Assessfy Pvt. Ltd Moderate 5 milestones 50 marks

Heart Failure Prediction System

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

Enroll as Student Mentor this project

Milestones

Available mentors

Enrolled students

Core skills

About this project

Heart Failure Prediction System

Objective: Create a machine learning system to predict heart failure risk based on patient health records.

Context: Heart disease is a major health challenge in India, and early risk prediction can help save lives and optimize healthcare resources.

What you'll build: You'll explore and preprocess a medical dataset using Pandas, select relevant features, and implement classification algorithms with Scikit-learn. Model performance will be validated through accuracy metrics and summarized in a final report.

Deliverables: Submit a trained prediction model, code scripts, accuracy results, and a comprehensive project report.

Milestones

1. Load and explore dataset

10 marks 6d

Load the provided heart failure clinical records dataset into a pandas DataFrame. Perform exploratory data analysis by summarizing key statistics, visualizing feature distributions, and identifying missing values. Submit your Jupyter notebook with code, output, and concise observations about data shape, feature types, and any anomalies found.

2. Preprocessing and scaling

10 marks 6d

Clean the dataset by handling missing values and encoding categorical variables as needed. Apply appropriate feature scaling (e.g., StandardScaler or MinMaxScaler) to numerical columns. Submit your notebook showing preprocessing steps, code, and confirmation that all features are numeric and scaled, ready for modeling.

3. Train/test split

10 marks 6d

Split the preprocessed dataset into training and testing sets using an 80/20 ratio, ensuring class distribution is maintained (stratification). Document the code used and display the resulting set sizes and class balance in each split. Submit your notebook with code and a brief summary verifying correct partitioning.

4. Accuracy validation

10 marks 6d

Train a classification model (e.g., logistic regression or random forest) on the training set and evaluate its accuracy on the test set. Submit your notebook with model training code, test predictions, and the calculated accuracy score. Clearly indicate the achieved accuracy and confirm it was computed on the test data only.

5. Report results

10 marks 6d

Summarize your findings in a concise report, including the final model's accuracy, any notable patterns or insights from the data, and potential limitations. Submit your notebook with a markdown cell or a separate PDF containing your results, supporting visualizations, and a brief discussion of model performance and next steps.

Open internships using this project -->

Upcoming sessions

Session	Window	Enrolled
Heart Failure Prediction System	11 Jun 2026 to 10 Jun 2028	0

Skills you'll learn

Classification algorithmsFeature selectionModel performance evaluation

Tools used

Python Scikit-learn Pandas

Prerequisites

Heart disease dataset familiarity ML basics

Available mentors

Priyang Kumar

Free

You'll earn — Certificate (PDF)

AICTE-aligned Project Completion Certificate

A formal, audit-ready PDF certificate issued by Assessfy + your institute on successful completion. Includes AICTE credit hours, your evaluator's signature, and a QR code for third-party verification.

AICTE-aligned

Certificate of Project Completion

This is to certify that

has successfully completed the project

Heart Failure Prediction System

Scan to Verify

Date

Signature

Auto-issued on completion QR-verifiable

You'll earn — Digital Badge

Shareable LinkedIn / Resume Skill Badge

A compact, verifiable Open-Badges-2.0-compliant digital credential. Add to your LinkedIn profile, GitHub README, or resume in one click. Recruiters can validate authenticity via a unique URL.

Moderate

Heart Failure Prediction System

Assessfy

Auto-issued on completion One-click LinkedIn add