## What you would learn in Machine Learning Project: Heart Attack Prediction Analysis course?

Welcoming to the **"** Machine Learning Project: Heart Attack Prediction Analysis **"**course.

Machine Learning & Data Science Enhance the quality of your Machine Learning skills with an authentic, practical heart attack prediction exercise

machine learning is a term used to describe systems that make predictions based on a model that has been built on data from real-world situations. As an example, suppose we're looking to create a system that recognizes the presence of cats in a photograph. First, we gather a lot of images to train our machine-learning model. During the training phase, we feed images into the model together with details regarding whether or not they are the cat. While training the model, it learns patterns within the images that are the closest to cats. The model then uses the patterns that it has learned from training to determine if the images it is fed include cats. We could employ a neural network to understand these patterns; however, machine learning could be more straightforward than this.

The machine-learning course will teach you the theories and techniques that underlie digital assistants, predictive texts, and AI. It will help you develop the fundamental abilities you require to move towards building neural networks and creating more complex tasks using learning the Python and R programming language. Training in machine learning helps keep up-to-date with the latest technology, trends, and applications in this field.

There is more data available than we ever have. However, just data can't provide a lot of information about the world surrounding us. We must interpret the data to discover the patterns that are hidden. This is the area where data science can help. This is where data science comes in. Data science utilizes algorithms to analyze the raw data. The significant distinction between traditional and data analysis is the focus on prediction. Data science aims to discover patterns in data and then use these patterns to forecast future data. Machine learning is used to process massive data, find patterns, and identify patterns. Data science involves preparing, analyzing, processing, and analyzing data. It draws on a variety of sciences, and as science is developed, it creates new algorithms to analyze data and test the current methods.

Information Science application is a sought-after ability in various industries around the world, including transportation, finance, education, manufacturing, human resources, and banking. Explore courses in data science using Python, machine learning, statistics, and many more to increase your understanding. Learn about data science for those interested in the study, research, or analytics.

#### What will you be able to learn?

In this class, we'll begin at the beginning and work our way through to the final part in "Machine Learning" using the heart attack data.

In every lesson, we will have an introduction to the theory. After we have learned the theory components, we will review the concepts with real-life examples.

In the course, you will be exposed to the following subjects:

**Introduction**

First Steps towards the Project

The Notebook Designs to be Utilized for the Project

Studying the Project Topic

Recognizing Variables In Dataset

**First Organization**

Python Libraries are required. Python Libraries

The Dataset is loaded Dataset

Initial analysis of the data

**Preparation for Exploratory Data Analyses (EDA)**

Reviewing Missing Values

Examining Unique Values

Differentiating variables (Numeric or Categorical)

Studying the Statistics of Variables

**Exploratory Data Analyse (EDA) - - Uni-variate Analysis**

Numeric Variables (Analysis using Distplot) 1.

Numeric Variables (Analysis using Distplot) 2nd lesson

Categoric Variables (Analysis using Pie Chart) 1st lesson

Categoric Variables (Analysis using Pie Chart) 2.

Examining the missing data according to the Analysis Results

**Exploratory Data Analysis (EDA) - Bi-variate Analysis**

Numeric Variables Target Variable (Analysis using FacetGrid) 1st lesson

Numeric Variables Target Variable (Analysis using FacetGrid) 2nd lesson

Categoric Variables: Target Variable (Analysis using Count Plot) 1st lesson

Categoric Variables and Target Variables (Analysis using a Count Plot) Lesson 2

Examining the Numeric Variables among Themselves (Analysis using a Pair Plot) Leçon 1

Examining the Numeric Variables among Themselves (Analysis using a Pair Plot) Lesson 2

Features Scaling using Robust Scaler Method Robust Scaler Method

Making a New DataFrame by using the Melt() Function

Numerical Categorical Variables (Analysis using Swarm Plot): Lesson 1

Numerical Categorical Variables (Analysis using Swarm Plot): Lesson 2

Numerical Categorical Variables (Analysis using Box Plot) 1.

Numerical Categorical Variables (Analysis using Box Plot) 2nd lesson

Relations between variables (Analysis using Heatmap) 1. Lesson 1.

Relations between variables (Analysis using Heatmap) 2nd Lesson

**Preparation for Modeling**

Dropping Columns with low correlation

Visualizing Outliers

Learning to deal with Outliers Trtbps Variable: Lesson 1.

Handling Outliers Trtbps Variable 2: Lesson 2.

How to deal with Outliers Thalach Variable

Handling Outliers Oldpeak Variable

The determination of distributions of Numeric Variables

Transformation Operations on Data that is Unsymmetrical

Utilizing One Hot Encoding Method for Categorical Variables

The Feature Scaling Method employs The Robust Scaler Method for Machine Learning Algorithms

Separating data into the Training and Test 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

### Course Content:

- Machine learning refers to methods that predict by using a model that has been trained on real-world data.
- Machine learning isn't only beneficial for predictive texting or smartphone voice recognition.
- Data science employs algorithms to analyze the raw data. The primary distinction between data science and traditional data analysis is the focus on data prediction.
- Data science involves gathering, analyzing, or processing information. It is a blend of many sciences, and as science progresses, it develops through the development of new algorithms.
- Data scientists use machine learning to uncover hidden patterns within large quantities of raw data to reveal the root of real issues.
- The first step in the Project
- The Notebook Designs to be Utilized for the Project
- Studying the Project Topic
- Recognizing Variables in the Dataset
- Essential Python Libraries
- Dataset loading Dataset
- Initial analysis of the dataset
- Checking for missing values
- Examining Unique Values
- Variables that are separated (Numeric or Categorical)
- Reviewing statistics of Variables
- Numeric Variables (Analysis using Distplot)
- Categoric Variables (Analysis using Pie Chart)
- Examining the missing data according to the Analysis Results
- Numeric Variables Target Variable (Analysis using FacetGrid)
- Categoric Variables Target Variable (Analysis using the Count Plot)
- Examining the Numeric Variables among Themselves (Analysis using the Pair Plot)
- The Feature Scaling Method employs the Robust Scaler Method for the New Visualization
- Create a new DataFrame using the Melt() Function
- Numerical Categorical Variables (Analysis using Swarm Plot)
- Numerical Categorical Variables (Analysis using Box Plot)
- Relations between variables (Analysis using Heatmap)
- Dropping Columns with low correlation
- Visualizing Outliers
- Handling Outliers
- Determining the distributions of Numeric Variables
- Transformation Operations on Data that is Unsymmetrical
- Utilizing an Encoding Technique that is Hot for Categorical Variables
- Scaling Feature Using The Robust Scaler Method for Machine Learning Algorithms
- Separating Test and Training Sets
- Logistic Regression
- Cross-Validation of 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

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