# Stroke Prediction Model

## Introduction

This repository hosts a machine learning project designed to predict the likelihood of a stroke based on various health indicators and demographic information. The project encapsulates the entire workflow of a typical data science project including data exploration, preprocessing, model training, evaluation, and suggestions for improvement.

## Data Exploration

We initiated the project by performing an extensive exploratory data analysis (EDA). The process involved:

- Examining summary statistics and distributions of various features.

- Identifying missing values, particularly in the 'bmi' feature.

- Visualizing data distributions and potential correlations between features.

## Data Preprocessing

Data preprocessing was a critical step due to issues such as missing values and the need to convert categorical variables into a machine-readable format. The following steps were taken:

- Missing values in 'bmi' were imputed using the median of the feature.

- Categorical variables were encoded using label encoding.

- Numerical features were scaled to have a mean of zero and a standard deviation of one.

## Modeling

We employed a step-wise approach to model building:

1. **Logistic Regression**: Served as our baseline model.

2. **Random Forest**: Provided a more robust model capable of capturing non-linear patterns.

3. **Gradient Boosting and XGBoost**: Leveraged for their prowess in dealing with imbalanced datasets.

## Evaluation Metrics Explained

Model performance was evaluated using several metrics suited for imbalanced datasets:

- **Precision**: The accuracy of positive predictions.

- **Recall**: The ability of the model to capture actual positive instances.

- **F1-Score**: A balance between precision and recall.

- **Support**: The number of instances for each class in the validation set.

- **Accuracy**: Although not the primary metric due to class imbalance, it was still considered.

- **Macro Average**: Average performance across classes.

- **Weighted Average**: Average performance weighted by the number of instances in each class.

## Handling Imbalanced Data

To address the imbalance in the dataset, we explored:

- **SMOTE**: For oversampling the minority class.

- **Class Weight Adjustment**: To make the model pay more attention to the minority class.

## Results and Discussion

The models' predictions and their respective performance metrics were analyzed to identify the best-performing model. The analysis revealed a strong bias towards the majority class, prompting the use of SMOTE and class weight adjustments.

## Future Work

Future improvements could include:

- Advanced feature engineering.

- Hyperparameter optimization.

- Exploration of alternative resampling strategies.

## Installation and Usage

To replicate this project, clone the repository and install the required dependencies listed in `requirements.txt`.

```bash

git clone https://github.com/mayankbaluni/StrokeRiskPredictor.git

cd stroke-prediction

pip install -r requirements.txt

# End of script

exit 0

```

## Contact

For any queries or suggestions, feel free to contact me at [mayankbaluni@gmail.com]