK: https://amzn.to/2QQADzG START YOUR OWN WEBSITE 50% OFF: https://www.bluehost.com/track/entiversal/ Our Mission: Inspire Creativity, Give Knowledge, Quality Entertainment, Drive Success. Are you UNIVERSAL? Subscribe for more: https://www.youtube.com/c/Entiversal?sub_confirmation=1 FOLLOW US: PATREON: https://www.patreon.com/entiversal FACEBOOK: https://www.facebook.com/Entiversal.M... INSTAGRAM: https://www.instagram.com/entiversal_... PODCAST: https://anchor.fm/entiversal https://soundcloud.com/entiversal http://www.stitcher.com/s?fid=179162&... Website: Entiversal.com Entertain and build Yourself with the good music, motivational podcasts, educational videos and successful lifestyle. Financial tips, the Fashion Insights, Health Advises and Cutting Edge Technology. Our values are: Virtue, Creativity, Wisdom. If you found value in this video, make sure you Subscribe, like it and share it with your friends and family. Look around Entiversal - I have a lot of educational, motivational, inspirational, entrepreneurship and entertainment videos - and it is all for you! Stay Entiversal - on the path to wisdom! I explain simply what Machine Learning is and how simple Artificial Intelligence systems work. This is part of my series on Machine Learning Tutorials, where we will explore the world of A.I. together and learn how to create A.I.! I hope you found it interesting, stay tuned for more!
Views: 5046 Entiversal
Linear Discriminant Analysis in R | Multi Class Classification | Data Science
In this video you will learn how to perform linear discriminant analysis in R. As opposed to Logistic Regression analysis, Linear discriminant analysis (LDA) performs well when there is multi class classification problem at hand. It assumes linear relationship between target and explanatory variables. For quadratic relationships you can used quadratic Discriminant analysis. It can well be used along with other classification algorithms like support vector machine, random forest, decision tree etc. ANalytics Study Pack : http://analyticuniversity.com/ Contact us for training/study packs [email protected] 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: 5054 Analytics University
Altman Z score
This video discusses the Altman Z-score, a useful metric for predicting a firm's risk of bankruptcy. The formula for computing the Altman Z-score is presented and explained, along with an explanation of how to interpret the resulting score. Edspira is your source for business and financial education. To view the entire video library for free, visit http://www.Edspira.com To like us on Facebook, visit https://www.facebook.com/Edspira Edspira is the creation of Michael McLaughlin, who went from teenage homelessness to a PhD. The goal of Michael's life is to increase access to education so all people can achieve their dreams. To learn more about Michael's story, visit http://www.MichaelMcLaughlin.com To follow Michael on Facebook, visit https://facebook.com/Prof.Michael.McLaughlin To follow Michael on Twitter, visit https://twitter.com/Prof_McLaughlin
Views: 16035 Edspira
4.4.4 Quadratic Discriminant Analysis (Part 2)
Book: Introduction to Statistical Learning - with Applications in R http://www-bcf.usc.edu/~gareth/ISL/
Views: 1110 MachineLearningGod
What is BANKRUPTCY PREDICTION? What does BANKRUPTCY PREDICTION mean? BANKRUPTCY PREDICTION meaning - BANKRUPTCY PREDICTION definition - BANKRUPTCY PREDICTION explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Bankruptcy prediction is the art of predicting bankruptcy and various measures of financial distress of public firms. It is a vast area of finance and accounting research. The importance of the area is due in part to the relevance for creditors and investors in evaluating the likelihood that a firm may go bankrupt. The quantity of research is also a function of the availability of data: for public firms which went bankrupt or did not, numerous accounting ratios that might indicate danger can be calculated, and numerous other potential explanatory variables are also available. Consequently, the area is well-suited for testing of increasingly sophisticated, data-intensive forecasting approaches. The history of bankruptcy prediction includes application of numerous statistical tools which gradually became available, and involves deepening appreciation of various pitfalls in early analyses. Interestingly, research is still published that suffers pitfalls that have been understood for many years. Bankruptcy prediction has been a subject of formal analysis since at least 1932, when FitzPatrick published a study of 20 pairs of firms, one failed and one surviving, matched by date, size and industry, in The Certified Public Accountant. He did not perform statistical analysis as is now common, but he thoughtfully interpreted the ratios and trends in the ratios. His interpretation was effectively a complex, multiple variable analysis. In 1967, William Beaver applied t-tests to evaluate the importance of individual accounting ratios within a similar pair-matched sample. In 1968, in the first formal multiple variable analysis, Edward I. Altman applied multiple discriminant analysis within a pair-matched sample. One of the most prominent early models of bankruptcy prediction is the Z-Score Financial Analysis Tool, which is still applied today. In 1980, James Ohlson applied logit regression in a much larger sample that did not involve pair-matching. Survival methods are now applied. Option valuation approaches involving stock price variability have been developed. Under structural models, a default event is deemed to occur for a firm when its assets reach a sufficiently low level compared to its liabilities. Neural network models and other sophisticated models have been tested on bankruptcy prediction. Modern methods applied by business information companies surpass the annual accounts content and also consider current events like age, judgements, bad press, payment incidents and payment experiences from creditors. The latest research within the field of Bankruptcy and Insolvency Prediction compares various differing approaches, modelling techniques, and individual models to ascertain whether any one technique is superior to its counterparts. Jackson and Wood (2013) provides an excellent discussion of the literature to date, including an empirical evaluation of 15 popular models from the existing literature. These models range from the univariate models of Beaver through the multidimensional models of Altman and Ohlson, and continuing to more recent techniques which include option valuation approaches. They find that models based on market data - such as an option valuation approach - outperform those earlier models which rely heavily on accounting numbers. Zhang, Wang, and Ji (2013) proposed a novel rule-based system to solve bankruptcy prediction problem. The whole procedure consists of the following four stages: first, sequential forward selection was used to extract the most important features; second, a rule-based model was chosen to fit the given dataset since it can present physical meaning; third, a genetic ant colony algorithm (GACA) was introduced; the fitness scaling strategy and the chaotic operator were incorporated with GACA, forming a new algorithm—fitness-scaling chaotic GACA (FSCGACA), which was used to seek the optimal parameters of the rule-based model; and finally, the stratified K-fold cross-validation technique was used to enhance the generalization of the model.
Views: 1128 The Audiopedia
Quadratic Discriminant Analysis | Linear Discrimnant Analysis | Data Science
In this video you will learn about the quadratic discriminant analysis and how to perform QDA in R. In the previous video you had learnt about the linear discriminant analysis which assumes linear between target and explanatory variables where as QDA assumes quadratic relationship ANalytics Study Pack : http://analyticuniversity.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: 3518 Analytics University
4.4.2 Linear Discriminant Analysis with One Predictor (Part 2)
Book: Introduction to Statistical Learning - with Applications in R http://www-bcf.usc.edu/~gareth/ISL/
Views: 334 MachineLearningGod
Will the price go up or down? Predicting financial data.
This webinar examines several procedures in Statgraphics that are useful for analyzing stock market data. It demonstrates the use of trading bands within the Open-High-Low-Close Candlestick Plot, the fitting of time series forecast models, and the use of discriminant analysis to pick winning stocks.
Views: 514 Statgraphics
Behaviopral Finance (BeFi) - Theory of Regret
Explains the Behavioral Finance concept of Theory of Regret as it pertains to decision choice. For more on Behavioral Finance visit www.bostonrt.com.
1 Factor Analysis - An Introduction
The factor analysis video series is availablefor FREE as an iTune book for download on the iPad. The ISBN is 978-1-62847-041-3. The title is "Factor Analysis". Waller and Lumadue are the authors. The iTune text provides accompanying narrative and the SPSS readouts used in the video series. The book can be accessed at: https://itunes.apple.com/us/book/factor-analysis/id656956844?ls=1 This video gives a brief introduction to factor analysis. Emphasis is placed on a visual representation of the process of data reduction. Groundwork is laid for the development of the other aspects of factor analysis.
Views: 87054 Lee Rusty Waller
9.4 The nature of quantitative analysis
Coded data is input into a computer manually or scanned. Tables are then produced. Cell or rim-weighting is used to ensure that the sample is balanced. Grossing means that figures are multiplied up to the population levels. Bivariate analysis takes two variables at a time and inspects the pattern between them and correlation is the measure of the nature and the strength of association between two variables. Multivariate analysis takes three or more variables at a time and inspects the pattern between them. Regression is used to analyse associative relationships. Other techniques of relevance include analysis of variance (ANOVA), factor analysis, discriminant analysis, cluster analysis, CHAID and data fusion. http://www.oxfordtextbooks.co.uk/orc/bradley2e
Views: 6865 MarketResearchVideos
Logistic Regression Model for Stock Price Movement | Data Science in Finance
In this video you will learn how to build a logistic regression model that would predict the movement of stock price. Other models like decisions tree, SVM, random forest can be used for the same purpose. The R code is given below. ANalytics Study Pack : https://analyticuniversity.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 R code :
Views: 3765 Analytics University
StatQuest: PCA - Practical Tips
This is a follow-up video for StatQuest: Principal Component Analysis (PCA), Step-by-Step https://youtu.be/FgakZw6K1QQ In it, I give practical advice about the need to scale your data, the need to center your data, and how many principal components you should expect to get. If you are interested in doing PCA in R see: https://youtu.be/0Jp4gsfOLMs For a complete index of all the StatQuest videos, check out: https://statquest.org/video-index/ If you'd like to support StatQuest, please consider a StatQuest t-shirt or sweatshirt... https://teespring.com/stores/statquest ...or buying one or two of my songs (or go large and get a whole album!) https://joshuastarmer.bandcamp.com/
Principal Components Analysis - Georgia Tech - Machine Learning
Watch on Udacity: https://www.udacity.com/course/viewer#!/c-ud262/l-649069103/m-661438544 Check out the full Advanced Operating Systems course for free at: https://www.udacity.com/course/ud262 Georgia Tech online Master's program: https://www.udacity.com/georgia-tech
Views: 281455 Udacity
How To Predict Future Bankruptcy / Failure Of any Company ( FREE Excel Model)
Find how you you can check failure of a company or if a company may go bankrupt is future. FREE Excel sheet also attached. Altman Z Score is the one of the best process to check whether the company may go bankrupt of not?
Multiple Discriminant Analysis
An Easy Overview Of Multiple Discriminant Analysis
Views: 3689 Christopher Hunt
Decision Analysis 1
Session 5, Team HW
Views: 1279 Robert Emrich
Charla #1: Machine Learning for Financial Statement Fraud
[NOTA: Hay problemas menores de sonido en la grabación pero son sólo al comienzo y entre 1:30 y 3:30] Título: Machine Learning for Financial Statement Fraud Fecha: 14:30-15:00, viernes 15/1/2016 Expositor: María Jofré (estudiante de doctorado, University of Sydney, Australia.) Lugar: Sala John von Neumann, CMM. Abstract: Financial statement fraud (FSF) is a global concern representing a significant threat to financial system stability. In recent years, data-informed quantitative models have been developed to automate and reduce the manual auditing processes. Although the existing techniques have improved the detection rate of FSF, these are very limited and can be improved in terms of data, accuracy and efficiency, leading to more targeted and effective examinations. The main objective of this study is to develop four machine learning methods - Discriminant Analysis, Logistic Regression, Decision Trees and AdaBoost - in order to differentiate between fraud and non-fraud cases by assessing the likelihood of FSF using publicly available financial statement information.
Views: 1078 Games-UChile
David Sullivan, LDA (Inspiring Entrepreneurs - Desperately Seeking Finance)
David Sullivan is financial relationship manager at the London Development Agency and a highly experienced banker. During his career, he has looked after a variety of customers including small and medium-sized enterprises. David joined Business Link for London in 2003 as a member of its Access to Finance programme, which transferred to the London Development Agency in October 2006. Business & IP Centre is a gateway to help start, run and grow your business. Visit: http://www.bl.uk/bipc/
Views: 797 BIPCTV
Cluster analysis
Currell: Scientific Data Analysis. Minitab and SPSS analysis for Fig 9.2 http://ukcatalogue.oup.com/product/9780198712541.do © Oxford University Press
Ratio analysis- theory 3
Project Name: Developing ICT based pedagogical practices for management accounting Project Investigator: Dr. Manoj Shah Module Name: Ratio analysis- theory 3
Views: 71 Vidya-mitra
Evaluate Health Risk Model
This video is about the risk prediction model in medical use.
Views: 317 Chunxue LI
FRM: Discrimination test
Download FRM Question Bank: http://www.edupristine.com/ca/courses/frm-program/ Discrimination testing is a technique employed in sensory analysis to determine whether there is a detectable difference among two or more products. About EduPristine: Trusted by Fortune 500 Companies and 10,000 Students from 40+ countries across the globe, EduPristine is one of the leading Training provider for Finance Certifications like CFA, PRM, FRM, Financial Modeling etc. EduPristine strives to be the trainer of choice for anybody looking for Finance Training Program across the world. Subscribe to our YouTube Channel: http://www.youtube.com/subscription_center?add_user=edupristine Visit our webpage: http://www.edupristine.com/ca
Views: 160 EduPristine
BADM 1.1: Data Mining Applications
This video was created by Professor Galit Shmueli and has been used as part of blended and online courses on Business Analytics using Data Mining. It is part of a series of 37 videos, all of which are available on YouTube. For more information: www.dataminingbook.com twitter.com/gshmueli facebook.com/dataminingbook Here is the complete list of the videos: • Welcome to Business Analytics Using Data Mining (BADM) • BADM 1.1: Data Mining Applications • BADM 1.2: Data Mining in a Nutshell • BADM 1.3: The Holdout Set • BADM 2.1: Data Visualization • BADM 2.2: Data Preparation • BADM 3.1: PCA Part 1 • BADM 3.2: PCA Part 2 • BADM 3.3: Dimension Reduction Approaches • BADM 4.1: Linear Regression for Descriptive Modeling Part 1 • BADM 4.2 Linear Regression for Descriptive Modeling Part 2 • BADM 4.3 Linear Regression for Prediction Part 1 • BADM 4.4 Linear Regression for Prediction Part 2 • BADM 5.1 Clustering Examples • BADM 5.2 Hierarchical Clustering Part 1 • BADM 5.3 Hierarchical Clustering Part 2 • BADM 5.4 K-Means Clustering • BADM 6.1 Classification Goals • BADM 6.2 Classification Performance Part 1: The Naive Rule • BADM 6.3 Classification Performance Part 2 • BADM 6.4 Classification Performance Part 3 • BADM 7.1 K-Nearest Neighbors • BADM 7.2 Naive Bayes • BADM 8.1 Classification and Regression Trees Part 1 • BADM 8.2 Classification and Regression Trees Part 2 • BADM 8.3 Classification and Regression Trees Part 3 • BADM 9.1 Logistic Regression for Profiling • BADM 9.2 Logistic Regression for Classification • BADM 10 Multi-Class Classification • BADM 11 Ensembles • BADM 12.1 Association Rules Part 1 • BADM 12.2 Association Rules Part 2 • Neural Networks: Part I • Neural Nets: Part II • Discriminant Analysis (Part 1) • Discriminant Analysis: Statistical Distance (Part 2) • Discriminant Analysis: Misclassification costs and over-sampling (Part 3)
Views: 3125 Galit Shmueli
DataEngConf: Apache Spark in Financial Modeling at BlackRock
WANT TO EXPERIENCE A TALK LIKE THIS LIVE? Barcelona: https://www.datacouncil.ai/barcelona New York City: https://www.datacouncil.ai/new-york-city San Francisco: https://www.datacouncil.ai/san-francisco Singapore: https://www.datacouncil.ai/singapore Andrew Rothstein (Managing Director, BlackRock) will talk about how the Financial Modeling Group at BlackRock leverages Apache Spark to explore and better understand the financial and economic behaviors of debtors through data. Several use-cases will highlight how we apply Spark and D3 to visualize a large loan-level mortgage dataset; extract distributions and cluster boundaries with K-Means; and ultimately draw meaningful insights into the composition and corresponding discriminant attributes of borrower groups. We will showcase software components built and employed to streamline the running of big data Spark analyses. FOLLOW DATA COUNCIL: Twitter: https://twitter.com/DataCouncilAI LinkedIn: https://www.linkedin.com/company/datacouncil-ai Facebook: https://www.facebook.com/datacouncilai
Views: 945 Data Council
Investment and Rate of Interest
Subject:Economics Paper:Basic macroeconomics
Views: 138 Vidya-mitra
Partial Least Squares regression
Partial Least Squares regression (PLS) is a quick, efficient and optimal for a criterion method based on covariance. It is recommended in cases where the number of variables is high, and where it is likely that the explanatory variables are correlated. Partial Least Squares regression principle: The idea of PLS regression is to create, starting from a table with n observations described by p variables, a set of h components with h PLS1 and PLS2 algorithms: Some programs differentiate PLS1 from PLS2. PLS1 corresponds to the case where there is only one dependent variable. PLS2 corresponds to the case where there are several dependent variables. The algorithms used by XLSTAT are such that the PLS1 is only a particular case of PLS2. More info at http://www.xlstat.com/en/features/partial-least-square-regression-pls.htm
Views: 56860 XLSTAT
Principal Components Analysis - SPSS (part 2)
I demonstrate how to perform a principal components analysis based on some real data that correspond to the percentage discount/premium associated with nine listed investment companies. Based on the results of the PCA, the listed investment companies could be segmented into two largely orthogonal components.
Views: 106835 how2stats
Principal Components Analysis - SPSS (part 4)
I demonstrate how to perform a principal components analysis based on some real data that correspond to the percentage discount/premium associated with nine listed investment companies. Based on the results of the PCA, the listed investment companies could be segmented into two largely orthogonal components.
Views: 70359 how2stats
R tutorial: Missing data and coarse classification
Learn more about credit risk modeling in R: https://www.datacamp.com/courses/introduction-to-credit-risk-modeling-in-r Now, we have removed the observation containing a bivariate outlier for age and annual income from the data set. What we did not discuss before is that there are missing inputs (or NA's, which stand for not available) for two variables: employment length and interest rate. In this video we will demonstrate some methods for handling missing data on the employment length variable. You'll practice this newly gained knowledge yourself on the variable interest rate. First, you want to know how many inputs are missing, as this will affect what you do with them. A simple way of finding out is with the function summary(). If you do this for employment length, you will see that there are 809 NA's. There are generally three ways to treat missing inputs: delete them, replace them, or keep them. We will illustrate these methods on employment length. When deleting, you can either delete the observations where missing inputs are detected, or delete an entire variable. Typically, you would only want to delete observations if there is just a small number of missing inputs, and would only consider deleting an entire variable when many cases are missing. Using this construction with which() and is.na(), the rows with missing inputs are deleted in the new data set loan_data_no_NA. To delete the entire variable employment length, you simply set the employment length variable in the loan data equal to NULL. Here, we save the result to a copy of the data set called loan_data_delete_employ. Making a copy of your original data before deleting things can be a good way to avoid losing information, but may be costly if working with very large data sets. Second, when replacing a variable, common practice is to replace missing values with the median of the values that are actually observed. This is called median imputation. Last, you can keep the missing values, since in some cases, the fact that a value is missing is important information. Unfortunately, keeping the NAs as such is not always possible, as some methods will automatically delete rows with NAs because they cannot deal with them. So how can we keep NAs? A popular solution is coarse classification. Using this method, you basically put a continuous variable into so-called bins. Let's start off making a new variable emp_cat, which will be the variable replacing emp_length. The employment length in our data set ranges from 0 to 62 years. We will put employment length into bins of roughly 15 years, with groups 0 to 15, 15 to 30, 30 to 45, 45 plus, and a "missing” group, representing the NAs. Let's see how this changes our data. Let's look at the plot of this new factor variable. It appears that the bin '0-15' contains a very high proportion of the cases, so it might seem more reasonable to look at bins of different ranges but with similar frequencies, as shown here. You can get these results by trial and error for different bin ranges, or by using quantile functions to know exactly where the breaks should be to get more balanced bins. Before trying all of this in R yourself, let me finish the video with a couple of remarks. First, all the methods for missing data handling can also be applied to outliers. If you think an outlier is wrong, you can treat it as NA and use any of the methods we have discussed in this chapter. Second, you may have noticed I only talked about missingness for continuous variables in this chapter. What about factor variables? Here's the basic approach. For categorical variables, deletion works in the exact same way as for continuous variables, deleting either observations or entire variables. When we wish to replace a missing factor variable, this is done by assigning it to the modal class, which is the class with the highest frequency. Keeping NAs for a categorical variable is done by including a missing category. Now, let's try some of these methods yourself!
Views: 5235 DataCamp
R Text Analytics For Beginners (using the Syuzhet package)
Text file resource used in video: https://drive.google.com/open?id=0B67hcgV97X0mWVN6UmdyQ0M0WE0 This video covers text analytics in R using the syuzhet package. Polarity, Sentiment, and word cloud. Leave comments for areas where I didn't explain it very well or any questions you have.
Views: 2582 James Dayhuff
Lean Finance_Altman Z Score
Bankruptcy Prediction Model, Phil Greenwood, UWisc-Madison
Views: 1309 Philip Greenwood
Introduction to Financial Series (Example 1 of 2: Rising salary)
More resources available at www.misterwootube.com
Views: 4341 Eddie Woo
Quantopian Lecture Series: p-Hacking and Multiple Comparisons Bias
Multiple comparisons bias is a big problem in quantitative analysis. Effectively it's just the notion that the more tests you run, the more likely you are to get false positives (things that look like they confirm your hypothesis, but are really just random chance). If you don't correct for this at some point you're very likely to accept hypotheses that aren't based on any real relationships. p-Hacking is just the abuse of this phenomenon, in which someone runs tons of tests until they find one specific situation in which their tests pass. It can be intentional or inadvertent, but it happens. This lecture will introduce you to the concept, show some experimental examples, and talk about correcting for it. More lectures can be found here: https://www.quantopian.com/lectures#p-Hacking-and-Multiple-Comparisons-Bias To learn more about Quantopian, visit us at: https://www.quantopian.com. ------- Quantopian provides this presentation to help people write trading algorithms - it is not intended to provide investment advice. More specifically, the material is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory or other services by Quantopian. In addition, the content neither constitutes investment advice nor offers any opinion with respect to the suitability of any security or any specific investment. Quantopian makes no guarantees as to accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.
Views: 1124 Quantopian
Manual ROI Analysis with Excel using data from Mongoose Metrics and Salesforce
Although it's easier with automated CRM integration, it is possible to use Excel to calculate the return on investment (ROI) that you are getting on advertisements that result in phone calls.
Views: 3402 MongooseMetricsVideo
SAP HANA Academy - PAL: 124. Classification - LDA Fit [2.0 SPS 00]
In this video tutorial, Philip Mugglestone introduces the new Linear Discriminant Analysis (LDA) algorithm for classification available with HANA 2.0 SPS 00. In this first LDA tutorial Philip covers how to use Linear Discriminant Analysis to fit a model. To access the code snippets used in the video series please visit https://github.com/saphanaacademy/PAL A video by the SAP HANA Academy.
Views: 361 SAP HANA Academy
Principal Components Analysis - SPSS (part 3)
I demonstrate how to perform a principal components analysis based on some real data that correspond to the percentage discount/premium associated with nine listed investment companies. Based on the results of the PCA, the listed investment companies could be segmented into two largely orthogonal components.
Views: 81720 how2stats
Chapter 4 - Data tables and sensitivity analysis
Learn how to conduct a sensitivity analysis using data tables.
Views: 845 Jaime Lancaster
LDA Film
Views: 1880 L&T -LDA
Which? Financial distress mapping tool.
Guy Weir (Which?, Senior Statistician) discusses an online, interactive financial distress mapping tool. Talk was at The Graphical Web (https://www.graphicalweb.org/2014/) 2014 conference at The University of Winchester in August 2014, organised by The Office for National Statistics. Video by John Wilson (@snoop2003) of Winchester University Journalism School.
Views: 102 John P Wilson
PCA: example - Steps 1 & 2
Steps 1 & 2 of simplified explanation of the mathematics behind how PCA reduce dimensions.
Views: 25114 quekovich
Altman Z-Score for non-manufacturer industrials
In order to calculate Altman Z-Score for non-manufacturer industrials and emerging market follow the link: http://www.financialratioss.com/other-financial-ratios-1/altman-z-score-for-non-manufacturer-industrials-and-emerging-market More info on other financial ratios can be found here: http://www.financialratioss.com
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Deep Learning Decal Fall 2017 Lecture 5: Linear Factor Models
The fifth lecture of the deep learning decal. Check the website for updates: https://ml.berkeley.edu/decals/DLD and the repository for slides: https://github.com/mlberkeley/Deep-Learning-Decal-Fall-2017
UConn Math 5800 Financial Data Science House Price Advanced Regression Techniques
We used some feature selection and feature engineering techniques in our analysis. Finally, we implemented our models on a Spark cluster.
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