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This document summarizes a heart disease data analysis project. It discusses data analysis steps like data exploration, cleaning, and model building. The analysis uses a heart disease dataset from Kaggle with 13 independent variables and 1 dependent variable indicating the presence or absence of heart disease. Various algorithms are tested on training and test splits of the data, with random forest classification found to have the best accuracy in predicting heart disease.
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HEALTH PREDICTION ANALYSIS USING DATA MINING
As we know that health care industry is completely based on assumptions, which after get tested and verified via various tests and patient have to be depend on the doctors knowledge on that topic . so we made a system that uses data mining techniques to predict the health of a person based on various medical test results. so we can predict the health of that person based on that analysis performed by the system.The system currently design only for heart issues, for that we had used Statlog (Heart) Data Set from UCI Machine Learning Repository it includes attributes like age, sex, chest pain type, cholesterol, sugar, outcomes,etc.for training the system. we only need to passed few general inputs in order to generate the prediction and the prediction results from all algorithms are they merged together by calculating there mean value that value shows the actual outcome of the prediction process which entirely works in background
Short story_2.pptx
This document proposes a hybrid machine learning model to predict heart disease. It uses a dataset from UCI with 303 instances and 14 attributes on factors like age, sex, cholesterol levels, etc. It compares the performance of decision tree, random forest, and a hybrid model combining the two. The hybrid model achieves the best accuracy of 88.7% for heart disease prediction. It discusses implementing the models in Python using libraries like sklearn. The results show random forest and hybrid models more accurately detect cardiovascular issues compared to decision trees alone. The proposed hybrid model aims to improve uniqueness and optimization for heart disease risk prediction.
Short story.pptx
This document describes a study that uses a hybrid machine learning model to predict heart disease. It tests three algorithms - Random Forest, Decision Tree, and a hybrid model combining Random Forest and Decision Tree. The hybrid model achieved the highest accuracy rate of 88.7% according to experimental results. The study uses the Cleveland heart disease dataset and implements the algorithms in Python using scikit-learn libraries. It finds that the hybrid and Random Forest models achieve similar high accuracy rates of around 88%, while the Decision Tree model achieves around 79% accuracy. The hybrid model architecture and workflow are described. Results show performance metrics for each model. The study concludes the hybrid model is effective for heart disease prediction and suggests future work exploring deep learning techniques and
Clinical_Decision_Support_For_Heart_Disease
This document describes building a clinical decision support system using a Naive Bayes classification model to predict the presence of heart disease. The model was trained on 303 patient records from the Cleveland Clinic dataset and tested on 123 records from the Switzerland dataset. The Naive Bayes model achieved 83.5% accuracy on the training data but lower 70.73% accuracy on the test data, likely due to overfitting and missing data. While not perfect, the model provides a first-level diagnosis of heart disease presence in patients to assist healthcare providers.
Data science notes for ASDS calicut 2.pptx
Data science involves both statistics and practical hacking skills. It is the engineering of data - applying tools and theoretical understanding to data in a practical way. Statistical modeling is the process of using mathematical models to analyze and understand data in order to make general predictions. There are several statistical modeling techniques including linear regression, classification, resampling, non-linear models, tree-based methods, and neural networks. Unsupervised learning identifies patterns in data without pre-existing categories by techniques like clustering. Time series forecasting predicts future values based on patterns in historical time series data.
A Heart Disease Prediction Model using Logistic Regression
The early prognosis of cardiovascular diseases can aid in making decisions to lifestyle changes in high risk patients and in turn reduce their complications. Research has attempted to pinpoint the most influential factors of heart disease as well as accurately predict the overall risk using homogenous data mining techniques. Recent research has delved into amalgamating these techniques using approaches such as hybrid data mining algorithms. This paper proposes a rule based model to compare the accuracies of applying rules to the individual results of logistic regression on the Cleveland Heart Disease Database in order to present an accurate model of predicting heart disease. K. Sandhya Rani | M. Sai Manoj | G. Suguna Mani"A Heart Disease Prediction Model using Logistic Regression" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd11401.pdf http://www.ijtsrd.com/computer-science/data-miining/11401/a-heart-disease-prediction-model-using-logistic-regression/k-sandhya-rani
IRJET- Predicting Heart Disease using Machine Learning Algorithm
1) The document discusses using machine learning algorithms like Naive Bayes and Decision Trees to predict heart disease using a dataset from Kaggle.
2) It describes preprocessing the dataset, training models, and evaluating accuracy. Decision Trees were found to more accurately predict heart disease than Naive Bayes.
3) The models use 13 attributes like age, sex, cholesterol levels, and examine classification performance to identify individuals at risk of heart disease.
IRJET- A Survey on Prediction of Heart Disease Presence using Data Mining and...
This document summarizes a survey on using data mining and machine learning techniques to predict heart disease. It begins with an abstract stating that heart disease is a leading cause of death worldwide and that earlier detection through technology could help reduce serious cases. The document then provides an overview of data mining, machine learning, and classification algorithms that have been used by researchers to predict heart disease. It describes a commonly used heart disease dataset and discusses evaluation measures for comparing classification algorithms to identify the best model for predicting heart disease.
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HEALTH PREDICTION ANALYSIS USING DATA MINING
As we know that health care industry is completely based on assumptions, which after get tested and verified via various tests and patient have to be depend on the doctors knowledge on that topic . so we made a system that uses data mining techniques to predict the health of a person based on various medical test results. so we can predict the health of that person based on that analysis performed by the system.The system currently design only for heart issues, for that we had used Statlog (Heart) Data Set from UCI Machine Learning Repository it includes attributes like age, sex, chest pain type, cholesterol, sugar, outcomes,etc.for training the system. we only need to passed few general inputs in order to generate the prediction and the prediction results from all algorithms are they merged together by calculating there mean value that value shows the actual outcome of the prediction process which entirely works in background
Short story_2.pptx
This document proposes a hybrid machine learning model to predict heart disease. It uses a dataset from UCI with 303 instances and 14 attributes on factors like age, sex, cholesterol levels, etc. It compares the performance of decision tree, random forest, and a hybrid model combining the two. The hybrid model achieves the best accuracy of 88.7% for heart disease prediction. It discusses implementing the models in Python using libraries like sklearn. The results show random forest and hybrid models more accurately detect cardiovascular issues compared to decision trees alone. The proposed hybrid model aims to improve uniqueness and optimization for heart disease risk prediction.
Short story.pptx
This document describes a study that uses a hybrid machine learning model to predict heart disease. It tests three algorithms - Random Forest, Decision Tree, and a hybrid model combining Random Forest and Decision Tree. The hybrid model achieved the highest accuracy rate of 88.7% according to experimental results. The study uses the Cleveland heart disease dataset and implements the algorithms in Python using scikit-learn libraries. It finds that the hybrid and Random Forest models achieve similar high accuracy rates of around 88%, while the Decision Tree model achieves around 79% accuracy. The hybrid model architecture and workflow are described. Results show performance metrics for each model. The study concludes the hybrid model is effective for heart disease prediction and suggests future work exploring deep learning techniques and
Clinical_Decision_Support_For_Heart_Disease
This document describes building a clinical decision support system using a Naive Bayes classification model to predict the presence of heart disease. The model was trained on 303 patient records from the Cleveland Clinic dataset and tested on 123 records from the Switzerland dataset. The Naive Bayes model achieved 83.5% accuracy on the training data but lower 70.73% accuracy on the test data, likely due to overfitting and missing data. While not perfect, the model provides a first-level diagnosis of heart disease presence in patients to assist healthcare providers.
Data science notes for ASDS calicut 2.pptx
Data science involves both statistics and practical hacking skills. It is the engineering of data - applying tools and theoretical understanding to data in a practical way. Statistical modeling is the process of using mathematical models to analyze and understand data in order to make general predictions. There are several statistical modeling techniques including linear regression, classification, resampling, non-linear models, tree-based methods, and neural networks. Unsupervised learning identifies patterns in data without pre-existing categories by techniques like clustering. Time series forecasting predicts future values based on patterns in historical time series data.
A Heart Disease Prediction Model using Logistic Regression
The early prognosis of cardiovascular diseases can aid in making decisions to lifestyle changes in high risk patients and in turn reduce their complications. Research has attempted to pinpoint the most influential factors of heart disease as well as accurately predict the overall risk using homogenous data mining techniques. Recent research has delved into amalgamating these techniques using approaches such as hybrid data mining algorithms. This paper proposes a rule based model to compare the accuracies of applying rules to the individual results of logistic regression on the Cleveland Heart Disease Database in order to present an accurate model of predicting heart disease. K. Sandhya Rani | M. Sai Manoj | G. Suguna Mani"A Heart Disease Prediction Model using Logistic Regression" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd11401.pdf http://www.ijtsrd.com/computer-science/data-miining/11401/a-heart-disease-prediction-model-using-logistic-regression/k-sandhya-rani
IRJET- Predicting Heart Disease using Machine Learning Algorithm
1) The document discusses using machine learning algorithms like Naive Bayes and Decision Trees to predict heart disease using a dataset from Kaggle.
2) It describes preprocessing the dataset, training models, and evaluating accuracy. Decision Trees were found to more accurately predict heart disease than Naive Bayes.
3) The models use 13 attributes like age, sex, cholesterol levels, and examine classification performance to identify individuals at risk of heart disease.
IRJET- A Survey on Prediction of Heart Disease Presence using Data Mining and...
This document summarizes a survey on using data mining and machine learning techniques to predict heart disease. It begins with an abstract stating that heart disease is a leading cause of death worldwide and that earlier detection through technology could help reduce serious cases. The document then provides an overview of data mining, machine learning, and classification algorithms that have been used by researchers to predict heart disease. It describes a commonly used heart disease dataset and discusses evaluation measures for comparing classification algorithms to identify the best model for predicting heart disease.
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This document describes a project that uses machine learning algorithms to predict multiple diseases based on user-provided symptoms. It proposes building a system that can predict diseases like diabetes, malaria, jaundice, dengue, and tuberculosis using linear regression and decision tree models. The system would take symptoms as input and output the probability of different diseases. It discusses the existing process for disease diagnosis and how the proposed system could save time. It also outlines the modules needed to develop the system, including collecting and preparing data, choosing models, training models, evaluating performance, and making predictions.
Machine Learning.pptx
1) Machine learning involves analyzing data to find patterns and make predictions. It uses mathematics, statistics, and programming.
2) Key aspects of machine learning include understanding the business problem, collecting and preparing data, building and evaluating models, and different types of machine learning algorithms like supervised, unsupervised, and reinforcement learning.
3) Common machine learning algorithms discussed include linear regression, logistic regression, KNN, K-means clustering, decision trees, and handling issues like missing values, outliers, and feature engineering.
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This document describes research on improving the accuracy of heart disease prediction using hybrid machine learning techniques. The researchers collected data on patient biomarkers and risk factors from hospitals and online repositories. They applied data preprocessing, feature selection, and various classification models like decision trees, support vector machines, random forests, and K-nearest neighbors. Evaluating the models showed that a hybrid of fuzzy K-nearest neighbor and K-nearest neighbor achieved the highest accuracy rate of 94% for heart disease prediction. The researchers then built a web application using this hybrid model to allow users to predict their risk of heart disease online with high accuracy. The study demonstrates that machine learning can effectively analyze medical data and help predict diseases.
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This document describes a project that uses machine learning algorithms to predict multiple diseases based on user-provided symptoms. It proposes building a system that can predict diseases like diabetes, malaria, jaundice, dengue, and tuberculosis using linear regression and decision tree models. The system would take symptoms as input and output the probability of different diseases. It discusses the existing process for disease diagnosis and how the proposed system could save time. It also outlines the modules needed to develop the system, including collecting and preparing data, choosing models, training models, evaluating performance, and making predictions.
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of the system and it is most significant to detect the botnets. However, peer-to-peer (P2P) structured botnets are very difficult to detect
because, it doesn’t have any centralized server. In this paper, we deliver an infrastructure of P2P that will improve the trust of the peers
and its data. In order to identify the botnets we provide a technique called data provenance integrity. It will ensure the correct origin or
source of information and prevents opponents from using host resources. A reputation based trust model is used for selecting the
trusted peer. In this model, each peer has a reputation value which is calculated based on its past activity. Here a hash table is used for
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C omparative S tudy of D iabetic P atient D ata’s U sing C lassification A lg...
Data mining refers to extracting knowledge from large amount of data. Real life data mining approaches are
interesting because they often present a different se
t of problems for
diabetic
patient’s
data
.
The
research area to solve
various problems and classification is one of main problem in the field. The research describes algorithmic discussion of J48
,
J48 Graft, Random tree, REP, LAD. Here used to compare the
performance of computing time, correctly classified
instances, kappa statistics, MAE, RMSE, RAE, RRSE and
to find the error rate measurement for different classifiers in
weka .In this paper the
data
classification is diabetic patients data set is develope
d by collecting data from hospital repository
consists of 1865 instances with different attributes. The instances in the dataset are two categories of blood tests, urine t
ests.
Weka tool is used to classify the data is evaluated using 10 fold cross validat
ion and the results are compared. When the
performance of algorithms
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we found J48 is better algorithm in most of the cases
Comparative Study of Diabetic Patient Data’s Using Classification Algorithm i...
Data mining refers to extracting knowledge from large amount of data. Real life data mining approaches are
interesting because they often present a different set of problems for diabetic patient’s data. The research area to solve
various problems and classification is one of main problem in the field. The research describes algorithmic discussion of J48,
J48 Graft, Random tree, REP, LAD. Here used to compare the performance of computing time, correctly classified
instances, kappa statistics, MAE, RMSE, RAE, RRSE and to find the error rate measurement for different classifiers in
weka .In this paper the data classification is diabetic patients data set is developed by collecting data from hospital repository
consists of 1865 instances with different attributes. The instances in the dataset are two categories of blood tests, urine tests.
Weka tool is used to classify the data is evaluated using 10 fold cross validation and the results are compared. When the
performance of algorithms, we found J48 is better algorithm in most of the cases.
Hybrid Technique for Associative Classification of Heart Diseases
The document discusses a hybrid technique for associative classification of heart diseases using data mining. It summarizes existing classification and association rule mining algorithms applied to heart disease data. The author aims to improve accuracy by generating classification association rules efficiently and integrating classification with association rule mining. The proposed approach is implemented in Weka to extract rules from a heart disease dataset using Apriori and FP-Growth algorithms. The rules are used to classify patients and evaluate the performance compared to other methods.
E04733639
This document presents a comparative study of different data mining classification techniques for predicting heart diseases. It analyzes Naive Bayes, Decision Table, and J48 classifiers on a heart disease dataset from a UCI repository. The Naive Bayes technique achieved the highest accuracy of 83.4% and was the fastest to build the decision tree, taking only 0.01 seconds. Decision Table had the lowest accuracy of 76.2%. The study aims to show how data mining techniques can help in heart disease research and improve healthcare quality by accurately predicting heart conditions.
Chronic Kidney Disease Prediction Using Machine Learning
Chronic Kidney Disease prediction is one of the most important issues in healthcare analytics. The most interesting and challenging tasks in day to day life is prediction in medical field. In this paper, we employ some machine learning techniques for predicting the chronic kidney disease using clinical data. We use three machine learning algorithms such as Decision Tree(DT) algorithm, Naive Bayesian (NB) algorithm. The performance of the above models are compared with each other in order to select the best classifier in predicting the chronic kidney disease for given dataset.
Heart disease classification using Random Forest
This document presents research on using the random forest machine learning algorithm to classify and predict heart disease.
The researchers used a dataset of 303 patient records combining data from 4 databases to train and test their random forest model. They achieved a classification accuracy of 86.9% and diagnosis rate of 93.3% when predicting heart disease risk.
Future work could include improving data quality, collecting a larger and more diverse dataset, further optimizing model hyperparameters, and developing classifiers to predict specific heart conditions rather than a general heart disease risk prediction. The goal of this research is to develop an accurate and efficient heart disease prediction tool to help doctors diagnose diseases early and improve patient outcomes.
Heart Disease Prediction Using Data Mining Techniques
There are huge amounts of data in the medical industry which is not processed properly and hence cannot be used effectively in making decisions. We can use data mining techniques to mine these patterns and relationships. This research has developed a prototype Heart Disease Prediction using data mining techniques, namely Neural Network, K-Means Clustering and Frequent Item Set Generation. Using medical profiles such as age, sex, blood pressure and blood sugar it can predict the likelihood patients getting a heart disease. It enables significant knowledge, e.g. patterns, relationships between medical factors related to heart disease to be established. Performance of these techniques is compared through sensitivity, specificity and accuracy. It has been observed that Artificial Neural Networks outperform K Means clustering in all the parameters i.e. Sensitivity, Specificity and Accuracy.
Guide to data analytics
Data science has become essential for businesses to analyze large amounts of data. While machine learning seems promising, there are no shortcuts - data exploration is needed to improve model accuracy. The key steps of data exploration are identifying variables, univariate and bivariate analysis, and treating missing values and outliers to clean the data before building predictive models. Understanding the data generation process is important for leaders to evaluate the quality of analytics.
Ijcatr04041015
This document describes a study that developed an Android application to predict and suggest measures for diabetes using data mining techniques. The study used the Pima Indian diabetes dataset to build a decision tree classification model using the C4.5 algorithm to predict whether a person is diabetic or not based on their attributes. The most significant attributes identified for prediction were plasma glucose level, body mass index, diabetes pedigree function, and insulin level. The developed Android app allows a user to enter their details, which are then run through the decision tree model to predict their diabetes status and provide suggested measures if predicted to be diabetic. The goal of the study was to create a mobile application to help individuals assess their risk of diabetes and maintain healthy habits.
Singular Value Decomposition (SVD).pptx
1. Singular Value Decomposition (SVD) is a matrix factorization technique that decomposes a matrix into three other matrices.
2. SVD is primarily used for dimensionality reduction, information extraction, and noise reduction.
3. Key applications of SVD include matrix approximation, principal component analysis, image compression, recommendation systems, and signal processing.
EDAB Module 5 Singular Value Decomposition (SVD).pptx
1. Singular Value Decomposition (SVD) is a matrix factorization technique that decomposes a matrix into three other matrices.
2. SVD is primarily used for dimensionality reduction, information extraction, and noise reduction.
3. Key applications of SVD include matrix approximation, principal component analysis, image compression, recommendation systems, and signal processing.
Heart disease prediction system
A major challenge facing healthcare organizations (hospitals, medical centers) is
the provision of quality services at affordable costs. Quality service implies diagnosing
patients correctly and administering treatments that are effective. Poor clinical decisions
can lead to disastrous consequences which are therefore unacceptable. Hospitals must
also minimize the cost of clinical tests. They can achieve these results by employing
appropriate computer-based information and/or decision support systems.
Most hospitals today employ some sort of hospital information systems to manage
their healthcare or patient data.
These systems are designed to support patient billing, inventory management and generation of simple statistics. Some hospitals use decision support systems, but they are largely limited. Clinical decisions are often made based on doctors’ intuition and experience rather than on the knowledge rich data hidden in the database.
This practice leads to unwanted biases, errors and excessive medical costs which affects the quality of service provided to patients.
Dissertation
The document discusses random forest algorithms and their use in machine learning applications such as predicting breast cancer risk. It provides an overview of random forest methods, important hyperparameters that can improve predictive ability or increase speed, and advantages such as being robust, parallelizable, and handling unbalanced data. It also discusses some drawbacks like lower interpretability. The document then describes applying random forests to a dataset from the PLCO study to predict breast cancer risk, including data preprocessing steps and comparing models using different input variables.
Cancer detection using data mining
Cancer is a disease caused by abnormal cell growth and can affect different cell types. This paper focuses on breast cancer, which is classified based on the type of affected cell. There are several risk factors that can increase the likelihood of developing cancer, including gender, age, genetics, family history, weight, alcohol use, and smoking. The dataset used contains information from 569 breast cancer cases, including demographic and cell feature data. Machine learning algorithms like decision trees can be applied to build models for diagnosing breast cancer based on these attributes with up to 96.46% accuracy.
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IRJET -Improving the Accuracy of the Heart Disease Prediction using Hybrid Ma...
Kidney Failure Due to Diabetics – Detection using Classification Algorithm in...
Kidney Failure Due to Diabetics – Detection using Classification Algorithm in...
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C omparative S tudy of D iabetic P atient D ata’s U sing C lassification A lg...
Comparative Study of Diabetic Patient Data’s Using Classification Algorithm i...
Comparative Study of Diabetic Patient Data’s Using Classification Algorithm i...
Hybrid Technique for Associative Classification of Heart Diseases
Hybrid Technique for Associative Classification of Heart Diseases
Chronic Kidney Disease Prediction Using Machine Learning
Chronic Kidney Disease Prediction Using Machine Learning
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Heart Disease Prediction Using Data Mining Techniques
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Build applications with generative AI on Google Cloud
We will explore Vertex AI - Model Garden powered experiences, we are going to learn more about the integration of these generative AI APIs. We are going to see in action what the Gemini family of generative models are for developers to build and deploy AI-driven applications. Vertex AI includes a suite of foundation models, these are referred to as the PaLM and Gemini family of generative ai models, and they come in different versions. We are going to cover how to use via API to: - execute prompts in text and chat - cover multimodal use cases with image prompts. - finetune and distill to improve knowledge domains - run function calls with foundation models to optimize them for specific tasks. At the end of the session, developers will understand how to innovate with generative AI and develop apps using the generative ai industry trends.
在线办理(英国UCA毕业证书)创意艺术大学毕业证在读证明一模一样
学校原件一模一样【微信：741003700 】《(英国UCA毕业证书)创意艺术大学毕业证》【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
"Financial Odyssey: Navigating Past Performance Through Diverse Analytical Lens"
Embark on a captivating financial journey with 'Financial Odyssey,' our hackathon project. Delve deep into the past performance of two companies as we employ an array of financial statement analysis techniques. From ratio analysis to trend analysis, uncover insights crucial for informed decision-making in the dynamic world of finance."
一比一原版南十字星大学毕业证(SCU毕业证书)学历如何办理
原版办理【微信号:BYZS866】【南十字星大学毕业证(SCU毕业证书)】【微信号:BYZS866】《成绩单、外壳、雅思、offer、真实留信官方学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路）我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信号BYZS866】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信号BYZS866】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
Experts live - Improving user adoption with AI
Bekijk de slides van onze sessie Enhancing Modern Workplace Efficiency op Experts Live 2024.
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
by
Timothy Spann
Principal Developer Advocate
https://budapestdata.hu/2024/en/
https://budapestml.hu/2024/en/
tim.spann@zilliz.com
https://www.linkedin.com/in/timothyspann/
https://x.com/paasdev
https://github.com/tspannhw
https://www.youtube.com/@flank-stack
milvus
vector database
gen ai
generative ai
deep learning
machine learning
apache nifi
apache pulsar
apache kafka
apache flink
一比一原版英属哥伦比亚大学毕业证(UBC毕业证书)学历如何办理
原版办【微信号:BYZS866】【英属哥伦比亚大学毕业证(UBC毕业证书)】【微信号:BYZS866】《成绩单、外壳、雅思、offer、真实留信官方学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路）我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信号BYZS866】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信号BYZS866】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
UofT毕业证如何办理
原件一模一样【微信：95270640】【多伦多大学毕业证UofT学位证成绩单】【微信：95270640】（留信学历认证永久存档查询）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信：95270640】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信：95270640】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份【微信：95270640】
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
→ 【关于价格问题（保证一手价格）
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部官方认证查询网站查询到认证通过结果后付款，不成功不收费！
办理多伦多大学毕业证硕士毕业证成绩单UofT学位证【微信：95270640 】外观非常精致，由特殊纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理多伦多大学毕业证UofT学位证硕士毕业证成绩单【微信：95270640 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理多伦多大学毕业证硕士毕业证成绩单UofT学位证【微信：95270640 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理多伦多大学毕业证硕士毕业证成绩单UofT学位证【微信：95270640 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
Population Growth in Bataan: The effects of population growth around rural pl...
A population analysis specific to Bataan.
University of New South Wales degree offer diploma Transcript
澳洲UNSW毕业证书制作新南威尔士大学假文凭定制Q微168899991做UNSW留信网教留服认证海牙认证改UNSW成绩单GPA做UNSW假学位证假文凭高仿毕业证申请新南威尔士大学University of New South Wales degree offer diploma Transcript
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
原版制作(unimelb毕业证书)墨尔本大学毕业证Offer一模一样
学校原件一模一样【微信：741003700 】《(unimelb毕业证书)墨尔本大学毕业证》【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
一比一原版(UO毕业证)渥太华大学毕业证如何办理
UO毕业证录取书【微信95270640】购买（渥太华大学毕业证成绩单硕士学历）Q微信95270640代办UO学历认证留信网伪造渥太华大学学位证书精仿渥太华大学本科/硕士文凭证书补办渥太华大学 diplomaoffer,Transcript购买渥太华大学毕业证成绩单购买UO假毕业证学位证书购买伪造渥太华大学文凭证书学位证书,专业办理雅思、托福成绩单，学生ID卡，在读证明，海外各大学offer录取通知书，毕业证书，成绩单，文凭等材料:1:1完美还原毕业证、offer录取通知书、学生卡等各种在读或毕业材料的防伪工艺（包括 烫金、烫银、钢印、底纹、凹凸版、水印、防伪光标、热敏防伪、文字图案浮雕，激光镭射，紫外荧光，温感光标）学校原版上有的工艺我们一样不会少，不论是老版本还是最新版本，都能保证最高程度还原，力争完美以求让所有同学都能享受到完美的品质服务。
文凭办理流程：
1客户提供办理信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：微信95270640我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）。
7完成交易删除客户资料
高精端提供以下服务：
一：渥太华大学渥太华大学毕业证文凭证书全套材料从防伪到印刷水印底纹到钢印烫金
二：真实使馆认证（留学人员回国证明）使馆存档
三：真实教育部认证教育部存档教育部留服网站可查
四：留信认证留学生信息网站可查
五：与学校颁发的相关证件1:1纸质尺寸制定（定期向各大院校毕业生购买最新版本毕,业证成绩单保证您拿到的是鲁昂大学内部最新版本毕业证成绩单微信95270640）
A.为什么留学生需要操作留信认证?
留信认证全称全国留学生信息服务网认证,隶属于北京中科院。①留信认证门槛条件更低,费用更美丽,并且包过,完单周期短,效率高②留信认证虽然不能去国企,但是一般的公司都没有问题,因为国内很多公司连基本的留学生学历认证都不了解。这对于留学生来说,这就比自己光拿一个证书更有说服力,因为留学学历可以在留信网站上进行查询!
B.为什么我们提供的毕业证成绩单具有使用价值？
查询留服认证是国内鉴别留学生海外学历的唯一途径但认证只是个体行为不是所有留学生都操作所以没有办理认证的留学生的学历在国内也是查询不到的他们也仅仅只有一张文凭。所以这时候我们提供的和学校颁发的一模一样的毕业证成绩单就有了使用价值。只硕大的蛇皮袋手里拎着长铁钩正站在门口朝黑色的屋内张望不好坏人小偷山娃一怔却也灵机一动立马仰起头双手拢在嘴边朝楼上大喊：“爸爸爸——有人找——那人一听朝山娃尴尬地笑笑悻悻地走了山娃立马“嘭的一声将铁门锁死心却咚咚地乱跳当山娃跟父亲说起这事时父亲很吃惊抚摸着山娃的头说还好醒得及时要不家早被人掏空了到时连电视也没得看啰不过父亲还是夸山娃能临危不乱随机应变有胆有谋山娃笑笑说那都是书上学的看童话和小说时多
The Ipsos - AI - Monitor 2024 Report.pdf
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
一比一原版(UCSF文凭证书)旧金山分校毕业证如何办理
毕业原版【微信:176555708】【(UCSF毕业证书)旧金山分校毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
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Rishat ML ppt.pptx
- 1. Topic : Heart Disease Analysis Name : Rishat Dadial UID : 22MSM40191 Course : MSc Data Science Semester : 2nd Section : MSD-3
- 2. What is Data Analysis? Data analysis is the process of cleaning, changing, and processing raw data and extracting actionable, relevant information that helps businesses make informed decisions. The procedure helps reduce the risks inherent in decision- making by providing useful insights and statistics, often presented in charts, images, tables, and graphs.
- 3. Steps followed by data analysis Data Exploration: It is the initial step where we learn about the dataset’s content and characteristics. We know about the size of the dataset. We know if the dataset contain having any missing value or contain any outliers. Data cleaning: This step involves handing of missing values and outliers, removing unwanted variable from the dataset that are not suitable for our analysis. Modal building: Once we get the cleaned data, now its time to divide the dataset into training and testing set and apply the suitable algorithm accord. to the nature of the problem and type of dataset we have. Performance: At last check the performance of the modal on unseen data.
- 4. Heart Disease Data Analysis In this analysis we have to predict whether the person is having the cancer or not and for the data we have taken the dataset from Kaggle and perform the analysis. In this there are 13 independent variable and 1 dependent variable. ‘0’ is used person not having the cancer and ‘1’ is used person having the cancer
- 5. Heart Disease Data Analysis Load the dataset
- 6. Heart Disease Data Analysis Use of head() to load the 1st five rows of dataset
- 7. Heart Disease Data Analysis Detect and remove the duplicate the rows from the dataset
- 8. Heart Disease Data Analysis Check if the dataset is evenly distribute or not.
- 9. Heart Disease Data Analysis Check the correlation of independent variable with dependent variable.
- 10. Heart Disease Data Analysis Now split the dataset into training and testing set
- 11. Build the model On this dataset we have perform various algorithms like logistic regression, decision tree and random forest and the modal which give the best accuaracy is “Random Forest classifer”
- 12. Thank You Have A Great Day