Machine Learning Project: Heart Attack Prediction Analysis

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

Published 05/2022
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 53 lectures (7h 23m) | Size: 2.2 GB

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