Kaggle Master with Heart Attack Prediction Kaggle Project

Kaggle, machine learning, data science, python, statistics, r, machine learning python, python data science, deep learning, python programming, django, machine learning a-z, data scientist, python for data science

Hello there,

Welcome to the “ Kaggle Masterclass with Hearth Attack Prediction Project” course.

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

Do you know that there is no such detailed course on Kaggle on any platform?

And 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

AND SO REVIEVE THIS CAREER WITH THE KAGGLE PLATFORM

Well, why is Data Science such an important field? Let's examine it together.

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 “Kaggle - Get The Best Data Science, Machine Learning Profile” a super course to improve your CV in data science.

In the course, you will study each chapter in detail. With this course, you will get to know the Kaggle platform step by step.

This course is for everyone!

My “Kaggle Masterclass with Hearth Attack Prediction Project” is for everyone! If you don’t have any previous experience, not a problem! This course is expertly designed to teach everyone from complete beginners, right through to professionals (as a refresher).

What will you learn?

In this course, we will start from the very beginning and go all the way to end of "Kaggle" with examples.

During the course you will see the following topics:

• 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?

• 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

• Examining Numeric Variables Among Themselves

• Feature Scaling with the Robust Scaler Method

• Creating a New DataFrame with the Melt() Function

• Numerical - Categorical Variables

• Preparation for Modelling Project

• Modelling Project

• Project Sharing

Video and Audio Production Quality

All our videos are created/produced as high-quality video and audio to provide you with the best learning experience.

You will be,

• Seeing clearly

• Hearing clearly

• Moving through the course without distractions

You'll also get:

Lifetime Access to The Course

Fast & Friendly Support in the Q&A section

We offer full support, answering any questions.

If you are ready to learn

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!

Reed courses certificate of completion

Digital certificate - Included

• Course Intro 01:33

  • 1: Kaggle Master with Heart Attack Prediction Kaggle Project 01:33

• First Contact with Kaggle 44:49

  • 2: What is Kaggle? 15:57
  • 3: FAQ about Kaggle 03:00
  • 4: Registering on Kaggle and Member Login Procedures 06:07
  • 5: Project Link File - Hearth Attack Prediction Project, Machine Learning 01:00
  • 6: Getting to Know the Kaggle Homepage 17:45
  • 7: quiz 01:00

• Competition Section on Kaggle 45:10

  • 8: Competitions on Kaggle: Lesson 1 22:45
  • 9: Competitions on Kaggle: Lesson 2 21:25
  • 10: quiz 01:00

• Dataset Section on Kaggle 17:00

  • 11: Datasets on Kaggle 16:00
  • 12: quiz 01:00

• Code Section on Kaggle 48:24

  • 13: Examining the Code Section in Kaggle: Lesson 1 12:40
  • 14: Examining the Code Section in Kaggle Lesson 2 14:49
  • 15: Examining the Code Section in Kaggle Lesson 3 19:55
  • 16: quiz 01:00

• Discussion Section on Kaggle 06:40

  • 17: What is Discussion on Kaggle? 05:40
  • 18: quiz 01:00

• Other Most Used Options on Kaggle 28:10

  • 19: Courses in Kaggle 06:48
  • 20: Ranking Among Users on Kaggle 15:33
  • 21: Blog and Documentation Sections 04:49
  • 22: quiz 01:00

• Details on Kaggle 32:55

  • 23: User Page Review on Kaggle 10:38
  • 24: Treasure in The Kaggle 07:42
  • 25: Publishing Notebooks on Kaggle 05:11
  • 26: What Should Be Done to Achieve Success in Kaggle? 08:24
  • 27: quiz 01:00

• Introduction to Machine Learning with Real Hearth Attack Prediction Project 1:00:35

  • 28: First Step to the Hearth Attack Prediction Project 15:16
  • 29: FAQ about Machine Learning, Data Science 02:00
  • 30: Notebook Design to be Used in the Project 14:16
  • 31: Project Link File - Hearth Attack Prediction Project, Machine Learning 01:00
  • 32: Examining the Project Topic 10:01
  • 33: Recognizing Variables In Dataset 17:02
  • 34: quiz 01:00

• First Organization 23:50

  • 35: Required Python Libraries 08:40
  • 36: Loading the Statistics Dataset in Data Science 01:48
  • 37: Initial analysis on the dataset 12:22
  • 38: quiz 01:00

• Preparation For Exploratory Data Analysis (EDA) in Data Science 41:40

  • 39: Examining Missing Values 10:05
  • 40: Examining Unique Values 09:11
  • 41: Separating variables (Numeric or Categorical) 03:12
  • 42: Examining Statistics of Variables 18:12
  • 43: quiz 01:00

• Exploratory Data Analysis (EDA) - Uni-variate Analysis 59:10

  • 44: Numeric Variables (Analysis with Distplot): Lesson 1 14:29
  • 45: Numeric Variables (Analysis with Distplot): Lesson 2 03:57
  • 46: Categoric Variables (Analysis with Pie Chart): Lesson 1 13:55
  • 47: Categoric Variables (Analysis with Pie Chart): Lesson 2 15:40
  • 48: Examining the Missing Data According to the Analysis Result 10:09
  • 49: quiz 01:00

• Exploratory Data Analysis (EDA) - Bi-variate Analysis 2:03:47

  • 50: Numeric Variables – Target Variable (Analysis with FacetGrid): Lesson 1 08:33
  • 51: Numeric Variables – Target Variable (Analysis with FacetGrid): Lesson 2 07:31
  • 52: Categoric Variables – Target Variable (Analysis with Count Plot): Lesson 1 03:58
  • 53: Categoric Variables – Target Variable (Analysis with Count Plot): Lesson 2 12:57
  • 54: Examining Numeric Variables Among Themselves Lesson 1 04:56
  • 55: Examining Numeric Variables Among Themselves Lesson 2 06:55
  • 56: Feature Scaling with the Robust Scaler Method 09:00
  • 57: Creating a New DataFrame with the Melt() Function 09:00
  • 58: Numerical - Categorical Variables (Analysis with Swarm Plot): Lesson 1 11:22
  • 59: Numerical - Categorical Variables (Analysis with Swarm Plot): Lesson 2 11:22
  • 60: Numerical - Categorical Variables (Analysis with Box Plot): Lesson 1 06:26
  • 61: Numerical - Categorical Variables (Analysis with Box Plot): Lesson 2 11:10
  • 62: Relationships between variables (Analysis with Heatmap): Lesson 1 06:05
  • 63: Relationships between variables (Analysis with Heatmap): Lesson 2 12:32
  • 64: quiz 02:00

• Preparation for Modelling in Machine Learning 1:15:16

  • 65: Dropping Columns with Low Correlation 03:47
  • 66: Visualizing Outliers 08:31
  • 67: Dealing with Outliers – Trtbps Variable: Lesson 1 09:58
  • 68: Dealing with Outliers – Trtbps Variable: Lesson 2 10:53
  • 69: Dealing with Outliers – Thalach Variable 08:22
  • 70: Dealing with Outliers – Oldpeak Variable 07:50
  • 71: Determining Distributions of Numeric Variables 05:02
  • 72: Transformation Operations on Unsymmetrical Data 04:56
  • 73: Applying One Hot Encoding Method to Categorical Variables 05:24
  • 74: Feature Scaling with the Robust Scaler Method 02:29
  • 75: Separating Data into Test and Training Set 07:04
  • 76: quiz 01:00

• Modelling for Machine Learning 1:02:03

  • 77: Logistic Regression 06:54
  • 78: Cross Validation 05:41
  • 79: Roc Curve and Area Under Curve(AUC) 08:17
  • 80: Hyperparameter Optimization(with GridSearchCV) 12:54
  • 81: Decision Tree Algorithm 05:05
  • 82: Support Vector Machine Algorithm 05:02
  • 83: Random Forest Algorithm 06:17
  • 84: Hyperparameter Optimization(with GridSearchCV) 10:53
  • 85: quiz 01:00

• Conclusion 05:32

  • 86: Project Conclusion and Sharing 03:32
  • 87: quiz 02:00

• Extra 01:00

  • 88: Kaggle Masterclass with Hearth Attack Prediction Project 01:00

Kaggle, machine learning, data science, python, statistics, r, machine learning python, python data science, deep learning, python programming, django, machine learning a-z, data scientist, python for data science

Hello there,

Welcome to the “ Kaggle Masterclass with Hearth Attack Prediction Project” course.

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

Do you know that there is no such detailed course on Kaggle on any platform?

And 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

AND SO REVIEVE THIS CAREER WITH THE KAGGLE PLATFORM

Well, why is Data Science such an important field? Let's examine it together.

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 “Kaggle - Get The Best Data Science, Machine Learning Profile” a super course to improve your CV in data science.

In the course, you will study each chapter in detail. With this course, you will get to know the Kaggle platform step by step.

This course is for everyone!

My “Kaggle Masterclass with Hearth Attack Prediction Project” is for everyone! If you don’t have any previous experience, not a problem! This course is expertly designed to teach everyone from complete beginners, right through to professionals (as a refresher).

What will you learn?

In this course, we will start from the very beginning and go all the way to end of "Kaggle" with examples.

During the course you will see the following topics:

• 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?

• 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

• Examining Numeric Variables Among Themselves

• Feature Scaling with the Robust Scaler Method

• Creating a New DataFrame with the Melt() Function

• Numerical - Categorical Variables

• Preparation for Modelling Project

• Modelling Project

• Project Sharing

Video and Audio Production Quality

All our videos are created/produced as high-quality video and audio to provide you with the best learning experience.

You will be,

• Seeing clearly

• Hearing clearly

• Moving through the course without distractions

You'll also get:

Lifetime Access to The Course

Fast & Friendly Support in the Q&A section

We offer full support, answering any questions.

If you are ready to learn

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 engineers, 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

• 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 determination and patience.
• LIFETIME ACCESS, course updates, new content, anytime, 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

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