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Liver Cirrhosis Prediction System using Random Forest

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

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About this project

Liver Cirrhosis Prediction System using Random Forest

Objective: Design a predictive model to assess the risk of liver cirrhosis using Random Forest classification.

Context: Liver cirrhosis is a significant health concern in India, often detected late due to limited diagnostic resources. Early risk prediction can guide timely medical interventions and reduce complications.

What you'll build: You'll preprocess and visualize medical data using pandas and Seaborn, engineer features, and train a Random Forest classifier with scikit-learn. The model's accuracy will be evaluated and prepared for deployment as a practical diagnostic aid.

Deliverables: Trained prediction model, data visualizations, and deployment-ready codebase.

Milestones

No submission required for this milestone.

2. Data loading and preprocessing

10 marks 6d

Load the liver cirrhosis dataset into your environment, handle missing values, and encode categorical variables as needed. Submit your cleaned dataset and a brief summary of preprocessing steps. 'Done' means the data is ready for modeling and all preprocessing steps are clearly documented.

3. Feature engineering

10 marks 6d

Create new features relevant to liver cirrhosis prediction, such as ratios or transformations of clinical variables. Submit the updated dataset and a short explanation of each engineered feature. 'Done' means features are justified and improve the dataset's predictive potential.

4. Train/validate Random Forest

10 marks 6d

Split your data into training and validation sets, then train a Random Forest classifier using the processed features. Submit your code and a summary of model parameters. 'Done' means the model is trained, reproducible, and ready for evaluation.

5. Accuracy evaluation

10 marks 6d

Evaluate your Random Forest model's accuracy using the validation set and report metrics such as accuracy, precision, recall, and confusion matrix. Submit these results with a brief interpretation. 'Done' means metrics are clearly presented and interpreted in the context of liver cirrhosis prediction.

6. Model deployment

10 marks 6d

Deploy your trained Random Forest model as a REST API or web application that accepts patient data and returns a cirrhosis prediction. Submit deployment code and usage instructions. 'Done' means the model is accessible, functional, and can be tested with sample inputs.

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Upcoming sessions

Session	Window	Enrolled
Liver Cirrhosis Prediction System using Random Forest	11 Jun 2026 to 10 Jun 2028	0

Skills you'll learn

Classification modelsData visualizationPython ML libraries

Tools used

Python Pandas Scikit-learn Seaborn

Prerequisites

Understanding of liver dataset Random Forest algorithm

Available mentors

Priyang Kumar

Free

You'll earn — Certificate (PDF)

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Liver Cirrhosis Prediction System using Random Forest

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