Machine Learning Project: Heart Attack Prediction Analysis

Overview

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

Syllabus

• Introduction to Machine Learning and Heart Disease
Overview of machine learning in healthcare
Understanding the dataset
• Python for Data Science
Python setup and essential libraries (Pandas, NumPy, Matplotlib, Seaborn)
Loading and exploring data
• Data Preprocessing
Handling missing values
Feature scaling and normalization
Categorical data encoding
• Exploratory Data Analysis (EDA)
Statistical summary and visualization
Feature correlation and importance
• Introduction to Statistics for Machine Learning
Basic statistical concepts
Probability and distributions
Hypothesis testing
• Supervised Learning: Classification
Overview of classification algorithms
Logistic regression
Decision trees and random forests
• Model Evaluation and Validation
Train-test split and cross-validation
Evaluation metrics: accuracy, precision, recall, F1-score, ROC-AUC
• Advanced Machine Learning Techniques
Hyperparameter tuning
Ensemble methods (Bagging, Boosting)
• Implementation with R and Python
Comparative analysis using R and Python
Code examples and best practices
• Project: Heart Attack Prediction Analysis
Project overview and objectives
Step-by-step implementation
Model deployment strategies
• Conclusion and Future Directions
Summary of key concepts
Exploring advanced topics: deep learning, model interpretability
Resources for continued learning
• Capstone Presentation
Preparing a project report
Presentation skills and peer feedback

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Taught by

Oak Academy and OAK Academy Team

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Subjects

Data Science