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This document summarizes a project to apply machine learning models to predict outcomes of cases in the US Court of Appeals based on historical data. It describes the data source and characteristics, data preprocessing steps including handling missing values and complex variables, dimensionality reduction techniques, models selected like random forest, neural networks and XGBoost, and the results of model tuning and testing. The neural network achieved an accuracy of 85% on test data after oversampling the training data, while XGBoost achieved 91% accuracy after tuning. The most significant variables identified for prediction included the circuit court, verdict of the previous court, nature of the appellant, and directionality of the third judge.
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ICBAI Paper (1)
The document describes developing machine learning models to predict outcomes of cases in the US Court of Appeals using historical data. Neural networks and XGBoost classifiers were developed and evaluated on a dataset of over 2,000 court cases. The models achieved encouraging accuracy levels between 36-98%, though limitations included the small dataset size. Significant factors identified included the appeals court, nature of prior rulings, applicant type, and judges' ideological leanings. Future work could focus on decision trees to characterize outcomes and simplify the models.
International Journal of Engineering and Science Invention (IJESI)
This document proposes a new approach for processing multiple queries for a patented medical database that handles temporal domain events more efficiently. The approach uses three techniques: automatic error correction, topic relevant query suggestion, and extended query augmentation. Queries are first used to retrieve coherent clusters from topic-classified medical data. Relevant results from each cluster are then combined to generate the top K answers for the user. The techniques aim to better define the user's information need and improve retrieval speed and memory usage when searching large medical databases. An experimental evaluation found the approach improved retrieval quality and efficiency compared to existing methods.
4113ijaia09
This document reviews the use of data mining and neural network techniques for stock market prediction. It discusses how data mining can extract hidden patterns from large datasets and neural networks can handle nonlinear and uncertain financial data. Specifically, it examines how a combination of data mining and neural networks may improve the reliability of stock predictions by leveraging their complementary strengths. The document also provides an overview of common data mining and neural network methods used for this purpose, such as statistical data mining, neural network-based data processing, clustering, and fuzzy logic. It reviews several previous studies that found neural networks and other nonlinear techniques often outperform traditional statistical models at predicting stock prices and indices.
IRJET- Medical Data Mining
This document discusses medical data mining and classification techniques. It begins with an introduction to data mining and its applications in healthcare to improve treatment. Medical data mining can help discover patterns in medical data to aid diagnosis. Classification algorithms like decision trees can categorize medical records and help predict outcomes. Specifically, the document discusses the J48 decision tree algorithm available in the WEKA data mining tool, which implements the C4.5 algorithm for classification. Decision trees work by recursively splitting the data into subsets based on attribute values, forming a tree structure. The document concludes that while data mining can help with medical analysis, results from small medical datasets should be interpreted cautiously.
The 8 Step Data Mining Process
The document describes the 8 step data mining process:
1) Defining the problem, 2) Collecting data, 3) Preparing data, 4) Pre-processing, 5) Selecting an algorithm and parameters, 6) Training and testing, 7) Iterating models, 8) Evaluating the final model. It discusses issues like defining classification vs estimation problems, selecting appropriate inputs and outputs, and determining when sufficient data has been collected for modeling.
Sara Gerke: "AI in Drug Discovery and Clinical Trials"
Sara Gerke: "AI in Drug Discovery and Clinical Trials"The Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics
PLEASE NOTE: THESE SLIDES MAY NOT DISPLAY PROPERLY ONLINE, BUT THEY ARE READABLE IF DOWNLOADED.
October 28, 2018
This one-day conference explored the current pharmaceutical pricing landscape by bringing together leaders from the pharmaceutical industry, policymakers, legal practitioners, and scholars to engage in novel, interdisciplinary discussions to better understand current challenges and articulate best practices to address these issues. Participants assessed the current challenges presented in drug pricing policy, from development to delivery, in both the United States and international context. We also explored and articulated best practices to expand access to medicines and worked toward developing a plan for disseminating these practices more widely.Selecting the correct Data Mining Method: Classification & InDaMiTe-R
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Assess data reliability from a set of criteria using the theory of belief fun...
This document proposes a method to assess data reliability from metadata using belief functions. It discusses evaluating data reliability from a set of criteria as there is little existing work focused on this problem. The method aims to provide a general approach to calculate an overall reliability score by combining multiple criteria despite any conflicts between them. It models the information with evidence theory to handle uncertainty and provide useful ordering of reliability assessments for end users.
Recommended
ICBAI Paper (1)
The document describes developing machine learning models to predict outcomes of cases in the US Court of Appeals using historical data. Neural networks and XGBoost classifiers were developed and evaluated on a dataset of over 2,000 court cases. The models achieved encouraging accuracy levels between 36-98%, though limitations included the small dataset size. Significant factors identified included the appeals court, nature of prior rulings, applicant type, and judges' ideological leanings. Future work could focus on decision trees to characterize outcomes and simplify the models.
International Journal of Engineering and Science Invention (IJESI)
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4113ijaia09
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IRJET- Medical Data Mining
This document discusses medical data mining and classification techniques. It begins with an introduction to data mining and its applications in healthcare to improve treatment. Medical data mining can help discover patterns in medical data to aid diagnosis. Classification algorithms like decision trees can categorize medical records and help predict outcomes. Specifically, the document discusses the J48 decision tree algorithm available in the WEKA data mining tool, which implements the C4.5 algorithm for classification. Decision trees work by recursively splitting the data into subsets based on attribute values, forming a tree structure. The document concludes that while data mining can help with medical analysis, results from small medical datasets should be interpreted cautiously.
The 8 Step Data Mining Process
The document describes the 8 step data mining process:
1) Defining the problem, 2) Collecting data, 3) Preparing data, 4) Pre-processing, 5) Selecting an algorithm and parameters, 6) Training and testing, 7) Iterating models, 8) Evaluating the final model. It discusses issues like defining classification vs estimation problems, selecting appropriate inputs and outputs, and determining when sufficient data has been collected for modeling.
Sara Gerke: "AI in Drug Discovery and Clinical Trials"
Sara Gerke: "AI in Drug Discovery and Clinical Trials"The Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics
PLEASE NOTE: THESE SLIDES MAY NOT DISPLAY PROPERLY ONLINE, BUT THEY ARE READABLE IF DOWNLOADED.
October 28, 2018
This one-day conference explored the current pharmaceutical pricing landscape by bringing together leaders from the pharmaceutical industry, policymakers, legal practitioners, and scholars to engage in novel, interdisciplinary discussions to better understand current challenges and articulate best practices to address these issues. Participants assessed the current challenges presented in drug pricing policy, from development to delivery, in both the United States and international context. We also explored and articulated best practices to expand access to medicines and worked toward developing a plan for disseminating these practices more widely.Selecting the correct Data Mining Method: Classification & InDaMiTe-R
This document describes an intelligent data mining assistant called InDaMiTe-R that aims to help users select the correct data mining method for their problem and data. It presents a classification of common data mining techniques organized by the goal of the problem (descriptive vs predictive) and the structure of the data. This classification is meant to model the human decision process for selecting techniques. The document then describes InDaMiTe-R, which uses a case-based reasoning approach to recommend techniques based on past user experiences with similar problems and data. An example of its use is provided to illustrate how it extracts problem metadata, gets user restrictions, recommends initial techniques, and learns from the user's evaluations to improve future recommendations. A small evaluation
Assess data reliability from a set of criteria using the theory of belief fun...
This document proposes a method to assess data reliability from metadata using belief functions. It discusses evaluating data reliability from a set of criteria as there is little existing work focused on this problem. The method aims to provide a general approach to calculate an overall reliability score by combining multiple criteria despite any conflicts between them. It models the information with evidence theory to handle uncertainty and provide useful ordering of reliability assessments for end users.
Query-Based Retrieval of Annotated Document
1. The document discusses a new technique that combines the Collaborative Adaptive Data Sharing (CADS) framework and USHER technique to improve information quality and recommendation of attributes for annotated document retrieval.
2. The technique uses CADS to first build the structure and provide attribute recommendations, then applies USHER's probabilistic model to automatically generate query forms and minimize questions, improving information quality with lower cost.
3. By jointly using CADS for structure design and USHER for applying probabilistic models, the proposed dual approach achieves more effective results from both frameworks for enhanced data search.
Application of data mining tools for
One of the most important problems in modern finance is finding efficient ways to summarize and visualize
the stock market data to give individuals or institutions useful information about the market behavior for
investment decisions Therefore, Investment can be considered as one of the fundamental pillars of national
economy. So, at the present time many investors look to find criterion to compare stocks together and
selecting the best and also investors choose strategies that maximize the earning value of the investment
process. Therefore the enormous amount of valuable data generated by the stock market has attracted
researchers to explore this problem domain using different methodologies. Therefore research in data
mining has gained a high attraction due to the importance of its applications and the increasing generation
information. So, Data mining tools such as association rule, rule induction method and Apriori algorithm
techniques are used to find association between different scripts of stock market, and also much of the
research and development has taken place regarding the reasons for fluctuating Indian stock exchange.
But, now days there are two important factors such as gold prices and US Dollar Prices are more
dominating on Indian Stock Market and to find out the correlation between gold prices, dollar prices and
BSE index statistical correlation is used and this helps the activities of stock operators, brokers, investors
and jobbers. They are based on the forecasting the fluctuation of index share prices, gold prices, dollar
prices and transactions of customers. Hence researcher has considered these problems as a topic for
research.
IJET-V2I6P32
This document summarizes and compares different classification algorithms that can be used for disease prediction in data mining. It first introduces disease prediction and classification processes. It then reviews related works that have used various classification algorithms like random forest, support vector machine, and naive Bayes for tasks like disease diagnosis, text classification, and rainfall forecasting. The document also discusses supervised, unsupervised, and semi-supervised machine learning. It provides details on support vector machine and random forest algorithms, describing how each works and is used for classification. Finally, it analyzes the random forest algorithm construction process.
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This document discusses techniques for enhancing the performance of data warehouse staging areas. It proposes two algorithms: 1) A semantics-based extraction algorithm that reduces extraction time by pruning useless data using semantic information. 2) A semantics-based transformation algorithm that similarly aims to reduce transformation time. It also explores three scheduling techniques (FIFO, minimum cost, round robin) for loading data into the data warehouse and experimentally evaluates their performance. The goal is to enhance each stage of the ETL process to maximize overall performance.
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Credit scoring is a procedure that exists in every financial institution. A way to predict whether the debtor was qualified to be given the loan or not and has been a major concern in the overall steps of the loan process. Almost all banks and other financial institutions have their own credit scoring methods. Nowadays, data mining approach has been accepted to be one of the wellknown methods. Certainly, accuracy was also a major issue in this approach. This research proposed a hybrid method using CART algorithm and Binary Particle Swarm Optimization. Performance indicators that are used in this research are classification accuracy, error rate, sensitivity, specificity, and precision. Experimental results based on the public dataset showed that the proposed method accuracy is 78 % and 87.53 %. In compare to several popular algorithms, such as neural network, logistic regression and support vector machine, the proposed method showed an outstanding performance.
Ez36937941
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Analysis on Student Admission Enquiry System
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records from the different integrated data sources. It refers to the process of selecting or fusing attribute
values from the clustered duplicates into a single record representing the real world object. In this paper, a
statistical technique for data fusion is introduced based on some probabilistic scores from both data
sources and clustered duplicates
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Quality Assurance in Knowledge Data Warehouse
Knowledge discovery is the process of adding knowledge from a large amount of data. The quality of knowledge generated from the process of knowledge discovery greatly affects the results of the decisions obtained. Existing data must be qualified and tested to ensure knowledge discovery processes can produce knowledge or information that is useful and feasible. It deals with strategic decision making for an organization. Combining multiple operational databases and external data create data warehouse. This treatment is very vulnerable to incomplete, inconsistent, and noisy data. Data mining provides a mechanism to clear this deficiency before finally stored in the data warehouse. This research tries to give technique to improve the quality of information in the data warehouse.
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Data Mining is a computational progression to ascertain patterns in hefty data sets. It has various important techniques and one of them is Classification which is receiving great attention recently in the database community. Classification technique can solve several problems in different fields like medicine, industry, business, science. PSO is based on social behaviour for optimization problem. Feature Selection (FS) is a solution that involves finding a subset of prominent features to improve predictive accuracy and to remove the redundant features. Rough Set Theory (RST) is a mathematical tool which deals with the uncertainty and vagueness of the decision systems.
Ijcatr04061009
The objective of this study was to apply Data Mining in the analysis of imports of pharmaceutical products in Kenya with the aim of discovering patterns of association and correlation among the various product groups. The RapidMiner Data Mining was used to analyze data obtained from the Pharmacy and Poison Board, the regulator of pharmaceutical products in the country, covering the period 2008 to 2010. The CRISP method was used to get a business understanding of the Board, understand the nature of the data held, prepare the data for analyze and actual analysis of the data. The results of the study discovered various patterns through correlation and association analysis of various product groups. The results were presented through various graphs and discussed with the domain experts. These patterns are similar to prescription patterns from studies in Ethiopia, Nigeria and India. The research will provide regulators of pharmaceutical products, not only in Kenya but other African countries, a better insight into the patterns of imports and exports of pharmaceutical products. This would result into better controls, not only in import and exports of the products, but also enforcement on their usage in order to avert negative effects to the citizens
Effective data mining for proper
With the development of database, the data volume stored in database increases rapidly and in the large
amounts of data much important information is hidden. If the information can be extracted from the
database they will create a lot of profit for the organization. The question they are asking is how to extract
this value. The answer is data mining. There are many technologies available to data mining practitioners,
including Artificial Neural Networks, Genetics, Fuzzy logic and Decision Trees. Many practitioners are
wary of Neural Networks due to their black box nature, even though they have proven themselves in many
situations. This paper is an overview of artificial neural networks and questions their position as a
preferred tool by data mining practitioners.
IRJET- A Review of Data Cleaning and its Current Approaches
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term of discharge summary, drug & pharma, diagnostics details, doctor’s report, medical history,
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Paper ID 216@ ICMLBDA 2023.pptx
This paper compares various machine learning classification algorithms to predict student academic performance using two datasets from the UCI Machine Learning Repository containing 1044 student records and 33 attributes. Logistic regression achieved the best performance with an accuracy of 0.899, outperforming decision trees, random forest, KNN, SVM, AdaBoost, and stochastic gradient descent models. The paper concludes that logistic regression is well-suited for predicting student risk of poor performance to help educators provide targeted support.
Classifier Model using Artificial Neural Network
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Query-Based Retrieval of Annotated Document
1. The document discusses a new technique that combines the Collaborative Adaptive Data Sharing (CADS) framework and USHER technique to improve information quality and recommendation of attributes for annotated document retrieval.
2. The technique uses CADS to first build the structure and provide attribute recommendations, then applies USHER's probabilistic model to automatically generate query forms and minimize questions, improving information quality with lower cost.
3. By jointly using CADS for structure design and USHER for applying probabilistic models, the proposed dual approach achieves more effective results from both frameworks for enhanced data search.
Application of data mining tools for
One of the most important problems in modern finance is finding efficient ways to summarize and visualize
the stock market data to give individuals or institutions useful information about the market behavior for
investment decisions Therefore, Investment can be considered as one of the fundamental pillars of national
economy. So, at the present time many investors look to find criterion to compare stocks together and
selecting the best and also investors choose strategies that maximize the earning value of the investment
process. Therefore the enormous amount of valuable data generated by the stock market has attracted
researchers to explore this problem domain using different methodologies. Therefore research in data
mining has gained a high attraction due to the importance of its applications and the increasing generation
information. So, Data mining tools such as association rule, rule induction method and Apriori algorithm
techniques are used to find association between different scripts of stock market, and also much of the
research and development has taken place regarding the reasons for fluctuating Indian stock exchange.
But, now days there are two important factors such as gold prices and US Dollar Prices are more
dominating on Indian Stock Market and to find out the correlation between gold prices, dollar prices and
BSE index statistical correlation is used and this helps the activities of stock operators, brokers, investors
and jobbers. They are based on the forecasting the fluctuation of index share prices, gold prices, dollar
prices and transactions of customers. Hence researcher has considered these problems as a topic for
research.
IJET-V2I6P32
This document summarizes and compares different classification algorithms that can be used for disease prediction in data mining. It first introduces disease prediction and classification processes. It then reviews related works that have used various classification algorithms like random forest, support vector machine, and naive Bayes for tasks like disease diagnosis, text classification, and rainfall forecasting. The document also discusses supervised, unsupervised, and semi-supervised machine learning. It provides details on support vector machine and random forest algorithms, describing how each works and is used for classification. Finally, it analyzes the random forest algorithm construction process.
Enhancement techniques for data warehouse staging area
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Credit Scoring Using CART Algorithm and Binary Particle Swarm Optimization
Credit scoring is a procedure that exists in every financial institution. A way to predict whether the debtor was qualified to be given the loan or not and has been a major concern in the overall steps of the loan process. Almost all banks and other financial institutions have their own credit scoring methods. Nowadays, data mining approach has been accepted to be one of the wellknown methods. Certainly, accuracy was also a major issue in this approach. This research proposed a hybrid method using CART algorithm and Binary Particle Swarm Optimization. Performance indicators that are used in this research are classification accuracy, error rate, sensitivity, specificity, and precision. Experimental results based on the public dataset showed that the proposed method accuracy is 78 % and 87.53 %. In compare to several popular algorithms, such as neural network, logistic regression and support vector machine, the proposed method showed an outstanding performance.
Ez36937941
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Analysis on Student Admission Enquiry System
Data mining referred to extracting the hidden predictive information from huge amount of data set. Recently, there are number of private institution are came into existence and they put their efforts to get fruitful admissions. In this paper, the techniques of data mining are used to analyze the mind setup of student after matriculate. One of the best tools of data mining is known as WEKA (Waikato Environment Knowledge Analysis), is used to formulate the process of analysis.
A statistical data fusion technique in virtual data integration environment
Data fusion in the virtual data integration environment starts after detecting and clustering duplicated
records from the different integrated data sources. It refers to the process of selecting or fusing attribute
values from the clustered duplicates into a single record representing the real world object. In this paper, a
statistical technique for data fusion is introduced based on some probabilistic scores from both data
sources and clustered duplicates
Jane Howard
The literature review examines whether big data analysis and data mining can substitute traditional data collection and analysis in healthcare. It summarizes 5 papers that discuss both the benefits and limitations of these new techniques. A key theme is that while data mining is useful for detecting patterns, the quality of the data being mined is paramount. Several papers note that data fragmentation, missing information, and incorrect entries still pose challenges. Proper data cleaning and management remain essential parts of the process that cannot be fully automated or replaced. The review concludes that both new technologies and traditional human data quality practices are needed to maximize the insights that can be gained.
Quality Assurance in Knowledge Data Warehouse
Knowledge discovery is the process of adding knowledge from a large amount of data. The quality of knowledge generated from the process of knowledge discovery greatly affects the results of the decisions obtained. Existing data must be qualified and tested to ensure knowledge discovery processes can produce knowledge or information that is useful and feasible. It deals with strategic decision making for an organization. Combining multiple operational databases and external data create data warehouse. This treatment is very vulnerable to incomplete, inconsistent, and noisy data. Data mining provides a mechanism to clear this deficiency before finally stored in the data warehouse. This research tries to give technique to improve the quality of information in the data warehouse.
Introduction to feature subset selection method
Data Mining is a computational progression to ascertain patterns in hefty data sets. It has various important techniques and one of them is Classification which is receiving great attention recently in the database community. Classification technique can solve several problems in different fields like medicine, industry, business, science. PSO is based on social behaviour for optimization problem. Feature Selection (FS) is a solution that involves finding a subset of prominent features to improve predictive accuracy and to remove the redundant features. Rough Set Theory (RST) is a mathematical tool which deals with the uncertainty and vagueness of the decision systems.
Ijcatr04061009
The objective of this study was to apply Data Mining in the analysis of imports of pharmaceutical products in Kenya with the aim of discovering patterns of association and correlation among the various product groups. The RapidMiner Data Mining was used to analyze data obtained from the Pharmacy and Poison Board, the regulator of pharmaceutical products in the country, covering the period 2008 to 2010. The CRISP method was used to get a business understanding of the Board, understand the nature of the data held, prepare the data for analyze and actual analysis of the data. The results of the study discovered various patterns through correlation and association analysis of various product groups. The results were presented through various graphs and discussed with the domain experts. These patterns are similar to prescription patterns from studies in Ethiopia, Nigeria and India. The research will provide regulators of pharmaceutical products, not only in Kenya but other African countries, a better insight into the patterns of imports and exports of pharmaceutical products. This would result into better controls, not only in import and exports of the products, but also enforcement on their usage in order to avert negative effects to the citizens
Effective data mining for proper
With the development of database, the data volume stored in database increases rapidly and in the large
amounts of data much important information is hidden. If the information can be extracted from the
database they will create a lot of profit for the organization. The question they are asking is how to extract
this value. The answer is data mining. There are many technologies available to data mining practitioners,
including Artificial Neural Networks, Genetics, Fuzzy logic and Decision Trees. Many practitioners are
wary of Neural Networks due to their black box nature, even though they have proven themselves in many
situations. This paper is an overview of artificial neural networks and questions their position as a
preferred tool by data mining practitioners.
IRJET- A Review of Data Cleaning and its Current Approaches
This document summarizes various approaches to data cleaning. It begins by stating that data cleaning is an important preprocessing step in data mining to detect and remove corrupted, inaccurate, or inconsistent data. It then reviews several common data cleaning techniques, including constraint-based data repairing, statistical methods, interactive approaches, Hadoop-based distributed cleaning, and clustering-based outlier detection. The document concludes that the appropriate cleaning approach depends on the type of data but the overall goal is to improve data quality and accuracy for downstream analysis and mining.
INTEGRATED ASSOCIATIVE CLASSIFICATION AND NEURAL NETWORK MODEL ENHANCED BY US...
The document summarizes a proposed methodology that integrates associative classification and neural networks for improved classification accuracy. It begins by introducing association rule mining and associative classification. It then describes using chi-squared analysis and the Gini index for attribute selection and rule pruning to generate a reduced set of rules. These rules are used to train a backpropagation neural network classifier. The methodology is tested on datasets from a public repository, demonstrating improved accuracy over traditional associative classification alone. Future work to integrate optical neural networks is also proposed.
Pharma data analytics
Presentation Alliance of European Life Sciences Law Firms
(Julian Hitchcock and Sofie van der Meulen) on legal aspects of big data in pharma. Topics: privacy, IP, medical devices and IVD.
[IJET-V1I3P10] Authors : Kalaignanam.K, Aishwarya.M, Vasantharaj.K, Kumaresan...
[IJET-V1I3P10] Authors : Kalaignanam.K, Aishwarya.M, Vasantharaj.K, Kumaresan...IJET - International Journal of Engineering and Techniques
Big data is to be implemented in as full way in real-time; it is still in a research. People
need to know what to do with enormous data. Insurance agencies are actively participating for the
analysis of patient's data which could be used to extract some useful information. Analysis is done in
term of discharge summary, drug & pharma, diagnostics details, doctor’s report, medical history,
allergies & insurance policies which are made by the application of map reduce and useful data is
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insurance policy details along with sanctioned amount, family grade wise segregation.
Keywords: Big data, Stemming, Map reduce Policy and Hadoop.Digital webinar master deck final
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PREDICTION OF MALIGNANCY IN SUSPECTED THYROID TUMOUR PATIENTS BY THREE DIFFER...
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Paper ID 216@ ICMLBDA 2023.pptx
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Data mining techniques play an important role in data analysis. For the construction of a classification model which could predict performance of students, particularly for engineering branches, a decision tree algorithm associated with the data mining techniques have been used in the research. A number of factors may affect the performance of students. Data mining technology which can related to this student grade well and we also used classification algorithms prediction. In this paper, we used educational data mining to predict students final grade based on their performance. We proposed student data classification using ID3 Iterative Dichotomiser 3 Decision Tree Algorithm Khin Khin Lay | San San Nwe "Using ID3 Decision Tree Algorithm to the Student Grade Analysis and Prediction" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26545.pdfPaper URL: https://www.ijtsrd.com/computer-science/data-miining/26545/using-id3-decision-tree-algorithm-to-the-student-grade-analysis-and-prediction/khin-khin-lay
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ICBAI Presentation (2)
- 1. Application of Machine Learning to Predict Outcome of US Court of Appeals Presented By: Krishna Mohan Nitin Hosurkar Pradeepta Mishra Thomson Reuters Arrow Electronics Ma Foi Analytics Indian Institute of Science (IISc), Bangalore India
- 2. 12/21/2016Indian Institute of Science, Bangalore
- 3. Background Andrew Martin Theodore RugerPauline Kim 12/21/2016Indian Institute of Science, Bangalore
- 4. Process 12/21/2016Indian Institute of Science, Bangalore
- 5. US Court System Source: http://www.slideshare.net/bmtoth/organization-of-us-court-system U.S. Court of Appeals 12/21/2016Indian Institute of Science, Bangalore
- 6. Problem Statement Predict the outcome or treatment of a case by the US Court of Appeals based on historical data • Basic case characteristics • Participants • Nature of the case • Judges 12/21/2016Indian Institute of Science, Bangalore
- 7. Data Source and Characteristics Website: http://artsandsciences.sc.edu/poli/juri/appct.htm Codebook: http://artsandsciences.sc.edu/poli/juri/KH_update_codebook.pdf Data: http://www.cas.sc.edu/poli/juri/KH_update_stata.zip • 2,160 Rows • 244 Columns • 12 possible outcomes • Integers + Dates + Categories + Binaries + Complex variables • Majority Categorical variables • Multi-level Factor variables • Coded Factor values • Missing values 12/21/2016Indian Institute of Science, Bangalore
- 8. Project Plan 12/21/2016Indian Institute of Science, Bangalore
- 9. Data Prep MissingValues • Remove variables with more than 15% missing values 244 204 Decompose Complex Fields • Consists of 5-digit variables • Each digit represents a categorical value • As many as 12 sub-categories with different combinations Change Column names for better understanding Change levels from Numbers to Description 12/21/2016Indian Institute of Science, Bangalore
- 10. Schematic Data Organization 12/21/2016Indian Institute of Science, Bangalore
- 11. Dimensionality Reduction Chi-Square HypothesisTesting H0 =The predictor and the target variable (CaseTreatment) are independent of each other HA =The predictor and the target variable (CaseTreatment) are dependent / related 12/21/2016Indian Institute of Science, Bangalore
- 12. Dimensionality Reduction Feature Selection using Boruta Package in R 12/21/2016Indian Institute of Science, Bangalore
- 13. Model Selection Criteria • Multi-variate output • Categorical predictor and target variables • Multi-level factors • Size of dataset Models selected • Random Forest • Neural Network • XG Boost • Ensemble 12/21/2016Indian Institute of Science, Bangalore
- 14. Neural Network Neural Network Model Tuning FullTraining Data Accuracy: 0.62 TrimmedTraining Data Accuracy: 0.64 Not much improvement Training Data with Oversampling Accuracy: 0.65 Test Data Accuracy: 0.85 Big Improvement BalancedTrimmedTraining Data Accuracy: 0.82 Good Performance 12/21/2016Indian Institute of Science, Bangalore
- 15. XGBoostXG Boost ModelTuning FullTraining Data Accuracy: 0.69 TrimmedTraining Data Accuracy: 0.7 Not much improvement TrimmedTraining Data with Oversampling Accuracy: 0.94 Big improvement Test Data Accuracy: 0.91 Good Performance 12/21/2016Indian Institute of Science, Bangalore
- 16. Observations Neural Network XG Boost Misclassification Petition Denied as Affirmed Reversed as Affirmed Vacated as Affirmed Affirmed as Reversed Isolate factors contributing to misclassification 12/21/2016Indian Institute of Science, Bangalore
- 17. Observations & Conclusions Adequate representation of all outcomes in Multinomial Classification Models Most SignificantVariables • Circuit Court – Appeals court that is currently hearing the case • Verdict of the previous court as ‘Not Ascertained’ • Nature of the Appellant – Natural citizen has greater significance • Directionality of 3rd Judge - Assertion for broadest interpretation of First Amendment protection – Freedom of Speech, Religion and Peaceful Protest • Initiator –Who initiated the Appeal – original plaintiff or others Important!! 12/21/2016Indian Institute of Science, Bangalore
- 18. THANK YOU 12/21/2016Indian Institute of Science, Bangalore