Kaggle Master with Heart Attack Prediction Kaggle Project

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Kaggle, a subsidiary of Google LLC, is an online community of data scientists and machine learning practitioners.

Kaggle is a platform where data scientists can compete in machine learning challenges. These challenges can be anything from predicting housing prices to detect
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. Machine learning is constantly being applied to new industries and ne
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 science application is an in-demand skill in many industries worldwide — including finance, transportation, education, manufacturing, human resources
Data science uses algorithms to understand raw data. The main difference between data science and traditional data analysis is its focus on prediction.
Data Scientists use machine learning to discover hidden patterns in large amounts of raw data to shed light on real problems.
What is Kaggle
Registering on Kaggle and Member Login Procedures
Getting to Know the Kaggle Homepage
Competitions on Kaggle
Datasets on Kaggle
Examining the Code Section in Kaggle
What is Discussion on Kaggle
Courses in Kaggle
Ranking Among Users on Kaggle
Blog and Documentation Sections
User Page Review on Kaggle
Treasure in The Kaggle
Publishing Notebooks on Kaggle
What Should Be Done to Achieve Success in Kaggle
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
Deteing 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

Desire to learn about Kaggle
Watch the course videos completely and in order
Internet Connection.
Any device such as mobile phone, computer, or tablet where you can watch the lesson.
Learning deteation and patience.
LIFE ACCESS, course updates, new content, any, anywhere, on any device
Nothing else! It's just you, your computer and your ambition to get started today
Desire to improve Data Science, Machine Learning, Python Portfolio with Kaggle
Free software and tools used during the course

It's possible to use machine learning without coding, but building new systems generally requires code. For example, '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 eeering" 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. Eeers writing machine learning systems often use Jupyter Notebooks and Python together. Jupyter Notebooks is a web application that allows expentation by creating and sharing documents that contain live code, equations, and more. Machine learning involves trial and error to see which hyperparameters and feature eeering 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 . 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 somes 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 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 eeers 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 eeers 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 eeer 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 eeer know

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

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 detee 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 bner 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, jаvascript, 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 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 deteing what interests you about data science. If you gravitate to visualizations, b 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 with learning Python. It is an excellent introduction to programming languages and will be useful as a data scientist. B 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 skillsset.

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 eeers, 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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Now Dive into; " Kaggle Masterclass with Hearth Attack Prediction Project

Kaggle is Machine Learning & Data Science community. Boost your CV with Hearth Attack Prediction Project in Kaggle " course.

See you in the course!

Anyone who wants to find and publish data sets, explore and build models in a web-based data-science environment, work with other data scientists and machine learning eeers, and enter competitions to solve data science challenges.
For those who want to compete in data science and machine learn by learning about Kaggle
Anyone who wants to learn Kaggle
Those who want to improve their CV in Data Science, Machine Learning, Python with Kaggle
Anyone who is interested in Artificial Intelligence, Machine Learning, Deep Learning, in short Data Science
Anyone who have a career goal in Data Science
Anyone who is interested in Artificial Intelligence, Machine Learning, Deep Learning, in short Data Science

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