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+# 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]