Stroke Prediction using Machine Learning

This project analyzes a medical dataset to predict the likelihood of a patient having a stroke using various machine learning classifiers. It includes data preprocessing, feature engineering, model training, evaluation, and comparison.

📁 Dataset

The dataset used is the Stroke Prediction Dataset from Kaggle.

You can download it directly from the Kaggle link above.

📊 Features

Feature	Description
id	Unique identifier (dropped in preprocessing)
gender	Gender of the patient
age	Age of the patient
hypertension	1 if the patient has hypertension, else 0
heart_disease	1 if the patient has any heart disease, else 0
ever_married	Marital status
work_type	Type of work (e.g. Private, Self-employed)
Residence_type	Urban or Rural
avg_glucose_level	Average glucose level in blood
bmi	Body Mass Index (missing values filled)
smoking_status	Smoking status
stroke	Target variable (1 = stroke, 0 = no stroke)

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🔧 Preprocessing Steps

• Dropped id column.
• Imputed missing values in bmi using the median.
• Label encoded binary columns: gender, ever_married, Residence_type.
• One-hot encoded: work_type, smoking_status.
• Standard scaled: age, avg_glucose_level, bmi.

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🤖 Models Implemented

• Logistic Regression
• SGD Classifier
• Perceptron
• Gaussian Naive Bayes
• K-Nearest Neighbors (KNN)
• Linear Support Vector Classifier (SVC)
• Multi-Layer Perceptron (MLPClassifier)

Note: The dataset is imbalanced. SMOTE (Synthetic Minority Over-sampling Technique) was used to oversample the minority class during training.

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📈 Evaluation Metrics

The following metrics are used to evaluate the models:

• Accuracy
• Precision
• Recall
• F1 Score
• Mean Absolute Error (MAE)
• Mean Squared Error (MSE)
• Confusion Matrix
• Classification Report