Face Recognition is one of the revolution technology, which is based on various machine learning and deep learning algorithms, which is mostly used for bio metrics, but it is also used to detect criminals in traffic signals, identify the and track the lost persons, etc., This project is written in python, where a GUI appears with a button, which let us to open the face recognition program, that captures the video frames from the webcam of a laptop or a pc, and then analyses it
Missing data is a common challenge in data analysis. Methods for handling missing data aim to minimize bias, maximize use of available information, and accurately estimate uncertainty. There are two main categories of methods: deletion methods which remove cases or variables with missing data, and imputation methods which substitute values for the missing data. Multiple imputation, which generates multiple plausible values for the missing data, is now considered the best practice for handling missing data.
Imputation techniques for missing data in clinical trialsNitin George
Missing data are unavoidable in clinical and epidemiological researches. Missing data leads to bias and loss of information in research analysis. Usually we are not aware of missing data techniques because we are depending on some software’s. The objective of this seminar is to introduce different missing data mechanisms and imputation techniques for missing data with the help of examples.
This document discusses potential sources of missing data in meta-analyses, including studies not being found, outcomes not being fully reported, missing standard deviations or other information needed for the meta-analysis, and missing participants. It also covers concepts related to missing data like whether it is missing completely at random, missing at random, or informatively missing. Strategies for dealing with missing data include simple or multiple imputation as well as sensitivity analyses. Specific examples discussed include imputing missing standard deviations or correlation coefficients.
Missing data handling is typically done in an ad-hoc way. Without understanding the repurcussions of a missing data handling technique, approaches that only let you get to the "next step" in your analytics pipeline leads to terrible outputs, conclusions that aren't robust and biased estimates. Handling missing data in data sets requires a structured approach. In this workshop, we will cover the key tenets of handling missing data in a structured way
EDA and Preprocessing in Tabular and Text data .pptxBrajkishore23
This presentation will help to understand how the EDA and preprocessing are performed on tabular and text data, and some of the feature engineering steps
Analysis Report Presentation 041515 - Team 4Zijian Huang
The document analyzes different statistical models to best predict heart disease using a dataset of 294 patients and 76 variables, finding that logistic regression had the best performance with an accuracy of 84% and reasonably high sensitivity and low false positive rate, and recommending implementing a logistic regression model in a consumer-facing heart disease prediction app.
This document discusses developing appropriate methods for handling missing data in health economic evaluations. It covers:
- Missing data is common in cost-effectiveness analyses due to patient loss to follow-up and incomplete questionnaires, but is often not adequately addressed.
- Multiple imputation is recommended over other methods as it recognizes the uncertainty in imputed values and can handle missing data in both outcomes and covariates. It provides a flexible framework for sensitivity analyses.
- Sensitivity analyses are important to assess how inferences may change under different assumptions about the missing data mechanism, such as departures from the missing at random assumption. Selection models and pattern-mixture models are two approaches discussed.
COMPARISION OF PERCENTAGE ERROR BY USING IMPUTATION METHOD ON MID TERM EXAMIN...ijiert bestjournal
The issue of incomplete data exists across the enti re field of data mining. In this paper,Mean Imputation,Median Imputation and Standard Dev iation Imputation are used to deal with challenges of incomplete data on classifi cation problems. By using different imputation methods converts incomplete dataset in t o the complete dataset. On complete dataset by applying the suitable Imputatio n Method and comparing the percentage error of Imputation Method and comparing the result
Missing data is a common challenge in data analysis. Methods for handling missing data aim to minimize bias, maximize use of available information, and accurately estimate uncertainty. There are two main categories of methods: deletion methods which remove cases or variables with missing data, and imputation methods which substitute values for the missing data. Multiple imputation, which generates multiple plausible values for the missing data, is now considered the best practice for handling missing data.
Imputation techniques for missing data in clinical trialsNitin George
Missing data are unavoidable in clinical and epidemiological researches. Missing data leads to bias and loss of information in research analysis. Usually we are not aware of missing data techniques because we are depending on some software’s. The objective of this seminar is to introduce different missing data mechanisms and imputation techniques for missing data with the help of examples.
This document discusses potential sources of missing data in meta-analyses, including studies not being found, outcomes not being fully reported, missing standard deviations or other information needed for the meta-analysis, and missing participants. It also covers concepts related to missing data like whether it is missing completely at random, missing at random, or informatively missing. Strategies for dealing with missing data include simple or multiple imputation as well as sensitivity analyses. Specific examples discussed include imputing missing standard deviations or correlation coefficients.
Missing data handling is typically done in an ad-hoc way. Without understanding the repurcussions of a missing data handling technique, approaches that only let you get to the "next step" in your analytics pipeline leads to terrible outputs, conclusions that aren't robust and biased estimates. Handling missing data in data sets requires a structured approach. In this workshop, we will cover the key tenets of handling missing data in a structured way
EDA and Preprocessing in Tabular and Text data .pptxBrajkishore23
This presentation will help to understand how the EDA and preprocessing are performed on tabular and text data, and some of the feature engineering steps
Analysis Report Presentation 041515 - Team 4Zijian Huang
The document analyzes different statistical models to best predict heart disease using a dataset of 294 patients and 76 variables, finding that logistic regression had the best performance with an accuracy of 84% and reasonably high sensitivity and low false positive rate, and recommending implementing a logistic regression model in a consumer-facing heart disease prediction app.
This document discusses developing appropriate methods for handling missing data in health economic evaluations. It covers:
- Missing data is common in cost-effectiveness analyses due to patient loss to follow-up and incomplete questionnaires, but is often not adequately addressed.
- Multiple imputation is recommended over other methods as it recognizes the uncertainty in imputed values and can handle missing data in both outcomes and covariates. It provides a flexible framework for sensitivity analyses.
- Sensitivity analyses are important to assess how inferences may change under different assumptions about the missing data mechanism, such as departures from the missing at random assumption. Selection models and pattern-mixture models are two approaches discussed.
COMPARISION OF PERCENTAGE ERROR BY USING IMPUTATION METHOD ON MID TERM EXAMIN...ijiert bestjournal
The issue of incomplete data exists across the enti re field of data mining. In this paper,Mean Imputation,Median Imputation and Standard Dev iation Imputation are used to deal with challenges of incomplete data on classifi cation problems. By using different imputation methods converts incomplete dataset in t o the complete dataset. On complete dataset by applying the suitable Imputatio n Method and comparing the percentage error of Imputation Method and comparing the result
Missing data occurs when no data value is stored for a variable in an observation, usually due to manual errors or incorrect measurements. There are three types of missing data: missing completely at random, missing at random, and missing not at random. Several methods can be used to deal with missing data, including reducing the dataset, treating missing values as a special value, replacing with the mean, replacing with the most common value, and using the closest fit from similar observations. Proper handling of missing data is important to avoid bias and distortions in analyzing the dataset.
Missing data occurs when no data value is stored for a variable in an observation, usually due to manual errors or incorrect measurements. There are three types of missing data: missing completely at random, missing at random, and missing not at random. Several methods can be used to deal with missing data, including reducing the dataset, treating missing values as a special value, replacing with the mean, replacing with the most common value, and using the closest fit to impute missing values. Proper handling of missing data is important to avoid bias and distortions in analyzing the data.
Statistical analysis & errors (lecture 3)Farhad Ashraf
This document discusses statistical analysis and errors in measurement. It defines statistical analysis as dealing with numerical data using probability theory. Measurement errors can be divided into determinate (systematic) errors and indeterminate (random) errors. Determinate errors can be avoided or corrected, while indeterminate errors cannot be determined precisely but their probability can be estimated using statistical distributions like the Gaussian curve. The document also discusses concepts like significant figures, rounding off data, measures of central tendency (mean, median, mode), standard deviation, tests like F-test and T-test, quality control/quality assurance, good laboratory practices, validation of analytical methods and their parameters.
This document discusses feature engineering, which is the process of transforming raw data into features that better represent the underlying problem for predictive models. It covers feature engineering categories like feature selection, feature transformation, and feature extraction. Specific techniques covered include imputation, handling outliers, binning, log transforms, scaling, and feature subset selection methods like filter, wrapper, and embedded methods. The goal of feature engineering is to improve machine learning model performance by preparing proper input data compatible with algorithm requirements.
This presentation deals with the formal presentation of anomaly detection and outlier analysis and types of anomalies and outliers. Different approaches to tackel anomaly detection problems.
This document discusses issues related to using data and models to make reliable predictions in the physical sciences. It notes that while modern simulations and data collection enable unprecedented detail, making reliable predictions remains challenging. Several key issues are discussed, including model validation, the problem of prediction, and properly accounting for data and uncertainties. The document emphasizes that predictions require extrapolating available information, so models must be based on reliable theory, with uncertainties in embedded models represented probabilistically. An example involving predicting the maximum velocity of a spring-mass-damper system demonstrates representing model uncertainty and checking predictions against additional validation data. The document concludes that making reliable predictions requires models informed by constraints from both data and theory, as well as understanding the limitations of any data
This document discusses several methods for preparing data before analysis, including handling outliers, missing data, duplicated data, and heterogeneous data formats. For outliers, it describes techniques like trimming, winsorizing, and changing regression models. For missing data, it covers identifying patterns, assessing causes, and handling techniques like listwise deletion, imputation, and multiple imputations. It also addresses detecting and removing duplicate records based on field similarities, as well as standardizing heterogeneous data formats.
Simple math for anomaly detection toufic boubez - metafor software - monito...tboubez
This is my presentation at Monitorama PDX in Portland on May 5, 2014
Simple math to get some signal out of your noisy sea of data
You’ve instrumented your system and application to the hilt. You can now “measure all the things”. Your team has set up thousands of metrics collecting millions of data points a day. Now what?
Most IT ops teams only keep an eye on a small fraction of the metrics they collect because analyzing this mountain of data and extracting signal from the noise is not easy. The choice of what analytic method to use ranges from simple statistical analysis to sophisticated machine learning techniques. And one algorithm doesn’t fit all data.
This document outlines key concepts for analyzing qualitative and quantitative data. It discusses preparing data through editing, coding and inserting into a matrix. Graphical techniques like histograms, scatter plots and box plots are presented for depicting individual, comparative and relational data. Measures of central tendency, dispersion, relationships and models are explained including mean, median, standard deviation, correlation, and linear and non-linear models. The goal is for students to understand how to analyze data using appropriate statistical techniques and data visualization.
This document provides an overview of data mining techniques for predictive modeling, including classification and regression trees (CART), chi-squared automatic interaction detection (CHAID), neural networks, bagging, boosting, and examples of applying these techniques using SAS Enterprise Miner. It discusses data preparation, partitioning data into training, validation and test sets, handling missing data, selecting optimal tree size to avoid overfitting, and summarizes a preliminary decision tree model for predicting student GPA.
Lecture 10 - Model Testing and Evaluation, a lecture in subject module Statis...Maninda Edirisooriya
Model Testing and Evaluation is a lesson where you learn how to train different ML models with changes and evaluating them to select the best model out of them. This was one of the lectures of a full course I taught in University of Moratuwa, Sri Lanka on 2023 second half of the year.
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.
Exploratory data analysis (EDA) involves analyzing datasets to discover patterns, trends, and relationships. EDA techniques include graphical methods like histograms, box plots, and scatter plots as well as calculating summary statistics. The goal of EDA is to better understand the data structure and relationships between variables through visual and numerical techniques without beginning with a specific hypothesis. EDA is used to generate hypotheses for further confirmatory analysis and to identify outliers, anomalies, and other unusual data characteristics. Lattice graphics and other plotting functions in R can be useful tools for EDA to visualize univariate and bivariate relationships in data.
This document outlines the key topics in Analytical Chemistry I including significant figures, types of errors, propagation of uncertainty, and systematic vs random errors. It discusses how measurements have uncertainty and errors. There are two main types of errors - systematic errors which affect accuracy and can be discovered and corrected, and random errors which cannot be eliminated and have equal chances of being positive or negative. The document also describes how to calculate the propagation of uncertainty through calculations using addition, subtraction, multiplication, division and other operations. It emphasizes keeping extra digits in calculations to properly account for uncertainty.
Application of Machine Learning in AgricultureAman Vasisht
With the growing trend of machine learning, it is needless to say how machine learning can help reap benefits in agriculture. It will be boon for the farmer welfare.
1) The document describes a method for imputing missing values in large multivariate databases. It proposes modeling variables on bins to address the curse of dimensionality. Variables are selected based on correlation and those with overlapping missingness are excluded. Values are imputed sequentially based on patterns in other variables.
2) Potential improvements discussed include using more sophisticated modeling methods, addressing increased variances from using imputed variables, and handling non-random missingness.
3) An empirical application imputes missing ages in a customer database using various methods. Results show the multivariate method best matches the true distribution of a binary variable compared to mean imputation or deleting missing cases.
Patient’s Condition Classification Using Drug Reviews.pptxAravind Reddy
Using patient drug reviews and natural language processing, this project aims to classify patients' conditions to reduce doctors' time spent evaluating test results. The workflow involves preprocessing drug reviews with lemmatization, building machine learning models, and evaluating the models' performance at classifying patients. The results show using bag-of-words with TF-IDF and trigrams was an effective approach.
Missing data occurs when no data value is stored for a variable in an observation, usually due to manual errors or incorrect measurements. There are three types of missing data: missing completely at random, missing at random, and missing not at random. Several methods can be used to deal with missing data, including reducing the dataset, treating missing values as a special value, replacing with the mean, replacing with the most common value, and using the closest fit from similar observations. Proper handling of missing data is important to avoid bias and distortions in analyzing the dataset.
Missing data occurs when no data value is stored for a variable in an observation, usually due to manual errors or incorrect measurements. There are three types of missing data: missing completely at random, missing at random, and missing not at random. Several methods can be used to deal with missing data, including reducing the dataset, treating missing values as a special value, replacing with the mean, replacing with the most common value, and using the closest fit to impute missing values. Proper handling of missing data is important to avoid bias and distortions in analyzing the data.
Statistical analysis & errors (lecture 3)Farhad Ashraf
This document discusses statistical analysis and errors in measurement. It defines statistical analysis as dealing with numerical data using probability theory. Measurement errors can be divided into determinate (systematic) errors and indeterminate (random) errors. Determinate errors can be avoided or corrected, while indeterminate errors cannot be determined precisely but their probability can be estimated using statistical distributions like the Gaussian curve. The document also discusses concepts like significant figures, rounding off data, measures of central tendency (mean, median, mode), standard deviation, tests like F-test and T-test, quality control/quality assurance, good laboratory practices, validation of analytical methods and their parameters.
This document discusses feature engineering, which is the process of transforming raw data into features that better represent the underlying problem for predictive models. It covers feature engineering categories like feature selection, feature transformation, and feature extraction. Specific techniques covered include imputation, handling outliers, binning, log transforms, scaling, and feature subset selection methods like filter, wrapper, and embedded methods. The goal of feature engineering is to improve machine learning model performance by preparing proper input data compatible with algorithm requirements.
This presentation deals with the formal presentation of anomaly detection and outlier analysis and types of anomalies and outliers. Different approaches to tackel anomaly detection problems.
This document discusses issues related to using data and models to make reliable predictions in the physical sciences. It notes that while modern simulations and data collection enable unprecedented detail, making reliable predictions remains challenging. Several key issues are discussed, including model validation, the problem of prediction, and properly accounting for data and uncertainties. The document emphasizes that predictions require extrapolating available information, so models must be based on reliable theory, with uncertainties in embedded models represented probabilistically. An example involving predicting the maximum velocity of a spring-mass-damper system demonstrates representing model uncertainty and checking predictions against additional validation data. The document concludes that making reliable predictions requires models informed by constraints from both data and theory, as well as understanding the limitations of any data
This document discusses several methods for preparing data before analysis, including handling outliers, missing data, duplicated data, and heterogeneous data formats. For outliers, it describes techniques like trimming, winsorizing, and changing regression models. For missing data, it covers identifying patterns, assessing causes, and handling techniques like listwise deletion, imputation, and multiple imputations. It also addresses detecting and removing duplicate records based on field similarities, as well as standardizing heterogeneous data formats.
Simple math for anomaly detection toufic boubez - metafor software - monito...tboubez
This is my presentation at Monitorama PDX in Portland on May 5, 2014
Simple math to get some signal out of your noisy sea of data
You’ve instrumented your system and application to the hilt. You can now “measure all the things”. Your team has set up thousands of metrics collecting millions of data points a day. Now what?
Most IT ops teams only keep an eye on a small fraction of the metrics they collect because analyzing this mountain of data and extracting signal from the noise is not easy. The choice of what analytic method to use ranges from simple statistical analysis to sophisticated machine learning techniques. And one algorithm doesn’t fit all data.
This document outlines key concepts for analyzing qualitative and quantitative data. It discusses preparing data through editing, coding and inserting into a matrix. Graphical techniques like histograms, scatter plots and box plots are presented for depicting individual, comparative and relational data. Measures of central tendency, dispersion, relationships and models are explained including mean, median, standard deviation, correlation, and linear and non-linear models. The goal is for students to understand how to analyze data using appropriate statistical techniques and data visualization.
This document provides an overview of data mining techniques for predictive modeling, including classification and regression trees (CART), chi-squared automatic interaction detection (CHAID), neural networks, bagging, boosting, and examples of applying these techniques using SAS Enterprise Miner. It discusses data preparation, partitioning data into training, validation and test sets, handling missing data, selecting optimal tree size to avoid overfitting, and summarizes a preliminary decision tree model for predicting student GPA.
Lecture 10 - Model Testing and Evaluation, a lecture in subject module Statis...Maninda Edirisooriya
Model Testing and Evaluation is a lesson where you learn how to train different ML models with changes and evaluating them to select the best model out of them. This was one of the lectures of a full course I taught in University of Moratuwa, Sri Lanka on 2023 second half of the year.
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.
Exploratory data analysis (EDA) involves analyzing datasets to discover patterns, trends, and relationships. EDA techniques include graphical methods like histograms, box plots, and scatter plots as well as calculating summary statistics. The goal of EDA is to better understand the data structure and relationships between variables through visual and numerical techniques without beginning with a specific hypothesis. EDA is used to generate hypotheses for further confirmatory analysis and to identify outliers, anomalies, and other unusual data characteristics. Lattice graphics and other plotting functions in R can be useful tools for EDA to visualize univariate and bivariate relationships in data.
This document outlines the key topics in Analytical Chemistry I including significant figures, types of errors, propagation of uncertainty, and systematic vs random errors. It discusses how measurements have uncertainty and errors. There are two main types of errors - systematic errors which affect accuracy and can be discovered and corrected, and random errors which cannot be eliminated and have equal chances of being positive or negative. The document also describes how to calculate the propagation of uncertainty through calculations using addition, subtraction, multiplication, division and other operations. It emphasizes keeping extra digits in calculations to properly account for uncertainty.
Application of Machine Learning in AgricultureAman Vasisht
With the growing trend of machine learning, it is needless to say how machine learning can help reap benefits in agriculture. It will be boon for the farmer welfare.
1) The document describes a method for imputing missing values in large multivariate databases. It proposes modeling variables on bins to address the curse of dimensionality. Variables are selected based on correlation and those with overlapping missingness are excluded. Values are imputed sequentially based on patterns in other variables.
2) Potential improvements discussed include using more sophisticated modeling methods, addressing increased variances from using imputed variables, and handling non-random missingness.
3) An empirical application imputes missing ages in a customer database using various methods. Results show the multivariate method best matches the true distribution of a binary variable compared to mean imputation or deleting missing cases.
Patient’s Condition Classification Using Drug Reviews.pptxAravind Reddy
Using patient drug reviews and natural language processing, this project aims to classify patients' conditions to reduce doctors' time spent evaluating test results. The workflow involves preprocessing drug reviews with lemmatization, building machine learning models, and evaluating the models' performance at classifying patients. The results show using bag-of-words with TF-IDF and trigrams was an effective approach.
Natural Language Processing for developmentAravind Reddy
Natural Language Processing (NLP) is a field of artificial intelligence that allows computers to understand, process, and derive meaning from human language. NLP incorporates machine learning, statistics, and computational linguistics to analyze large amounts of natural language data and emulate human language understanding. Key applications of NLP include machine translation, conversational agents, information extraction, and natural language generation. While NLP has advanced capabilities, fully simulating human language comprehension remains a challenge for artificial intelligence.
The document discusses pilots for green jobs and tech jobs. It provides details on pilots for solar panel installation technicians and EV technicians, including target groups, course duration, centers, and progress updates on completed batches. It also discusses tech job pilots for full stack developers and data scientists, with similar details on target groups, course duration, centers, and progress on completed batches. Placement rates and average salaries are provided for each completed batch.
Tech Jobs Green Jobs- Deck . 24-11.pptxAravind Reddy
The training programs helped three individuals increase their and their families' monthly incomes and achieve financial independence. Siri Chandana's monthly income increased from ₹10,000 to ₹35,000 after completing a front end development training and securing a job as a software trainee earning ₹25,000 per month. Gopinadh's monthly income rose from ₹11,000 to ₹41,000 after a green jobs program, getting hired as a junior depot manager earning ₹30,833. Anarasi Ramakrishna's income increased from ₹6,000 to ₹19,000 following solar technician training, now earning ₹13,
Data Analyst, Data Scientist, and Data Engineer are three distinct roles within the field of data and analytics, each with its own set of responsibilities and skill requirements. Here's a brief overview of each role:
Python is an interpreted, object-oriented, high-level programming language with dynamic semantics. Its high-level built in data structures, combined with dynamic typing and dynamic binding, make it very attractive for Rapid Application Development, as well as for use as a scripting or glue language to connect existing components together
I am a graduate with excellent communication and problem-solving skills. Experienced professional with strong work ethics and experience utilizing latest technologies. Proven ability to quickly learn new concepts and apply them effectively. Self-directed and motivated to exceed expectations
Just like a list, a tuple is also an ordered collection of Python objects. The only difference between a tuple and a list is that tuples are immutable i.e. tuples cannot be modified after it is created. It is represented by a tuple class
Types of Primary and Secondary Sources.pptAravind Reddy
Just like a list, a tuple is also an ordered collection of Python objects. The only difference between a tuple and a list is that tuples are immutable i.e. tuples cannot be modified after it is created. It is represented by a tuple class
adminsitarative data data-57511556 (1).pptxAravind Reddy
Administrative data are records collected by government agencies and private organizations to administer non-statistical programs. Examples include educational records, client information from banks, hospital patient records, tax filings, Medicare claims, and insurance enrollment and claims data. Administrative data provide information on individuals and transactions for operational purposes rather than statistical analysis.
This document provides an introduction to data science, including:
- The large amounts of data being collected from sources like the web, financial transactions, and social networks.
- How "big data" refers to data that is expensive to manage and hard to extract value from due to its volume, velocity, and variety.
- What data scientists do, such as finding patterns and stories in data to help decision makers, and how data science draws from fields like computer science, mathematics, and statistics.
The information you provided appears to be a list of column headers or variables related to a dataset containing information about loans or credit-related data. Here's a brief description of each column:
1. credit.policy: A binary variable indicating whether a customer meets the credit policy criteria (1 for yes, 0 for no).
2. purpose: The purpose for which the loan was taken (e.g., debt consolidation, credit card, small business).
3. int.rate: The interest rate of the loan.
4. installment: The monthly installment payment amount.
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
The simplified electron and muon model, Oscillating Spacetime: The Foundation...RitikBhardwaj56
Discover the Simplified Electron and Muon Model: A New Wave-Based Approach to Understanding Particles delves into a groundbreaking theory that presents electrons and muons as rotating soliton waves within oscillating spacetime. Geared towards students, researchers, and science buffs, this book breaks down complex ideas into simple explanations. It covers topics such as electron waves, temporal dynamics, and the implications of this model on particle physics. With clear illustrations and easy-to-follow explanations, readers will gain a new outlook on the universe's fundamental nature.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
Physiology and chemistry of skin and pigmentation, hairs, scalp, lips and nail, Cleansing cream, Lotions, Face powders, Face packs, Lipsticks, Bath products, soaps and baby product,
Preparation and standardization of the following : Tonic, Bleaches, Dentifrices and Mouth washes & Tooth Pastes, Cosmetics for Nails.
How to Add Chatter in the odoo 17 ERP ModuleCeline George
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
2. MISSING DATA
• Missing data are observations that we intend to make but couldn’t.
• Answering only certain questions to a questionnaire
• Not measuring the temperature due to extreme cold
• Not answering income due to being too rich…
• When we have missing data, our goal remains the same with what it was if
have the complete data. So, the analysis are now more complex.
• How to denote missing data:
• SAS .
• S+ and R NA or na
• -9999 or something like this (Be careful! Make sure that the number is not
in the dataset and no use them in the analysis)
2
3. Missingness Mechanism
• Before starting any analysis with incomplete data, we have to clarify the
nature of missingness mechanism which causes some values being missing.
Previously, there was common belief that the mechanism was random but
it was really as it was thought?
• Generally, there are two notions accepted for missingness mechanism by all
researchers: ignorable and non-ignorable missingness mechanism.
• If the mechanism is ignorable we don’t have to care about it and we can
ignore it confidently before missing data analysis but if it is not we have to
model the mechanism also as part of the parameter estimation.
• Identifying the missingness mechanism with a statistical approach is still
being a tough problem and so try to develop some diagnostic procedure on
missingness mechanism is an important research topic.
3
4. Missingness Mechanism
• Rubin (1976) specified three types of assumptions on missingness mechanism:
• Missing Completely at Random (MCAR)
• Missing at Random (MAR)
• Missing Not at Random (MNAR).
• MCAR and MAR are in class of ignorable missingness mechanism but MNAR is in
class of non-ignorable mechanism.
• MCAR assumption is generally difficult to meet in reality and it assumes that
there is no statistically significant difference between incomplete and complete
cases. In other words, the observed data points can only be considered as a
simple random sample of the variables you would have to analyze. It assumes
that missingness is completely unrelated to the data (Enders, 2010). In this case,
there is no impact of missingness affecting on the inferences. Little (1988)
proposed a chi-square test for diagnosing MCAR mechanism so called Little’s
MCAR test.
4
5. Missingness Mechanism
• Failure to confirm the assumption of MCAR using statistical tests means that the
missing data mechanism is either MAR or MNAR.
• Unfortunately, it is impossible to determine whether a mechanism is MAR or
MNAR. This is an important practical problem of missing data analysis and
classified untestable assumption because we do not know the values of the
missing scores, we cannot compare the values of those with and without missing
data to see if they differ systematically on that variable (Allison, 2001).
• The most of the missing data handling approaches especially EM algorithm and
MI relies on MAR assumption (Schafer, 1997). If we can decide that the
mechanism that causes missingness is ignorable in such a way, then assuming the
mechanism is MAR seems suitable for further analysis. Conducting the EM
algorithm and MCMC based MI under MCAR assumption will be also appropriate,
since the mechanism of missingness is ignorable (Schafer, 1997).
5
14. Missing Data Patterns
(a) (b) (c)
𝑌1 𝑌2 𝑌3 𝑌4 𝑌1 𝑌2 𝑌3 𝑌4 𝑌1 𝑌2 𝑌3 𝑌4
m
m m
m m m m
m m m m m m m
Figure 1.1:Three prototypical missing data pattern: (a) monotone missingness,
(b) univariate missingness, (c) arbitrary missingness
14
16. Ways to Understand the Missingness
Mechanism within the Data
• It is not possible to extract missing data patterns from observed data
but you can explore data to get a sense.
e.g. Assume there are missing data in X1 variable. Divide X2 and X3 into
2 parts from where X1 is missing and investigate two parts separately. If
the results (summary measures or inferences) are different in two part,
the missingness in X1 is possibly not at random.
X1 X2 X3
missing
16
17. Ways to Understand the Missingness
Mechanism within the Data
• Although you can and should explore data, you need to make a
reasonable assumption for missing data.
• MCAR is a stronger assumption than MAR, and MNAR is hard to
model. There is usually very little we can do when the case is missing
not at random. Usually, MAR is assumed.
• Ask experts why data are missing?
17
18. Dealing with Missing Data
• Use what you know about
• Why data are missing
• Distribution of missing data
• Decide on the best analysis strategy to yield the least biased
estimates
18
19. Deletion Methods
• Delete all cases with incomplete data and conduct analysis using only
complete cases.
• Advantage: Simplicity
• Disadvantage: loss of data if we discard all incomplete cases. So, in
efficient
• NOTE: If you use complete case analysis, then change summary
statistics for other variables, too.
19
20. Example: n=19,p=4, only 15% missing values
Individual Case 1 Case 2 Case 3
y1 y2 y3 y4 y1 y2 y3 y4 y1 y2 y3 y4
1 NA NA NA NA NA NA
2 NA NA NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7
8
9
10
Eliminate individual 1 and 2.
Keep 8*4=32 data. 20% loss
Eliminate variable 1.
Keep 10*3=30 data. 25% loss
Eliminate individual 1 -6.
Keep 4*4=16 data. 60% loss20
21. Listwise Deletion (Complete case analysis)
• Only analyze cases with available data on each variable
• Advantage: simplicity and comparability across analyses
• Disadvantage: reduces statistical power (due to sample size), not use
all information, estimates may be biased if data not MCAR
• Listwise deletion often produces unbiased regression slope estimates
as long as missingness is not a function of outcome variable.
21
22. Pairwise Deletion (Available case analysis)
• Analysis with all cases in which the variables of interest are present
• Advantage: keeps as many cases as possible for each analysis, uses all
information possible with each analysis
• Disadvantage: cannot compare analyses because sample is different
each time, sample size vary for each parameter estimation, can obtain
nonsense results
• Compute the summary statistics using ni observations not n.
• Compute correlation type statistics using complete pairs for both
variables.
22
24. Imputation Methods
• 1. Random sample from existing values:
You can randomly generate an integer from 1 to n-nmissing, then replace
the missing value with the corresponding observation that you chose
randomly
Case: 1 2 3 4 5 6 7 8 9 10
Y1: 3.4 3.9 2.6 1.9 2.2 3.3 1.7 2.4 2.8 3.6
Y2: 5.7 4.8 4.9 6.2 6.8 5.6 5.4 4.9 5.7 NA
Randomly generate number between 1 and 9: Say 3
Replace Y2,10 by Y2,3=4.9
Disadvantage: It may change the distribution of data
4.9
24
25. Imputation Methods
• 2. Randomly sample from a reasonable distribution
e.g. If gender is missing and you have the information that there re
about the sample number of females and males in the population.
Gender ~Ber(p=0.5) or estimate p from the observed sample
Using random number generator from Bernoulli distribution for p=0.5,
generate numbers for missing gender data
Disadvantage: distributional assumption may not be reliable (or correct),
even the assumption is correct, its representativeness is doubtful.
25
26. Imputation Methods
• 3. Mean/Mode Substitution
Replace missing value with the sample mean or mode. Then, run
analyses as if all complete cases
Advantage: We can use complete case analyses
Disadvantage: Reduces variability, weakens the correlation estimates
because it ignores the relationship between variables, it creates
artificial band
Unless the proportion of missing data is low, do not use this method.
26
27. Last Observation Carried Forward
• This method is specific to longitudinal data problems.
• For each individual, NAs are replaced by the last observed value of
that variable. Then, analyze data as if data were fully observed.
Disadvantage: The covariance structure and distribution change
seriously
Cases 1 2 3 4 5 6
1 3.8 3.1 2.0 NA NA NA
2 4.1 3.5 2.8 2.4 2.8 3.0
3 2.7 2.4 2.9 3.5 NA NA
Observation time
2.0 2.0 2.0
3.5 3.5
27
28. Imputation Methods
• 4. Dummy variable adjustment
Create an indicator variable for missing value (1 for missing, 0 for
observed)
Impute missing value to a constant (such as mean)
Include missing indicator in the regression
Advantage: Uses all information about missing observation
Disadvantage: Results in biased estimates, not theoretically driven
28
29. Imputation Methods
• 5. Regression imputation
Replace missing values with predicted score from regression equation.
Use complete cases to regress the variable with incomplete data on the
other complete variables.
Advantage: Uses information from the observed data, gives better
results than previous ones
Disadvantage: over-estimates model fit and correlation estimates,
weakens variance
29
31. Imputation Methods
• 6. Maximum Likelihood Estimation
Identifies the set of parameter values that produces the highest log-
likelihood.
ML estimate: value that is most likely to have resulted in the observed
data.
Advantage: uses full information (both complete and incomplete) to
calculate the log-likelihood, unbiased parameter estimates with
MCAR/MAR data
Disadvantage: Standard errors biased downward but this can be
adjusted by using observed information matrix.
31
35. Multiple Imputation (MI)
• Multiple imputation (MI) appears to be one of the most attractive methods for
general- purpose handling of missing data in multivariate analysis. The basic idea,
first proposed by Rubin (1977) and elaborated in his (1987) book, is quite simple:
1. Impute missing values using an appropriate model that incorporates random
variation.
2. Do this M times producing M “complete” data sets.
3. Perform the desired analysis on each data set using standard complete-data
methods.
4. Average the values of the parameter estimates across the M samples to
produce a single point estimate.
5. Calculate the standard errors by (a) averaging the squared standard errors of
the M estimates (b) calculating the variance of the M parameter estimates
across samples, and (c) combining the two quantities using a simple formula
35
36. Multiple Imputation
• Multiple imputation has several desirable features:
• Introducing appropriate random error into the imputation process
makes it possible to get approximately unbiased estimates of all
parameters. No deterministic imputation method can do this in
general settings.
• Repeated imputation allows one to get good estimates of the
standard errors. Single imputation methods don’t allow for the
additional error introduced by imputation (without specialized
software of very limited generality).
36
37. Multiple Imputation
• With regards to the assumptions needed for MI,
• First, the data must be missing at random (MAR), meaning that the probability of
missing data on a particular variable Y can depend on other observed variables,
but not on Y itself (controlling for the other observed variables).
Example: Data are MAR if the probability of missing income depends on marital
status, but within each marital status, the probability of missing income does not
depend on income; e.g. single people may be more likely to be missing data on
income, but low income single people are no more likely to be missing income than
are high income single people.
• Second, the model used to generate the imputed values must be “correct” in
some sense.
• Third, the model used for the analysis must match up, in some sense, with the
model used in the imputation
37
39. Imputation in R
• MICE (Multivariate Imputation via Chained Equations): Creating multiple imputations as compared to a single imputation (such as
mean) takes care of uncertainty in missing values. It assumes MAR
• Amelia(https://cran.r-project.org/web/packages/Amelia/vignettes/amelia.pdf): This package (Amelia II) is named after Amelia
Earhart, the first female aviator to fly solo across the Atlantic Ocean. History says, she got mysteriously disappeared (missing)
while flying over the pacific ocean in 1937, hence this package was named to solve missing value problems. This package also
performs multiple imputation to deal with missing values. It is enabled with bootstrap based EMB algorithm which makes it faster
and robust to impute many variables including cross sectional, time series data etc. Also, it is enabled with parallel imputation
feature using multicore CPUs. Asumptions: All variables in a data set have Multivariate Normal Distribution (MVN) and MAR
• missForest: an implementation of random forest algorithm. It’s a non parametric imputation method applicable to various
variable types. It builds a random forest model for each variable. Then it uses the model to predict missing values in the variable
with the help of observed values. It yield OOB (out of bag) imputation error estimate. Moreover, it provides high level of control
on imputation process.
• Hmisc: a multiple purpose package useful for data analysis, high – level graphics, imputing missing values, advanced table
making, model fitting & diagnostics (linear regression, logistic regression & cox regression) etc. impute() function simply imputes
missing value using user defined statistical method (mean, max, mean). It’s default is median. On the other
hand, aregImpute() allows mean imputation using additive regression, bootstrapping, and predictive mean matching. In
bootstrapping, different bootstrap resamples are used for each of multiple imputations. Then, a flexible additive model (non
parametric regression method) is fitted on samples taken with replacements from original data and missing values (acts as
dependent variable) are predicted using non-missing values (independent variable).
• mi: (Multiple imputation with diagnostics) package provides several features for dealing with missing values. It also
builds multiple imputation models to approximate missing values. And, uses predictive mean matching method. For each
observation in a variable with missing value, we find observation (from available values) with the closest predictive mean to that
variable. The observed value from this “match” is then used as imputed value. 39