This document outlines the steps to build a web application for heart disease classification from scratch using a data science lifecycle approach. It describes collecting a heart disease dataset with 14 attributes, preparing and exploring the data to identify correlations between attributes, modeling the data using gradient boosting which achieved the best results, and deploying the trained model into a web application using Flask that allows users to input a patient's symptoms and see a disease prediction. The full code for the web app is provided in the author's GitHub repository.
Congrats ! You got your Data Science JobRohit Dubey
Congrats ! You got your Data Science Job after completion of this presentation course.
What can you find on this presentation course?
I aim to provide as many resources as possible for learning Data Science. These resources include:
Course to upskill yourself in analytics and data science
Real life industry problems being released in form of contests
This slide will help you get:
Jobs – Apply on data science jobs to start or improve your career
DSAT – Access your data science knowledge using our adaptive test
Tips and tricks related to Data Science, Machine Learning, Business Analytics and Business Intelligence tools
Case studies: Case studies of problems and their analytical solutions Interviews of Business Analytics & Business Intelligence leaders.
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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/
Congrats ! You got your Data Science JobRohit Dubey
Congrats ! You got your Data Science Job after completion of this presentation course.
What can you find on this presentation course?
I aim to provide as many resources as possible for learning Data Science. These resources include:
Course to upskill yourself in analytics and data science
Real life industry problems being released in form of contests
This slide will help you get:
Jobs – Apply on data science jobs to start or improve your career
DSAT – Access your data science knowledge using our adaptive test
Tips and tricks related to Data Science, Machine Learning, Business Analytics and Business Intelligence tools
Case studies: Case studies of problems and their analytical solutions Interviews of Business Analytics & Business Intelligence leaders.
#datascience #machinelearning #python #artificialintelligence #ai #data #dataanalytics #bigdata #programming #coding #technology #datascientist #deeplearning #computerscience #datavisualization #tech #pythonprogramming #analytics #iot #dataanalysis #java #programmer #developer #business #database #ml #javascript #software #innovation #cybersecurity
#coder #statistics #datamining #dataanalyst #code #engineering #linux #codinglife #cloudcomputing #businessintelligence #robotics #softwaredeveloper #automation #cloud #neuralnetworks #sql #science #softwareengineer #digitaltransformation #computer #daysofcode #coders #bigdataanalytics #programminglife #dataviz #html #digitalmarketing #devops #datasciencetraining #dataprotection
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#free #love #giveaway #freedom #follow #music #life #like #instagood #art #instagram #nature
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/
Improve The Performance of K-means by using Genetic Algorithm for Classificat...IJECEIAES
In this research the k-means method was used for classification purposes after it was improved using genetic algorithms. An automated classification system for heart attack was implemented based on the intelligent recruitment of computer capabilities at the same time characterized by high performance based on (270) real cases stored within a globally database known (Statlog). The proposed system aims to support the efforts of staff in medical felid to reduce the diagnostic errors committed by doctors who do not have sufficient experience or because of the fatigue that the doctor suffers as a result of work pressure. The proposed system goes through two stages: in the first-stage genetic algorithm is used to select important features that have a strong influence in the classification process. These features forms the inputs to the K-means method in the second-stage which uses the selected features to divide the database into two groups one of them contain cases infected with the disease while the other group contains the correct cases depending on the distance Euclidean. The comparison of performance for the method (Kmeans) before and after addition genetic algorithm shows that the accuracy of the classification improves remarkably where the accuracy of classification was raised from (68..1481) in the case of use (k- means only) to (84.741) when improved the method by using genetic algorithm.
THE IMPLICATION OF STATISTICAL ANALYSIS AND FEATURE ENGINEERING FOR MODEL BUI...ijcseit
Scrutiny for presage is the era of advance statistics where accuracy matter the most. Commensurate
between algorithms with statistical implementation provides better consequence in terms of accurate
prediction by using data sets. Prolific usage of algorithms lead towards the simplification of mathematical
models, which provide less manual calculations. Presage is the essence of data science and machine
learning requisitions that impart control over situations. Implementation of any dogmas require proper
feature extraction which helps in the proper model building that assist in precision. This paper is
predominantly based on different statistical analysis which includes correlation significance and proper
categorical data distribution using feature engineering technique that unravel accuracy of different models
of machine learning algorithms.
THE IMPLICATION OF STATISTICAL ANALYSIS AND FEATURE ENGINEERING FOR MODEL BUI...IJCSES Journal
Scrutiny for presage is the era of advance statistics where accuracy matter the most. Commensurate between algorithms with statistical implementation provides better consequence in terms of accurate prediction by using data sets. Prolific usage of algorithms lead towards the simplification of mathematical models, which provide less manual calculations. Presage is the essence of data science and machine learning requisitions that impart control over situations. Implementation of any dogmas require proper feature extraction which helps in the proper model building that assist in precision. This paper is predominantly based on different statistical analysis which includes correlation significance and proper categorical data distribution using feature engineering technique that unravel accuracy of different models of machine learning algorithms.
Unraveling The Meaning From COVID-19 Dataset Using Python – A Tutorial for be...Kavika Roy
The Corona Virus – COVID-19 outbreak has brought the whole world to a standstill position, with complete lock-down in several countries. Salute! To every health and security professional. Here we will attempt to perform single data analysis with COVID-19 Dataset Using Python. https://www.datatobiz.com/blog/unraveling-the-u-meaning-from-covid-19-dataset-using-python-a-tutorial-for-beginners/
In healthcare sector, data are enormous and diverse because it contains a data of different types and getting knowledge from these data is crucial. So to get that knowledge, data mining techniques may be utilized to mine knowledge by building models from healthcare dataset. At present, the classification of heart diseases patients has been a demanding research confront for many researchers. For building a classification model for a these patient, we used four different classification algorithms such as NaiveBayes, MultilayerPerceptron, RandomForest and DecisionTable. The intention behind this work is to classify that whether a patient is tested positive or tested negative for heart diseases, based on some diagnostic measurements integrated into the dataset.
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.
We are predicting Heart Disease by Taking 14 Medical Parameters as an inputs through 2 data Minning Techniques(Decision Tree(Faster) And KNN neighbour Algorithms(Slower)).
And Visualizing The dataset.If the output 1 then it means Higher Chances of getting Heart Attack ,if 0 then it means Less chances of Heart Attack.
how to sell pi coins at high rate quickly.DOT TECH
Where can I sell my pi coins at a high rate.
Pi is not launched yet on any exchange. But one can easily sell his or her pi coins to investors who want to hold pi till mainnet launch.
This means crypto whales want to hold pi. And you can get a good rate for selling pi to them. I will leave the telegram contact of my personal pi vendor below.
A vendor is someone who buys from a miner and resell it to a holder or crypto whale.
Here is the telegram contact of my vendor:
@Pi_vendor_247
Improve The Performance of K-means by using Genetic Algorithm for Classificat...IJECEIAES
In this research the k-means method was used for classification purposes after it was improved using genetic algorithms. An automated classification system for heart attack was implemented based on the intelligent recruitment of computer capabilities at the same time characterized by high performance based on (270) real cases stored within a globally database known (Statlog). The proposed system aims to support the efforts of staff in medical felid to reduce the diagnostic errors committed by doctors who do not have sufficient experience or because of the fatigue that the doctor suffers as a result of work pressure. The proposed system goes through two stages: in the first-stage genetic algorithm is used to select important features that have a strong influence in the classification process. These features forms the inputs to the K-means method in the second-stage which uses the selected features to divide the database into two groups one of them contain cases infected with the disease while the other group contains the correct cases depending on the distance Euclidean. The comparison of performance for the method (Kmeans) before and after addition genetic algorithm shows that the accuracy of the classification improves remarkably where the accuracy of classification was raised from (68..1481) in the case of use (k- means only) to (84.741) when improved the method by using genetic algorithm.
THE IMPLICATION OF STATISTICAL ANALYSIS AND FEATURE ENGINEERING FOR MODEL BUI...ijcseit
Scrutiny for presage is the era of advance statistics where accuracy matter the most. Commensurate
between algorithms with statistical implementation provides better consequence in terms of accurate
prediction by using data sets. Prolific usage of algorithms lead towards the simplification of mathematical
models, which provide less manual calculations. Presage is the essence of data science and machine
learning requisitions that impart control over situations. Implementation of any dogmas require proper
feature extraction which helps in the proper model building that assist in precision. This paper is
predominantly based on different statistical analysis which includes correlation significance and proper
categorical data distribution using feature engineering technique that unravel accuracy of different models
of machine learning algorithms.
THE IMPLICATION OF STATISTICAL ANALYSIS AND FEATURE ENGINEERING FOR MODEL BUI...IJCSES Journal
Scrutiny for presage is the era of advance statistics where accuracy matter the most. Commensurate between algorithms with statistical implementation provides better consequence in terms of accurate prediction by using data sets. Prolific usage of algorithms lead towards the simplification of mathematical models, which provide less manual calculations. Presage is the essence of data science and machine learning requisitions that impart control over situations. Implementation of any dogmas require proper feature extraction which helps in the proper model building that assist in precision. This paper is predominantly based on different statistical analysis which includes correlation significance and proper categorical data distribution using feature engineering technique that unravel accuracy of different models of machine learning algorithms.
Unraveling The Meaning From COVID-19 Dataset Using Python – A Tutorial for be...Kavika Roy
The Corona Virus – COVID-19 outbreak has brought the whole world to a standstill position, with complete lock-down in several countries. Salute! To every health and security professional. Here we will attempt to perform single data analysis with COVID-19 Dataset Using Python. https://www.datatobiz.com/blog/unraveling-the-u-meaning-from-covid-19-dataset-using-python-a-tutorial-for-beginners/
In healthcare sector, data are enormous and diverse because it contains a data of different types and getting knowledge from these data is crucial. So to get that knowledge, data mining techniques may be utilized to mine knowledge by building models from healthcare dataset. At present, the classification of heart diseases patients has been a demanding research confront for many researchers. For building a classification model for a these patient, we used four different classification algorithms such as NaiveBayes, MultilayerPerceptron, RandomForest and DecisionTable. The intention behind this work is to classify that whether a patient is tested positive or tested negative for heart diseases, based on some diagnostic measurements integrated into the dataset.
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.
We are predicting Heart Disease by Taking 14 Medical Parameters as an inputs through 2 data Minning Techniques(Decision Tree(Faster) And KNN neighbour Algorithms(Slower)).
And Visualizing The dataset.If the output 1 then it means Higher Chances of getting Heart Attack ,if 0 then it means Less chances of Heart Attack.
how to sell pi coins at high rate quickly.DOT TECH
Where can I sell my pi coins at a high rate.
Pi is not launched yet on any exchange. But one can easily sell his or her pi coins to investors who want to hold pi till mainnet launch.
This means crypto whales want to hold pi. And you can get a good rate for selling pi to them. I will leave the telegram contact of my personal pi vendor below.
A vendor is someone who buys from a miner and resell it to a holder or crypto whale.
Here is the telegram contact of my vendor:
@Pi_vendor_247
NO1 Uk Black Magic Specialist Expert In Sahiwal, Okara, Hafizabad, Mandi Bah...Amil Baba Dawood bangali
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Falcon stands out as a top-tier P2P Invoice Discounting platform in India, bridging esteemed blue-chip companies and eager investors. Our goal is to transform the investment landscape in India by establishing a comprehensive destination for borrowers and investors with diverse profiles and needs, all while minimizing risk. What sets Falcon apart is the elimination of intermediaries such as commercial banks and depository institutions, allowing investors to enjoy higher yields.
how to swap pi coins to foreign currency withdrawable.DOT TECH
As of my last update, Pi is still in the testing phase and is not tradable on any exchanges.
However, Pi Network has announced plans to launch its Testnet and Mainnet in the future, which may include listing Pi on exchanges.
The current method for selling pi coins involves exchanging them with a pi vendor who purchases pi coins for investment reasons.
If you want to sell your pi coins, reach out to a pi vendor and sell them to anyone looking to sell pi coins from any country around the globe.
Below is the contact information for my personal pi vendor.
Telegram: @Pi_vendor_247
USDA Loans in California: A Comprehensive Overview.pptxmarketing367770
USDA Loans in California: A Comprehensive Overview
If you're dreaming of owning a home in California's rural or suburban areas, a USDA loan might be the perfect solution. The U.S. Department of Agriculture (USDA) offers these loans to help low-to-moderate-income individuals and families achieve homeownership.
Key Features of USDA Loans:
Zero Down Payment: USDA loans require no down payment, making homeownership more accessible.
Competitive Interest Rates: These loans often come with lower interest rates compared to conventional loans.
Flexible Credit Requirements: USDA loans have more lenient credit score requirements, helping those with less-than-perfect credit.
Guaranteed Loan Program: The USDA guarantees a portion of the loan, reducing risk for lenders and expanding borrowing options.
Eligibility Criteria:
Location: The property must be located in a USDA-designated rural or suburban area. Many areas in California qualify.
Income Limits: Applicants must meet income guidelines, which vary by region and household size.
Primary Residence: The home must be used as the borrower's primary residence.
Application Process:
Find a USDA-Approved Lender: Not all lenders offer USDA loans, so it's essential to choose one approved by the USDA.
Pre-Qualification: Determine your eligibility and the amount you can borrow.
Property Search: Look for properties in eligible rural or suburban areas.
Loan Application: Submit your application, including financial and personal information.
Processing and Approval: The lender and USDA will review your application. If approved, you can proceed to closing.
USDA loans are an excellent option for those looking to buy a home in California's rural and suburban areas. With no down payment and flexible requirements, these loans make homeownership more attainable for many families. Explore your eligibility today and take the first step toward owning your dream home.
how to sell pi coins in South Korea profitably.DOT TECH
Yes. You can sell your pi network coins in South Korea or any other country, by finding a verified pi merchant
What is a verified pi merchant?
Since pi network is not launched yet on any exchange, the only way you can sell pi coins is by selling to a verified pi merchant, and this is because pi network is not launched yet on any exchange and no pre-sale or ico offerings Is done on pi.
Since there is no pre-sale, the only way exchanges can get pi is by buying from miners. So a pi merchant facilitates these transactions by acting as a bridge for both transactions.
How can i find a pi vendor/merchant?
Well for those who haven't traded with a pi merchant or who don't already have one. I will leave the telegram id of my personal pi merchant who i trade pi with.
Tele gram: @Pi_vendor_247
#pi #sell #nigeria #pinetwork #picoins #sellpi #Nigerian #tradepi #pinetworkcoins #sellmypi
how can I sell pi coins after successfully completing KYCDOT TECH
Pi coins is not launched yet in any exchange 💱 this means it's not swappable, the current pi displaying on coin market cap is the iou version of pi. And you can learn all about that on my previous post.
RIGHT NOW THE ONLY WAY you can sell pi coins is through verified pi merchants. A pi merchant is someone who buys pi coins and resell them to exchanges and crypto whales. Looking forward to hold massive quantities of pi coins before the mainnet launch.
This is because pi network is not doing any pre-sale or ico offerings, the only way to get my coins is from buying from miners. So a merchant facilitates the transactions between the miners and these exchanges holding pi.
I and my friends has sold more than 6000 pi coins successfully with this method. I will be happy to share the contact of my personal pi merchant. The one i trade with, if you have your own merchant you can trade with them. For those who are new.
Message: @Pi_vendor_247 on telegram.
I wouldn't advise you selling all percentage of the pi coins. Leave at least a before so its a win win during open mainnet. Have a nice day pioneers ♥️
#kyc #mainnet #picoins #pi #sellpi #piwallet
#pinetwork
Poonawalla Fincorp and IndusInd Bank Introduce New Co-Branded Credit Cardnickysharmasucks
The unveiling of the IndusInd Bank Poonawalla Fincorp eLITE RuPay Platinum Credit Card marks a notable milestone in the Indian financial landscape, showcasing a successful partnership between two leading institutions, Poonawalla Fincorp and IndusInd Bank. This co-branded credit card not only offers users a plethora of benefits but also reflects a commitment to innovation and adaptation. With a focus on providing value-driven and customer-centric solutions, this launch represents more than just a new product—it signifies a step towards redefining the banking experience for millions. Promising convenience, rewards, and a touch of luxury in everyday financial transactions, this collaboration aims to cater to the evolving needs of customers and set new standards in the industry.
If you are looking for a pi coin investor. Then look no further because I have the right one he is a pi vendor (he buy and resell to whales in China). I met him on a crypto conference and ever since I and my friends have sold more than 10k pi coins to him And he bought all and still want more. I will drop his telegram handle below just send him a message.
@Pi_vendor_247
What website can I sell pi coins securely.DOT TECH
Currently there are no website or exchange that allow buying or selling of pi coins..
But you can still easily sell pi coins, by reselling it to exchanges/crypto whales interested in holding thousands of pi coins before the mainnet launch.
Who is a pi merchant?
A pi merchant is someone who buys pi coins from miners and resell to these crypto whales and holders of pi..
This is because pi network is not doing any pre-sale. The only way exchanges can get pi is by buying from miners and pi merchants stands in between the miners and the exchanges.
How can I sell my pi coins?
Selling pi coins is really easy, but first you need to migrate to mainnet wallet before you can do that. I will leave the telegram contact of my personal pi merchant to trade with.
Tele-gram.
@Pi_vendor_247
Empowering the Unbanked: The Vital Role of NBFCs in Promoting Financial Inclu...Vighnesh Shashtri
In India, financial inclusion remains a critical challenge, with a significant portion of the population still unbanked. Non-Banking Financial Companies (NBFCs) have emerged as key players in bridging this gap by providing financial services to those often overlooked by traditional banking institutions. This article delves into how NBFCs are fostering financial inclusion and empowering the unbanked.
what is the future of Pi Network currency.DOT TECH
The future of the Pi cryptocurrency is uncertain, and its success will depend on several factors. Pi is a relatively new cryptocurrency that aims to be user-friendly and accessible to a wide audience. Here are a few key considerations for its future:
Message: @Pi_vendor_247 on telegram if u want to sell PI COINS.
1. Mainnet Launch: As of my last knowledge update in January 2022, Pi was still in the testnet phase. Its success will depend on a successful transition to a mainnet, where actual transactions can take place.
2. User Adoption: Pi's success will be closely tied to user adoption. The more users who join the network and actively participate, the stronger the ecosystem can become.
3. Utility and Use Cases: For a cryptocurrency to thrive, it must offer utility and practical use cases. The Pi team has talked about various applications, including peer-to-peer transactions, smart contracts, and more. The development and implementation of these features will be essential.
4. Regulatory Environment: The regulatory environment for cryptocurrencies is evolving globally. How Pi navigates and complies with regulations in various jurisdictions will significantly impact its future.
5. Technology Development: The Pi network must continue to develop and improve its technology, security, and scalability to compete with established cryptocurrencies.
6. Community Engagement: The Pi community plays a critical role in its future. Engaged users can help build trust and grow the network.
7. Monetization and Sustainability: The Pi team's monetization strategy, such as fees, partnerships, or other revenue sources, will affect its long-term sustainability.
It's essential to approach Pi or any new cryptocurrency with caution and conduct due diligence. Cryptocurrency investments involve risks, and potential rewards can be uncertain. The success and future of Pi will depend on the collective efforts of its team, community, and the broader cryptocurrency market dynamics. It's advisable to stay updated on Pi's development and follow any updates from the official Pi Network website or announcements from the team.
Currently pi network is not tradable on binance or any other exchange because we are still in the enclosed mainnet.
Right now the only way to sell pi coins is by trading with a verified merchant.
What is a pi merchant?
A pi merchant is someone verified by pi network team and allowed to barter pi coins for goods and services.
Since pi network is not doing any pre-sale The only way exchanges like binance/huobi or crypto whales can get pi is by buying from miners. And a merchant stands in between the exchanges and the miners.
I will leave the telegram contact of my personal pi merchant. I and my friends has traded more than 6000pi coins successfully
Tele-gram
@Pi_vendor_247
1. The Lifecycle to Build a Web Application for Prediction from
Scratch
The data science lifecycle is designed for big data issues and data science projects. Generally, the data
science project consists of seven steps which are problem definition, data collection, data preparation,
data exploration, data modeling, model evaluation and model deployment. This article goes through the
data science lifecycle in order to build a web application for heart disease classification.
If you would like to look at a specific step in the lifecycle, you can read it without looking deeply at the
other steps.
Problem Definition
Clinical decisions are often made based on doctors’ experience and intuition rather than on the knowledge-
rich hidden in the data. This leads to errors and many costs that affect the quality of medical services.
Using analytic tools and data modeling can help in enhancing the clinical decisions. Thus, the goal here is
to build a web application to help doctors in diagnosing heart diseases. The full code of is available in my
GitHub repository.
Data Collection
BEGINNER MACHINE LEARNING PROGRAMMING PYTHON STRUCTURED DATA TECHNIQUE UNCATEGORIZED
2. I collected the heart disease dataset from UCI ML. The dataset has the following 14 attributes:
age: age in years.
sex: sex (1=male; 0=female).
cp: chest pain type (0 = typical angina; 1 = atypical angina; 2 = non-anginal pain; 3: asymptomatic).
trestbps: resting blood pressure in mm Hg on admission to the hospital.
chol: serum cholesterol in mg/dl.
fbs: fasting blood sugar > 120 mg/dl (1=true; 0=false).
restecg: resting electrocardiographic results ( 0=normal; 1=having ST-T wave abnormality; 2=probable
or definite left ventricular hypertrophy).
thalach: maximum heart rate achieved.
exang: exercise-induced angina (1=yes; 0=no).
oldpeak: ST depression induced by exercise relative to rest.
slope: the slope of the peak exercise ST segment (0=upsloping; 1=flat; 2=downsloping).
ca: number of major vessels (0–3) colored by fluorosopy.
thal: thalassemia (3=normal; 6=fixed defect; 7=reversable defect).
target: heart disease (1=no, 2=yes).
Data Preparation and Exploration
Here is a snapshot of the data header.
The header of the heart disease dataset
From the first look, the dataset contains 14 columns, 5 of them contain numerical values and 9 of them
contain categorical values.
The dataset is clean and contains all the information needed for each variable. By using info(),
describe(), isnull() functions, no errors, missing values, and inconsistencies values are detected.
#Check null values df.isnull().sum()
3. Null values in the dataset
By checking the percentage of the persons with and without heart diseases, it was found that 56% of the
persons in the dataset have heart disease. So, the dataset is relatively balanced.
People with and
without heart disease
in the dataset
Attributes Correlation
4. This heatmap shows the correlations between the dataset attributes, and how the attributes interact with
each other. From the heatmap, we can observe that the chest pain type (cp), exercise-induced angina
(exang), ST depression induced by exercise relative to rest (oldpeak), the slope of the peak exercise ST
segment (slope), number of major vessels (0–3) colored by flourosopy (ca) and thalassemia (thal) are
highly correlated with the heart disease (target). We observe also that there is an inverse proportion
between heart disease and maximum heart rate (thalch).
Moreover, we can see that the age is correlated with number of major vessels (0–3) colored by flourosopy
(ca) and maximum heart rate (thalch). There is also a relation between ST depression induced by exercise
relative to rest (oldpeak) and the slope of the peak exercise ST segment (slope). Moreover, there is a
relation between chest pain type (cp) and exercise-induced angina (exang).
Next, we will analyze these correlations between these features further.
1. Age and Maximum Heart Rate
5. Heart disease is arising frequently in older people, and the max heart rates are lower for old people with
heart disease.
2. Chest Pain
There are four types of chest pain: typical angina, atypical angina, non-anginal pain, and asymptomatic.
Most of the heart disease patients are found to have asymptomatic chest pain.
2. Chest Pain and Exercise-Induced Angina
6. The people who have exercise-induced angina; they usually suffer from asymptomatic chest pain, and they
are more likely to have heart disease.
3. Thalassemia
People with reversible defects are likely to have heart disease.
4. ST depression and the Slope of the Peak Exercise ST Segment.
7. The people who have downsloping ST segment have higher values of ST depression and more chance to be
infected with heart disease. The greater the ST depression, the greater the chance of disease.
5. Age and Number of Major Vessels (0–3) Colored by Flourosopy.
Most of the heart disease patients are old and they have one or more major vessels colored by Flourosopy.
Data Modeling
Let’s create the machine learning model. We are trying to predict whether a person has heart disease. We
will use the ‘target’ column as the class, and all the other columns as features for the model.
# Initialize data and target target = df[‘target’] features = df.drop([‘target’], axis = 1)
– Data Splitting
We will divide the data into a training set and test set. 80% of the data will be for training and 20% for
testing.
# Split the data into training set and testing set X_train, X_test, y_train, y_test =
train_test_split(features, target, test_size = 0.2, random_state = 0)
8. – Machine Learning Model
Here, we will try the below machine learning algorithms then we will select the best one based on its
classification report.
Support Vector Machine
Random Forest
Ada Boost
Gradient Boosting
The following function for training and evaluating the classifiers.
def fit_eval_model(model, train_features, y_train, test_features, y_test): results = {} # Train the model
model.fit(train_features, y_train) # Test the model train_predicted = model.predict(train_features)
test_predicted = model.predict(test_features) # Classification report and Confusion Matrix
results[‘classification_report’] = classification_report(y_test, test_predicted) results[‘confusion_matrix’]
= confusion_matrix(y_test, test_predicted) return results
Initialize models, train and evaluate.
# Initialize the models sv = SVC(random_state = 1) rf = RandomForestClassifier(random_state = 1) ab =
AdaBoostClassifier(random_state = 1) gb = GradientBoostingClassifier(random_state = 1)# Fit and evaluate
models results = {} for cls in [sv, rf, ab, gb]: cls_name = cls.__class__.__name__ results[cls_name] = {}
results[cls_name] = fit_eval_model(cls, X_train, y_train, X_test, y_test)
Now, we will print the evaluation results.
# Print classifiers results for result in results: print (result) print()for i in results[result]: print (i,
‘:’) print(results[result][i]) print() print (‘ — — -’) print()
The results are below:
Support Vector Machine Result
9. Random Forest Result
Ada Boost Results
Gradient Boosting Result
From the above results, the best model is Gradient Boosting. So, I will save this model to use it for web
applications.
– Save the Prediction Model
Now, we will pickle the model so that it can be saved on disk.
10. # Save the model as serialized object pickle with open(‘model.pkl’, ‘wb’) as file: pickle.dump(gb, file)
Model Deployment
It is time to start deploying and building the web application using Flask web application framework. For
the web app, we have to create:
1. Web app python code (API) to load the model, get user input from the HTML template, make the
prediction, and return the result.
2. An HTML template for the front end to allow the user to input heart disease symptoms of the patient and
display if the patient has heart disease or not.
The structure of the files is like the following:
/ ├── model.pkl ├── heart_disease_app.py ├── templates/ └── Heart Disease Classifier.html
Web App Python Code
You can find the full code of the web app here.
As a first step, we have to import the necessary libraries.
import numpy as np import pickle from flask import Flask, request, render_template
Then, we create app object.
# Create application app = Flask(__name__)
After that, we need to load the saved model model.pkl in the app.
# Load machine learning model model = pickle.load(open(‘model.pkl’, ‘rb’))
After that home() function is called when the root endpoint ‘/’ is hit. The function redirects to the home
page Heart Disease Classifier.html of the website.
# Bind home function to URL @app.route(‘/’) def home(): return render_template(‘Heart Disease
Classifier.html’)
Now, create predict() function for the endpoint ‘/predict’. The function is defined as this endpoint
with POST method. When the user submits the form, the API receives a POST request, the API extracts all
data from the form using flask.request.form function. Then, the API uses the model to predict the
result. Finally, the function renders the Heart Disease Classifier.html template and returns the
result.
# Bind predict function to URL @app.route(‘/predict’, methods =[‘POST’]) def predict(): # Put all form
entries values in a list features = [float(i) for i in request.form.values()] # Convert features to array
11. array_features = [np.array(features)] # Predict features prediction = model.predict(array_features) output =
prediction # Check the output values and retrieve the result with html tag based on the value if output == 1:
return render_template(‘Heart Disease Classifier.html’, result = ‘The patient is not likely to have heart
disease!’) else: return render_template(‘Heart Disease Classifier.html’, result = ‘The patient is likely to
have heart disease!’)
Finally, start the flask server and run our web page locally on the computer by calling app.run() and then
enter http://localhost:5000 on the browser.
if __name__ == ‘__main__’: #Run the application app.run()
HTML Template
The following figure presents the HTML form. You can find the code here.
The form has 13 inputs for the 13 features and a button. The button sends POST request to the/predict
endpoint with the input data. In the form tag, the action attribute calls predict function when the form
is submitted.
<form action = “{{url_for(‘predict’)}}” method =”POST” >
12. Finally, the HTML page presents the stored result in the result parameter.
<strong style="color:red">{{result}}</strong>
Summary
In this article, you learned how to create a web application for prediction from scratch. Firstly, we started
with the problem definition and data collection. Then, we worked on data preparation, data exploration,
data modeling, and model evaluation. Finally, we deployed the model using a flask.
Now, it is time to practice and apply what you learn in this article. Define a problem, search for a dataset
on the Internet, and then go through the other steps of the data science lifecycle.
About the Author
Nada Alay
I am working in the data analytics field and passionate about data science, machine learning, and scientific
research. Photograph: I use the attached logo as a personal image on the Internet.
Article Url - https://www.analyticsvidhya.com/blog/2020/09/web-application/
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