Brain Stroke Prediction

Overview

This project predicts the likelihood of a brain stroke using machine learning models. The dataset includes health and demographic features.

Dataset

• Features: gender, age, hypertension, heart disease, marital status, work type, residence type, average glucose level, BMI, smoking status, and stroke occurrence.

Link to the dataset: https://www.kaggle.com/datasets/jillanisofttech/brain-stroke-dataset

Project Structure

Libraries

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
from sklearn.tree import DecisionTreeClassifier
from sklearn.preprocessing import StandardScaler, LabelEncoder
from imblearn.over_sampling import SMOTE
from sklearn.ensemble import RandomForestClassifier
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np

Data Preprocessing

• Load dataset
• Exploratory data analysis
• Encode categorical variables
• Handle imbalanced data
• Remove outliers

Visualization

• Distribution of stroke classes
• Kernel Density Estimation of age by stroke

Models

• Decision Tree
• Random Forest

Conclusion

This project demonstrates the use of machine learning models to predict brain strokes. The models were trained and evaluated, showing their performance in terms of accuracy, classification report, and confusion matrix.