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Stroke Prediction System using Linear Regression

Real-world project · AICTE-aligned · AI-graded · Audit-ready certificate

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About this project

Stroke Prediction System using Linear Regression

Objective: Develop a predictive system to estimate the likelihood of stroke occurrence using patient data.

Context: Stroke is a leading cause of death and disability in India, where early detection can significantly improve outcomes and reduce healthcare burdens.

What you'll build: You'll analyze a medical dataset, apply feature scaling, and implement a linear regression model using Python and Scikit-learn. Model performance will be evaluated with metrics like MAE and RMSE, and results will be visualized using Matplotlib.

Deliverables: Submit a working prediction prototype, visualizations, and a concise report detailing dataset analysis, model accuracy, and findings.

Milestones

1. Dataset analysis

10 marks 7d

Perform exploratory data analysis on the provided stroke dataset. Submit a Jupyter notebook showing summary statistics, visualizations of key features (e.g., age, hypertension, heart disease), and identification of missing values or outliers. 'Done' means your notebook clearly communicates dataset characteristics and potential data quality issues relevant to stroke prediction.

2. Model development

10 marks 7d

Develop a linear regression model to predict stroke risk using the dataset. Submit your code and a brief explanation of your feature selection, preprocessing steps, and model training process. 'Done' means your model runs without errors and you justify your choices based on the dataset's properties.

3. Accuracy assessment

15 marks 7d

Evaluate your model's performance using appropriate regression metrics (e.g., RMSE, MAE, R²) on a test set. Submit your metric calculations and a short interpretation of the results, discussing the model's predictive accuracy and any limitations. 'Done' means your assessment is reproducible and clearly explains how well the model predicts stroke risk.

4. Report generation

15 marks 9d

Compile a concise report summarizing your analysis, model development, and accuracy assessment. Include key findings, visualizations, and recommendations for improving the model. 'Done' means your report is well-organized, addresses the project goals, and demonstrates clear understanding of the stroke prediction task using linear regression.

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Upcoming sessions

Session	Window	Enrolled
Stroke Prediction System using Linear Regression	11 Jun 2026 to 10 Jun 2028	0

Skills you'll learn

Regression analysisFeature scalingEvaluation metrics (MAERMSE)

Tools used

Python Scikit-learn Matplotlib

Prerequisites

Linear regression theory Data visualization

Available mentors

Priyang Kumar

Free

You'll earn — Certificate (PDF)

AICTE-aligned Project Completion Certificate

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Stroke Prediction System using Linear Regression

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