This document discusses a project that uses machine learning algorithms to predict potential heart diseases. The project uses a dataset with 13 features and applies algorithms like K-Nearest Neighbors Classifier and Support Vector Classifier, with and without PCA. The K-Nearest Neighbors Classifier achieved the best accuracy score of 87% at predicting heart disease based on the dataset.
Data Science - Part III - EDA & Model SelectionDerek Kane
This lecture introduces the concept of EDA, understanding, and working with data for machine learning and predictive analysis. The lecture is designed for anyone who wants to understand how to work with data and does not get into the mathematics. We will discuss how to utilize summary statistics, diagnostic plots, data transformations, variable selection techniques including principal component analysis, and finally get into the concept of model selection.
Data Science - Part V - Decision Trees & Random Forests Derek Kane
This lecture provides an overview of decision tree machine learning algorithms and random forest ensemble techniques. The practical example includes diagnosing Type II diabetes and evaluating customer churn in the telecommunication industry.
Data Science - Part III - EDA & Model SelectionDerek Kane
This lecture introduces the concept of EDA, understanding, and working with data for machine learning and predictive analysis. The lecture is designed for anyone who wants to understand how to work with data and does not get into the mathematics. We will discuss how to utilize summary statistics, diagnostic plots, data transformations, variable selection techniques including principal component analysis, and finally get into the concept of model selection.
Data Science - Part V - Decision Trees & Random Forests Derek Kane
This lecture provides an overview of decision tree machine learning algorithms and random forest ensemble techniques. The practical example includes diagnosing Type II diabetes and evaluating customer churn in the telecommunication industry.
Machine Learning and Real-World ApplicationsMachinePulse
This presentation was created by Ajay, Machine Learning Scientist at MachinePulse, to present at a Meetup on Jan. 30, 2015. These slides provide an overview of widely used machine learning algorithms. The slides conclude with examples of real world applications.
Ajay Ramaseshan, is a Machine Learning Scientist at MachinePulse. He holds a Bachelors degree in Computer Science from NITK, Suratkhal and a Master in Machine Learning and Data Mining from Aalto University School of Science, Finland. He has extensive experience in the machine learning domain and has dealt with various real world problems.
Principal Component Analysis, or PCA, is a factual method that permits you to sum up the data contained in enormous information tables by methods for a littler arrangement of "synopsis files" that can be all the more handily envisioned and broke down.
A ppt based on predicting prices of houses. Also tells about basics of machine learning and the algorithm used to predict those prices by using regression technique.
Provides a brief overview of what machine learning is, how it works (theory), how to prepare data for a machine learning problem, an example case study, and additional resources.
Data Trend Analysis by Assigning Polynomial Function For Given Data SetIJCERT
This paper aims at explaining the method of creating a polynomial equation out of the given data set which can be used as a representation of the data itself and can be used to run aggregation against itself to find the results. This approach uses least-squares technique to construct a model of data and fit to a polynomial. Differential calculus technique is used on this equation to generate the aggregated results that represents the original data set.
Steering Model Selection with Visual Diagnostics: Women in Analytics 2019Rebecca Bilbro
Machine learning is ultimately a search for the best combination of features, algorithm, and hyperparameters that result in the best performing model. Oftentimes, this leads us to stay in our algorithmic comfort zones, or to resort to automated processes such as grid searches and random walks. Whether we stick to what we know or try many combinations, we are sometimes left wondering if we have actually succeeded.
By enhancing model selection with visual diagnostics, data scientists can inject human guidance to steer the search process. Visualizing feature transformations, algorithmic behavior, cross-validation methods, and model performance allows us a peek into the high dimensional realm that our models operate. As we continue to tune our models, trying to minimize both bias and variance, these glimpses allow us to be more strategic in our choices. The result is more effective modeling, speedier results, and greater understanding of underlying processes.
Visualization is an integral part of the data science workflow, but visual diagnostics are directly tied to machine learning transformers and models. The Yellowbrick library extends the scikit-learn API providing a Visualizer object, an estimator that learns from data and produces a visualization as a result. In this tutorial, we will explore feature visualizers, visualizers for classification, clustering, and regression, as well as model analysis visualizers. We'll work through several examples and show how visual diagnostics steer model selection, making machine learning more informed, and more effective.
Selecting the Right Type of Algorithm for Various Applications - PhdassistancePhD Assistance
Machine learning algorithms may be classified mainly into three main types. Supervised learning constructs a mathematical model from the training data, including input and output labels. The techniques of data categorization and regression are deemed supervised learning. In unsupervised learning, the system constructs a model using just the input characteristics but no output labeling. The classifiers are then trained to search the dataset for a specific pattern.
Learn More:https://bit.ly/3sX9xuQ
Contact Us:
Website: https://www.phdassistance.com/
UK: +44 7537144372
India No:+91-9176966446
Email: info@phdassistance.com
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
Performance Comparision of Machine Learning AlgorithmsDinusha Dilanka
In this paper Compare the performance of two
classification algorithm. I t is useful to differentiate
algorithms based on computational performance rather
than classification accuracy alone. As although
classification accuracy between the algorithms is similar,
computational performance can differ significantly and it
can affect to the final results. So the objective of this paper
is to perform a comparative analysis of two machine
learning algorithms namely, K Nearest neighbor,
classification and Logistic Regression. In this paper it
was considered a large dataset of 7981 data points and 112
features. Then the performance of the above mentioned
machine learning algorithms are examined. In this paper
the processing time and accuracy of the different machine
learning techniques are being estimated by considering the
collected data set, over a 60% for train and remaining
40% for testing. The paper is organized as follows. In
Section I, introduction and background analysis of the
research is included and in section II, problem statement.
In Section III, our application and data analyze Process,
the testing environment, and the Methodology of our
analysis are being described briefly. Section IV comprises
the results of two algorithms. Finally, the paper concludes
with a discussion of future directions for research by
eliminating the problems existing with the current
research methodology.
Machine Learning and Real-World ApplicationsMachinePulse
This presentation was created by Ajay, Machine Learning Scientist at MachinePulse, to present at a Meetup on Jan. 30, 2015. These slides provide an overview of widely used machine learning algorithms. The slides conclude with examples of real world applications.
Ajay Ramaseshan, is a Machine Learning Scientist at MachinePulse. He holds a Bachelors degree in Computer Science from NITK, Suratkhal and a Master in Machine Learning and Data Mining from Aalto University School of Science, Finland. He has extensive experience in the machine learning domain and has dealt with various real world problems.
Principal Component Analysis, or PCA, is a factual method that permits you to sum up the data contained in enormous information tables by methods for a littler arrangement of "synopsis files" that can be all the more handily envisioned and broke down.
A ppt based on predicting prices of houses. Also tells about basics of machine learning and the algorithm used to predict those prices by using regression technique.
Provides a brief overview of what machine learning is, how it works (theory), how to prepare data for a machine learning problem, an example case study, and additional resources.
Data Trend Analysis by Assigning Polynomial Function For Given Data SetIJCERT
This paper aims at explaining the method of creating a polynomial equation out of the given data set which can be used as a representation of the data itself and can be used to run aggregation against itself to find the results. This approach uses least-squares technique to construct a model of data and fit to a polynomial. Differential calculus technique is used on this equation to generate the aggregated results that represents the original data set.
Steering Model Selection with Visual Diagnostics: Women in Analytics 2019Rebecca Bilbro
Machine learning is ultimately a search for the best combination of features, algorithm, and hyperparameters that result in the best performing model. Oftentimes, this leads us to stay in our algorithmic comfort zones, or to resort to automated processes such as grid searches and random walks. Whether we stick to what we know or try many combinations, we are sometimes left wondering if we have actually succeeded.
By enhancing model selection with visual diagnostics, data scientists can inject human guidance to steer the search process. Visualizing feature transformations, algorithmic behavior, cross-validation methods, and model performance allows us a peek into the high dimensional realm that our models operate. As we continue to tune our models, trying to minimize both bias and variance, these glimpses allow us to be more strategic in our choices. The result is more effective modeling, speedier results, and greater understanding of underlying processes.
Visualization is an integral part of the data science workflow, but visual diagnostics are directly tied to machine learning transformers and models. The Yellowbrick library extends the scikit-learn API providing a Visualizer object, an estimator that learns from data and produces a visualization as a result. In this tutorial, we will explore feature visualizers, visualizers for classification, clustering, and regression, as well as model analysis visualizers. We'll work through several examples and show how visual diagnostics steer model selection, making machine learning more informed, and more effective.
Selecting the Right Type of Algorithm for Various Applications - PhdassistancePhD Assistance
Machine learning algorithms may be classified mainly into three main types. Supervised learning constructs a mathematical model from the training data, including input and output labels. The techniques of data categorization and regression are deemed supervised learning. In unsupervised learning, the system constructs a model using just the input characteristics but no output labeling. The classifiers are then trained to search the dataset for a specific pattern.
Learn More:https://bit.ly/3sX9xuQ
Contact Us:
Website: https://www.phdassistance.com/
UK: +44 7537144372
India No:+91-9176966446
Email: info@phdassistance.com
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
Performance Comparision of Machine Learning AlgorithmsDinusha Dilanka
In this paper Compare the performance of two
classification algorithm. I t is useful to differentiate
algorithms based on computational performance rather
than classification accuracy alone. As although
classification accuracy between the algorithms is similar,
computational performance can differ significantly and it
can affect to the final results. So the objective of this paper
is to perform a comparative analysis of two machine
learning algorithms namely, K Nearest neighbor,
classification and Logistic Regression. In this paper it
was considered a large dataset of 7981 data points and 112
features. Then the performance of the above mentioned
machine learning algorithms are examined. In this paper
the processing time and accuracy of the different machine
learning techniques are being estimated by considering the
collected data set, over a 60% for train and remaining
40% for testing. The paper is organized as follows. In
Section I, introduction and background analysis of the
research is included and in section II, problem statement.
In Section III, our application and data analyze Process,
the testing environment, and the Methodology of our
analysis are being described briefly. Section IV comprises
the results of two algorithms. Finally, the paper concludes
with a discussion of future directions for research by
eliminating the problems existing with the current
research methodology.
Predict Backorder on a supply chain data for an OrganizationPiyush Srivastava
Performed cleaning and founded the important variables and created a best model using different classification techniques (Random Forest, Naïve Bayes, Decision tree, KNN, Neural Network, Support Vector Machine) to predict the back-order for an organization using the best modelling and technique approach.
Explore the latest techniques and technologies used in classifying fetal health, from traditional methods to cutting-edge AI approaches. Understand the importance of accurate classification for prenatal care and fetal well-being. Join us to delve into this critical aspect of healthcare. visit https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/ for more data science insights
GENETIC ALGORITHM FOR FUNCTION APPROXIMATION: AN EXPERIMENTAL INVESTIGATIONijaia
Function Approximation is a popular engineering problems used in system identification or Equation
optimization. Due to the complex search space it requires, AI techniques has been used extensively to spot
the best curves that match the real behavior of the system. Genetic algorithm is known for their fast
convergence and their ability to find an optimal structure of the solution. We propose using a genetic
algorithm as a function approximator. Our attempt will focus on using the polynomial form of the
approximation. After implementing the algorithm, we are going to report our results and compare it with
the real function output.
Can data analysis help predict the future of your heart health?
The Boston Institute of Analytics (BIA) presents a collection of student presentations on data analysis projects tackling the critical topic of heart attack prediction.
Join us as we delve into the world of healthcare analytics and explore how data can be harnessed to identify individuals at risk of heart attack. These presentations offer valuable insights for:
Medical professionals seeking to develop preventative healthcare strategies
Individuals interested in understanding their own heart health risks
Data analysts passionate about applying data analysis for social good
Here's what you'll learn by watching these presentations:
The power of data analysis in predicting heart attacks
Various data analysis techniques used for risk assessment
Real-world examples of heart attack prediction models
Insights and findings from the research of dedicated BIA students
Empower yourself and others with the knowledge of heart health prediction. Watch these presentations and unlock the potential of data analysis in saving lives!
visit https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
HEALTH PREDICTION ANALYSIS USING DATA MININGAshish Salve
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
Simplified Knowledge Prediction: Application of Machine Learning in Real LifePeea Bal Chakraborty
Machine learning is the scientific study of algorithms and statistical models that is used by the machines to perform a specific task depending on patterns and inference rather than explicit instructions. This research and analysis aims to observe how precisely a machine can predict that a patient suspected of breast cancer is having malignant or benign cancer.In this paper the classification of cancer type and prediction of risk levels is done by various model of machine learning and is pictorially depicted by various tools of visual analytics.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
2. ABOUT THE PROJECT
Machine Learning is used across many spheres around the world. The healthcare industry is no exception. Machine
Learning can play an essential role in predicting presence/absence of Locomotor disorders, Heart diseases and more. Such
information, if predicted well in advance, can provide important insights to doctors who can then adapt their diagnosis and
treatment per patient basis.
In this project, I’ll discuss where I worked on predicting potential Heart Diseases in people using Machine Learning
algorithms. The algorithms included K Neighbors Classifier and Support Vector Classifier and applying PCA on
these 2 models.
Objective
Improve cardiovascular health and quality of life through prevention, detection, and treatment.
Early identification and treatment of heart attacks and strokes.
Prevention of repeat cardiovascular events and reduction in deaths from cardiovascular disease.
3. IMPORTING LIBRARIES AND DATASET
I imported several libraries for the project and all the necessary Machine Learning algorithms.
In the dataset,there are a total of 13 features and 1 target variable. Also, there are no missing values so we don’t need to take
care of any null values. Next, I used describe() method.
dataset.describe()
The method revealed
that the range of each
variable is different. The
maximum value of age is
77 but for chol it is 564.
Thus, feature scaling
must be performed on
the dataset.
4. Confusion Matrix
To begin with, let’s see the correlation matrix of features and try to analyse it. The figure size is defined to 12 x 8 by using
rcParams. Then, I used pyplot to show the correlation matrix. Using xticks and yticks, I’ve added names to the
correlation matrix. colorbar() shows the colorbar for the matrix.
It’s easy to see that there is no single feature
that has a very high correlation with our target
value. Also, some of the features have a
negative correlation with the target value and
some have positive.
5. Histogram
The best part about this type of plot is that it just takes a single command to draw the plots and it provides so much
information in return. Just use dataset.hist().
Let’s take a look at the plots. It shows
how each feature and label is
distributed along different ranges,
which further confirms the need for
scaling. Next, wherever you see discrete
bars, it basically means that each of
these is actually a categorical variable.
We will need to handle these
categorical variables before applying
Machine Learning. Our target labels
have two classes, 0 for no disease and 1
for disease.
6. Predict the Target class
It’s really essential that the dataset we are working on should be approximately balanced. An extremely imbalanced dataset
can render the whole model training useless and thus, will be of no use.
For x-axis I used the unique() values from the target column and
then set their name using xticks. For y-axis, I used value_count()
to get the values for each class. I colored the bars as green and red.
From the plot, we can see that the classes are almost balanced and we
are good to proceed with data processing.
7. Bar plot for Count of male and female
As per dataset,
0 – female and 1 – male
The plot shows the number of male is
mostly 2X greater than female patients in
the active study.
8. Scatter plot between Age and Maximum heart rate
A scatter plot is a type of plot or mathematical diagram using Cartesian coordinates to display values for typically two
variables- Age and Maximum Heart Rate.
The plot shows the ages between 40 to 70 is more
effective patients due to Maximum Heart Rate.
9. Data Processing and Splitting
To work with categorical variables, we should break each categorical column into dummy columns with 1s and 0s.
In this project, I took 4 algorithms and varied their various parameters and compared the final models.
I split the dataset into 67% training data and 33% testing data.
10. Support Vector Machine
I will run the model on the train and test set using SVC( Support Vector Classifier) and then use the test set to see what kind
of prediction results we get using the test data set.This classifier aims at forming a hyperplane that can separate the classes
as much as possible by adjusting the distance between the data points and the hyperplane. There are several kernels based
on which the hyperplane is decided. I tried four kernels namely, linear, poly, rbf, and sigmoid.
Once I had the scores for each, I used the rainbow method to select different colors for each bar and plot a bar graph of the
scores achieved by each.
As can be seen from the plot above, the linear
kernel performed the best for this dataset and
achieved a score of 83%
11. K – nearest neighbor classifier
This classifier looks for the classes of K nearest neighbors of a given data point and based on the majority class, it assigns a
class to this data point. However, the number of neighbors can be varied. I varied them from 1 to 20 neighbors and
calculated the test score in each case.Then, I plot a line graph of the number of neighbors and the test score achieved in each
case.
As you can see, we achieved the maximum score of 87%
when the number of neighbors was chosen to be 8.
12. SVM with PCA
The PCA/SVM-based method involves PCA-based data selection and image feature extraction for SVM classification; this
method can be used to solve the detection problems inherent in imprecise, uncertain, and incoherent data from multiple
sensors.
As you can see, we achieved the maximum score
of 80% the best for this dataset as per accuracy
check.
13. KNN with PCA
I am using KNN to classify and I also now have implemented PCA to reduce the dimensionality.As one closest neighbor is
red circle. Basically, with lower K value, - KNN model is trying to fit the model to data very closely and trying to find.
As you can see, we achieved the maximum
score of 56% when the number of neighbors
was chosen to be 20.
14. ACCURACY
1. Support Vector Classifier: 83%
2. K Neighbours Classifier: 87%
3. SVM with PCA: 80%
4. KNN with PCA: 56%
K Neighbours Classifier scored the best score of 87%
15. inference
K-Neighbours Classifier is amongst the most popular learning method grouped by similarities to build
machine learning models particularly for Heart disease prediction and document classification.