Machine Learning Project: Heart Attack Prediction Analysis

Overview

Data Science & Machine Learning - Boost your Machine Learning, statistics skills with real heart attack analysis project

What you'll learn:

Machine learning describes systems that make predictions using a model trained on real-world data.
Machine learning isn’t just useful for predictive texting or smartphone voice recognition.
Data science uses algorithms to understand raw data. The main difference between data science and traditional data analysis is its focus on prediction.
Data science includes preparing, analyzing, and processing data. It draws from many scientific fields, and as a science, it progresses by creating new algorithm
Data Scientists use machine learning to discover hidden patterns in large amounts of raw data to shed light on real problems.
First Step to the Project
Notebook Design to be Used in the Project
Examining the Project Topic
Recognizing Variables in Dataset
Required Python Libraries
Loading the Dataset
Initial analysis on the dataset
Examining Missing Values
Examining Unique Values
Separating variables (Numeric or Categorical)
Examining Statistics of Variables
Numeric Variables (Analysis with Distplot)
Categoric Variables (Analysis with Pie Chart)
Examining the Missing Data According to the Analysis Result
Numeric Variables – Target Variable (Analysis with FacetGrid)
Categoric Variables – Target Variable (Analysis with Count Plot)
Examining Numeric Variables Among Themselves (Analysis with Pair Plot)
Feature Scaling with the Robust Scaler Method for New Visualization
Creating a New DataFrame with the Melt() Function
Numerical - Categorical Variables (Analysis with Swarm Plot)
Numerical - Categorical Variables (Analysis with Box Plot)
Relationships between variables (Analysis with Heatmap)
Dropping Columns with Low Correlation
Visualizing Outliers
Dealing with Outliers
Determining Distributions of Numeric Variables
Transformation Operations on Unsymmetrical Data
Applying One Hot Encoding Method to Categorical Variables
Feature Scaling with the Robust Scaler Method for Machine Learning Algorithms
Separating Data into Test and Training Set
Logistic Regression
Cross Validation for Logistic Regression Algorithm
Roc Curve and Area Under Curve (AUC) for Logistic Regression Algorithm
Hyperparameter Optimization (with GridSearchCV) for Logistic Regression Algorithm
Decision Tree Algorithm
Support Vector Machine Algorithm
Random Forest Algorithm
Hyperparameter Optimization (with GridSearchCV) for Random Forest Algorithm
Project Conclusion and Sharing

Machine Learning, python, statistics, data science, machine learning python, python data science, machine learning a-z, data scientist, r, python for data science |

Hello there,

Welcome to the “ Machine Learning Project: Heart Attack Prediction Analysis ” course.

Machine Learning & Data Science - Boost your Machine Learning skills with a real hands-on heart attack prediction project

Machine learning describes systems that make predictions using a model trained on real-world data. For example, let's say we want to build a system that can identify if a cat is in a picture. We first assemble many pictures to train our machine learning model. During this training phase, we feed pictures into the model, along with information about whether they contain a cat. While training, the model learns patterns in the images that are the most closely associated with cats. This model can then use the patterns learned during training to predict whether the new images that it's fed contain a cat. In this particular example, we might use a neural network to learn these patterns, but machine learning can be much simpler than that.

A machine learning course teaches you the techniques and concepts behind the predictive text, virtual assistants, and artificial intelligence. You can develop the foundational skills you need to advance to building neural networks and creating more complex functions through the Python and R programming languages. Machine learning training helps you stay ahead of new trends, technologies, and applications in this field.

We have more data than ever before. But data alone cannot tell us much about the world around us. We need to interpret the information and discover hidden patterns. This is where data science comes in. Data science uses algorithms to understand raw data. The main difference between data science and traditional data analysis is its focus on prediction. Data science seeks to find patterns in data and use those patterns to predict future data. It draws on machine learning to process large amounts of data, discover patterns, and predict trends. Data science includes preparing, analyzing, and processing data. It draws from many scientific fields, and as a science, it progresses by creating new algorithms to analyze data and validate current methods.

Data science application is an in-demand skill in many industries worldwide — including finance, transportation, education, manufacturing, human resources, and banking. Explore data science courses with Python, statistics, machine learning, and more to grow your knowledge. Get data science training if you’re into research, statistics, and analytics.

Do you know data science needs will create 11.5 million job openings by 2026?

Do you know the average salary is $100.000 for data science careers!

DATA SCIENCE CAREERS ARE SHAPING THE FUTURE

Data science experts are needed in almost every field, from government security to dating apps. Millions of businesses and government departments rely on big data to succeed and better serve their customers. So data science careers are in high demand.

If you want to learn one of the employer’s most requested skills?
If you are curious about Data Science and looking to start your self-learning journey into the world of data with Python?
If you are an experienced developer and looking for a landing in Data Science!

In all cases, you are at the right place!

We've designed for you " Machine Learning with Real Hearth Attack Prediction Project " a straight-forward course for the Python Programming Language and Machine Learning.

In the course, you will have a down-to-earth way explanation of the project. With this course, you will carry out a data science project from start to finish. I made it simple and easy with a real-life example.

We will open the door of the Data Science and Machine Learning world and will move deeper. You will learn the fundamentals of Machine Learning and its beautiful libraries such as Scikit Learn.

Throughout the course, we will teach you how to use Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms.

This Data Science & Machine Learning course is for everyone!

Python is a general-purpose, high-level, and multi-purpose programming language. The best thing about Python is, it supports a lot of today’s technology including vast libraries for Twitter, data mining, scientific calculations, designing, back-end server for websites, engineering simulations, artificial learning, augmented reality and what not! Also, it supports all kinds of App development.

What you will learn?

In this course, we will start from the beginning and go all the way to the end of "Machine Learning" using the heart attack dataset.

Before each lesson, there will be a theory part. After learning the theory parts, we will reinforce the subject with practical examples.

During the course you will see the following topics:

Introduction

First Step to the Project

Notebook Design to be Used in the Project

Examining the Project Topic

Recognizing Variables In Dataset

First Organization

Required Python Libraries

Loading the Dataset

Initial analysis on the dataset

Preparation For Exploratory Data Analysis (EDA)

Examining Missing Values

Examining Unique Values

Separating variables (Numeric or Categorical)

Examining Statistics of Variables

Exploratory Data Analysis (EDA) - Uni-variate Analysis

Numeric Variables (Analysis with Distplot): Lesson 1

Numeric Variables (Analysis with Distplot): Lesson 2

Categoric Variables (Analysis with Pie Chart): Lesson 1

Categoric Variables (Analysis with Pie Chart): Lesson 2

Examining the Missing Data According to the Analysis Result

Exploratory Data Analysis (EDA) - Bi-variate Analysis

Numeric Variables – Target Variable (Analysis with FacetGrid): Lesson 1

Numeric Variables – Target Variable (Analysis with FacetGrid): Lesson 2

Categoric Variables – Target Variable (Analysis with Count Plot): Lesson 1

Categoric Variables – Target Variable (Analysis with Count Plot): Lesson 2

Examining Numeric Variables Among Themselves (Analysis with Pair Plot) Lesson 1

Examining Numeric Variables Among Themselves (Analysis with Pair Plot) Lesson 2

Feature Scaling with the Robust Scaler Method

Creating a New DataFrame with the Melt() Function

Numerical - Categorical Variables (Analysis with Swarm Plot): Lesson 1

Numerical - Categorical Variables (Analysis with Swarm Plot): Lesson 2

Numerical - Categorical Variables (Analysis with Box Plot): Lesson 1

Numerical - Categorical Variables (Analysis with Box Plot): Lesson 2

Relationships between variables (Analysis with Heatmap): Lesson 1

Relationships between variables (Analysis with Heatmap): Lesson 2

Preparation for Modelling

Dropping Columns with Low Correlation

Visualizing Outliers

Dealing with Outliers – Trtbps Variable: Lesson 1

Dealing with Outliers – Trtbps Variable: Lesson 2

Dealing with Outliers – Thalach Variable

Dealing with Outliers – Oldpeak Variable

Determining Distributions of Numeric Variables

Transformation Operations on Unsymmetrical Data

Applying One Hot Encoding Method to Categorical Variables

Feature Scaling with the Robust Scaler Method for Machine Learning Algorithms

Separating Data into Test and Training Set

Modeling

Logistic Regression

Cross-Validation

Roc Curve and Area Under Curve (AUC)

Hyperparameter Optimization (with GridSearchCV)

Decision Tree Algorithm

Support Vector Machine Algorithm

Random Forest Algorithm

Hyperparameter Optimization (with GridSearchCV)

Conclusion

Project Conclusion and Sharing

Frequently asked questions about Machine Learning, Data Science

What is machine learning?

Machine learning describes systems that make predictions using a model trained on real-world data. For example, let's say we want to build a system that can identify if a cat is in a picture. We first assemble many pictures to train our machine learning model. During this training phase, we feed pictures into the model, along with information around whether they contain a cat. While training, the model learns patterns in the images that are the most closely associated with cats. This model can then use the patterns learned during training to predict whether the new images that it's fed contain a cat. In this particular example, we might use a neural network to learn these patterns, but machine learning can be much simpler than that. Even fitting a line to a set of observed data points, and using that line to make new predictions, counts as a machine learning model.

What is machine learning used for?

Machine learning is being applied to virtually every field today. That includes medical diagnoses, facial recognition, weather forecasts, image processing, and more. In any situation in which pattern recognition, prediction, and analysis are critical, machine learning can be of use. Machine learning is often a disruptive technology when applied to new industries and niches. Machine learning engineers can find new ways to apply machine learning technology to optimize and automate existing processes. With the right data, you can use machine learning technology to identify extremely complex patterns and yield highly accurate predictions.

Does machine learning require coding?

It's possible to use machine learning without coding, but building new systems generally requires code. For example, Amazon’s Rekognition service allows you to upload an image via a web browser, which then identifies objects in the image. This uses a pre-trained model, with no coding required. However, developing machine learning systems involves writing some Python code to train, tune, and deploy your models. It's hard to avoid writing code to pre-process the data feeding into your model. Most of the work done by a machine learning practitioner involves cleaning the data used to train the machine. They also perform “feature engineering” to find what data to use and how to prepare it for use in a machine learning model. Tools like AutoML and SageMaker automate the tuning of models. Often only a few lines of code can train a model and make predictions from it. An introductory understanding of Python will make you more effective in using machine learning systems.

What is the best language for machine learning?

Python is the most used language in machine learning. Engineers writing machine learning systems often use Jupyter Notebooks and Python together. Jupyter Notebooks is a web application that allows experimentation by creating and sharing documents that contain live code, equations, and more. Machine learning involves trial and error to see which hyperparameters and feature engineering choices work best. It's useful to have a development environment such as Python so that you don't need to compile and package code before running it each time. Python is not the only language choice for machine learning. Tensorflow is a popular framework for developing neural networks and offers a C++ API. There is a machine learning framework for C# called ML. NET. Scala or Java are sometimes used with Apache Spark to build machine learning systems that ingest massive data sets. You may find yourself using many different languages in machine learning, but Python is a good place to start.

What are the different types of machine learning?

Machine learning is generally divided between supervised machine learning and unsupervised machine learning. In supervised machine learning, we train machine learning models on labeled data. For example, an algorithm meant to detect spam might ingest thousands of email addresses labeled 'spam' or 'not spam.' That trained model could then identify new spam emails even from data it's never seen. In unsupervised learning, a machine learning model looks for patterns in unstructured data. One type of unsupervised learning is clustering. In this example, a model could identify similar movies by studying their scripts or cast, then group the movies together into genres. This unsupervised model was not trained to know which genre a movie belongs to. Rather, it learned the genres by studying the attributes of the movies themselves. There are many techniques available within these two types of machine learning, for example deep learning, reinforcement learning, and more.

Is machine learning a good career?

Machine learning is one of the fastest-growing and most popular computer science careers today. Constantly growing and evolving, you can apply machine learning to a variety of industries, from shipping and fulfillment to medical sciences. Machine learning engineers work to create artificial intelligence that can better identify patterns and solve problems. The machine learning discipline frequently deals with cutting-edge, disruptive technologies. However, because it has become a popular career choice, it can also be competitive. Aspiring machine learning engineers can differentiate themselves from the competition through certifications, boot camps, code repository submissions, and hands-on experience.

What is the difference between machine learning and artifical intelligence?

Machine learning is a smaller subset of the broader spectrum of artificial intelligence. While artificial intelligence describes any "intelligent machine" that can derive information and make decisions, machine learning describes a method by which it can do so. Through machine learning, applications can derive knowledge without the user explicitly giving out the information. This is one of the first and early steps toward "true artificial intelligence" and is extremely useful for numerous practical applications. In machine learning applications, an AI is fed sets of information. It learns from these sets of information about what to expect and what to predict. But it still has limitations. A machine learning engineer must ensure that the AI is fed the right information and can use its logic to analyze that information correctly.

What skills should a machine learning engineer know?

A machine learning engineer will need to be an extremely competent programmer with in-depth knowledge of computer science, mathematics, data science, and artificial intelligence theory. Machine learning engineers must be able to dig deep into complex applications and their programming. As with other disciplines, there are entry-level machine learning engineers and machine learning engineers with high-level expertise. Python and R are two of the most popular languages within the machine learning field.

What is data science?

We have more data than ever before. But data alone cannot tell us much about the world around us. We need to interpret the information and discover hidden patterns. This is where data science comes in. Data science uses algorithms to understand raw data. The main difference between data science and traditional data analysis is its focus on prediction. Data science seeks to find patterns in data and use those patterns to predict future data. It draws on machine learning to process large amounts of data, discover patterns, and predict trends. Data science includes preparing, analyzing, and processing data. It draws from many scientific fields, and as a science, it progresses by creating new algorithms to analyze data and validate current methods.

What does a data scientist do?

Data Scientists use machine learning to discover hidden patterns in large amounts of raw data to shed light on real problems. This requires several steps. First, they must identify a suitable problem. Next, they determine what data are needed to solve such a situation and figure out how to get the data. Once they obtain the data, they need to clean the data. The data may not be formatted correctly, it might have additional unnecessary data, it might be missing entries, or some data might be incorrect. Data Scientists must, therefore, make sure the data is clean before they analyze the data. To analyze the data, they use machine learning techniques to build models. Once they create a model, they test, refine, and finally put it into production.

What are the most popular coding languages for data science?

Python is the most popular programming language for data science. It is a universal language that has a lot of libraries available. It is also a good beginner language. R is also popular; however, it is more complex and designed for statistical analysis. It might be a good choice if you want to specialize in statistical analysis. You will want to know either Python or R and SQL. SQL is a query language designed for relational databases. Data scientists deal with large amounts of data, and they store a lot of that data in relational databases. Those are the three most-used programming languages. Other languages such as Java, C++, JavaScript, and Scala are also used, albeit less so. If you already have a background in those languages, you can explore the tools available in those languages. However, if you already know another programming language, you will likely be able to pick up Python very quickly.

How long does it take to become a data scientist?

This answer, of course, varies. The more time you devote to learning new skills, the faster you will learn. It will also depend on your starting place. If you already have a strong base in mathematics and statistics, you will have less to learn. If you have no background in statistics or advanced mathematics, you can still become a data scientist; it will just take a bit longer. Data science requires lifelong learning, so you will never really finish learning. A better question might be, "How can I gauge whether I know enough to become a data scientist?" Challenge yourself to complete data science projects using open data. The more you practice, the more you will learn, and the more confident you will become. Once you have several projects that you can point to as good examples of your skillset as a data scientist, you are ready to enter the field.

How can I learn data science on my own?

It is possible to learn data science on your own, as long as you stay focused and motivated. Luckily, there are a lot of online courses and boot camps available. Start by determining what interests you about data science. If you gravitate to visualizations, begin learning about them. Starting with something that excites you will motivate you to take that first step. If you are not sure where you want to start, try starting by learning Python. It is an excellent introduction to programming languages and will be useful as a data scientist. Begin by working through tutorials or Oak Academy courses on the topic of your choice. Once you have developed a base in the skills that interest you, it can help to talk with someone in the field. Find out what skills employers are looking for and continue to learn those skills. When learning on your own, setting practical learning goals can keep you motivated.

Does data science require coding?

The jury is still out on this one. Some people believe that it is possible to become a data scientist without knowing how to code, but others disagree. A lot of algorithms have been developed and optimized in the field. You could argue that it is more important to understand how to use the algorithms than how to code them yourself. As the field grows, more platforms are available that automate much of the process. However, as it stands now, employers are primarily looking for people who can code, and you need basic programming skills. The data scientist role is continuing to evolve, so that might not be true in the future. The best advice would be to find the path that fits your skill set.

What skills should a data scientist know?

A data scientist requires many skills. They need a strong understanding of statistical analysis and mathematics, which are essential pillars of data science. A good understanding of these concepts will help you understand the basic premises of data science. Familiarity with machine learning is also important. Machine learning is a valuable tool to find patterns in large data sets. To manage large data sets, data scientists must be familiar with databases. Structured query language (SQL) is a must-have skill for data scientists. However, nonrelational databases (NoSQL) are growing in popularity, so a greater understanding of database structures is beneficial. The dominant programming language in Data Science is Python — although R is also popular. A basis in at least one of these languages is a good starting point. Finally, to communicate findings, data scientists require knowledge of visualizations. Data visualizations allow them to share complex data in an accessible manner.

Is data science a good career?

The demand for data scientists is growing. We do not just have data scientists; we have data engineers, data administrators, and analytics managers. The jobs also generally pay well. This might make you wonder if it would be a promising career for you. A better understanding of the type of work a data scientist does can help you understand if it might be the path for you. First and foremost, you must think analytically. Data science is about gaining a more in-depth understanding of info through data. Do you fact-check information and enjoy diving into the statistics? Although the actual work may be quite technical, the findings still need to be communicated. Can you explain complex findings to someone who does not have a technical background? Many data scientists work in cross-functional teams and must share their results with people with very different backgrounds. If this sounds like a great work environment, then it might be a promising career for you.

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