https://github.com/ayaanjawaid/brain_stroke_prediction

project aims to predict the likelihood of a stroke based on various health parameters using machine learning models. The dataset is preprocessed, analyzed, and multiple models are trained to achieve the best prediction accuracy.
https://github.com/ayaanjawaid/brain_stroke_prediction

decision-trees exploratory-data-analysis matplotlib numpy pandas python regression xgboost

Last synced: 17 days ago
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project aims to predict the likelihood of a stroke based on various health parameters using machine learning models. The dataset is preprocessed, analyzed, and multiple models are trained to achieve the best prediction accuracy.

• Host: GitHub
• URL: https://github.com/ayaanjawaid/brain_stroke_prediction
• Owner: Ayaanjawaid
• Created: 2023-04-11T08:15:22.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-10-28T22:41:28.000Z (2 months ago)
• Last Synced: 2024-10-28T23:27:12.369Z (2 months ago)
• Topics: decision-trees, exploratory-data-analysis, matplotlib, numpy, pandas, python, regression, xgboost
• Language: Jupyter Notebook
• Homepage:
• Size: 4.52 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        

## Project Overview

This project aims to predict the likelihood of a stroke based on various health parameters using machine learning models. The dataset is preprocessed, analyzed, and multiple models are trained to achieve the best prediction accuracy.

## Libraries Used

Pandas: For data manipulation and analysis.

NumPy: For numerical operations.

Matplotlib and Seaborn: For data visualization.

Scikit-learn: For implementing machine learning models.

XGBoost: For the implementation of the XGBoost model.

## Data Preprocessing

Data Loading: The dataset is loaded using Pandas.

Data Cleaning: Missing values are handled, and unnecessary columns are removed.

Feature Engineering: New features are created to enhance model performance.

Encoding: Categorical variables are encoded using one-hot encoding.

## Exploratory Data Analysis (EDA)

Visualization: Various plots (histograms, bar plots, correlation heatmaps) are used to understand the distribution and relationships of the data.

Statistical Analysis: Summary statistics are computed to gain insights into the dataset.

## Model Training

Multiple machine learning models are trained to predict strokes. The models used include:

Logistic Regression

Decision Tree Classifier

Random Forest Classifier

Support Vector Machine (SVM)

K-Nearest Neighbors (KNN)

XGBoost

## Model Evaluation

Confusion Matrix: Used to evaluate the performance of the classification models.

Accuracy, Precision, Recall, and F1-Score: Computed for each model to compare their performance.

ROC Curve and AUC Score: Analyzed to understand the models' ability to distinguish between classes.