Assessfy Pvt. Ltd Moderate 6 milestones 50 marks

Chronic Obstructive Pulmonary Disease Prediction System

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

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About this project

Chronic Obstructive Pulmonary Disease Prediction System

Objective: Develop a predictive system to identify individuals at risk of Chronic Obstructive Pulmonary Disease (COPD).

Context: COPD is a leading cause of morbidity in India, often going undiagnosed until advanced stages due to lack of early screening tools. Early prediction can help reduce healthcare burdens and improve patient outcomes.

What you'll build: You will preprocess real-world health datasets, select relevant features, and apply classification algorithms such as Random Forest and SVM using Python libraries like pandas and scikit-learn. The system will be trained, evaluated, and deployed in a Jupyter Notebook environment for practical use.

Deliverables: Working prototype with code, evaluation report, and deployment-ready notebook.

Milestones

Read the project brief and milestone requirements to understand the scope and deliverables for the COPD Prediction System. No submission is required for this stage.

2. Data collection

10 marks 6d

Collect a structured dataset relevant to COPD prediction, including patient demographics, clinical measurements, and risk factors. Submit the cleaned dataset (CSV or similar format) along with a brief data source description and summary statistics to demonstrate data completeness and relevance.

3. Feature selection

10 marks 6d

Analyze the collected dataset to identify and select features most predictive of COPD. Submit a list of selected features with a short justification for each, supported by exploratory data analysis or statistical methods (e.g., correlation, feature importance scores).

4. Train/test split

10 marks 6d

Split your dataset into training and testing subsets using an appropriate ratio (e.g., 80/20), ensuring class balance is maintained. Submit the code used for the split and summary statistics (e.g., class distribution) for both sets to verify correctness.

5. Model training & evaluation

10 marks 6d

Train at least one machine learning model on the training data to predict COPD, and evaluate its performance on the test set using metrics such as accuracy, precision, recall, and ROC-AUC. Submit your model code, evaluation results, and a brief interpretation of the findings.

6. Deployment phase

10 marks 6d

Deploy your trained COPD prediction model as a REST API or interactive web application that accepts patient data and returns a prediction. Submit a link or deployment instructions, along with sample input/output screenshots to demonstrate successful end-to-end functionality.

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Upcoming sessions

Session	Window	Enrolled
Chronic Obstructive Pulmonary Disease Prediction System	11 Jun 2026 to 10 Jun 2028	0

Skills you'll learn

Data preprocessingClassification algorithms (e.g.Random ForestSVM)Python (pandasscikit-learn)

Tools used

Pythonscikit-learnpandas Jupyter Notebook

Prerequisites

Medical data interpretation ML basics Data cleaning techniques

Available mentors

Priyang Kumar

Free

You'll earn — Certificate (PDF)

AICTE-aligned Project Completion Certificate

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AICTE-aligned

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Chronic Obstructive Pulmonary Disease Prediction System

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