jaideep-siva / heart-attack-prediction

Heart attack prediction

Heart Attack Prediction Using Advanced Statistical Methods

Authors: Jaideep Siva, Senthil

This Jupyter Notebook is an extensive analysis and application of advanced statistical methods to predict heart attacks. This is a meticulously crafted ipynb to showcase a deep understanding of statistical analysis, data preprocessing, and machine learning model application in the medical field.

Project Overview

The project aims to utilize a dataset containing various medical parameters to predict the likelihood of heart attacks. This README provides a detailed guide through the project's structure, methodologies employed, and key findings.

Preface

Understanding Heart Attacks

An introductory section provides a comprehensive overview of heart attacks, including definitions, risk factors, and their significance. This section sets the stage for the importance of predictive modeling in healthcare.

Dataset and Variables

Describes the dataset used in the project, including the source and the variables involved. This section is crucial for understanding the basis of the analysis.

Data Pre-Analysis and Analysis

Pre-Analysis Steps

• Installing Packages: Lists the necessary Python packages for running the notebook.
• Importing Data: Details the process of loading the dataset into the working environment.
• Understanding Data: Provides initial insights into the dataset's shape, variance, and general characteristics.

In-depth Data Analysis

• Categorical and Continuous Data Analysis: Delves into the dataset's categorical and continuous variables, exploring their distributions and impact on the prediction target.
• Correlation Mapping: Utilizes correlation matrices to identify potential relationships between variables.
• Advanced Visualizations: Implements UMAP and t-SNE visualizations to explore high-dimensional data relationships in lower-dimensional spaces.

Data Preprocessing

Outlines the steps taken to prepare the data for modeling, including handling missing values, feature scaling, one-hot encoding for categorical variables, and outlier transformation. This section is key to understanding how raw data is transformed into a format suitable for predictive modeling.

Models for Classification

Details the various machine learning models applied to the dataset, including Decision Trees, Random Forest, Gradient Boost, XG Boost, Support Vector Machines, Logistic Regression, and K-Nearest Neighbors. Each model's selection rationale, implementation, and performance evaluation are discussed.

Bagging to Improve Accuracy

Introduces bagging techniques used to enhance model accuracy, showcasing advanced ensemble methods' application to achieve better predictive performance.

Conclusion and Sources

The project concludes with a summary of the key findings, insights, and potential areas for further research. This section synthesizes the entire analysis and its implications for predicting heart attacks.

Acknowledgments

Lists the sources and references used throughout the project, ensuring the credibility and reliability of the information presented.

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