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Machine Learningintermediate

Predictive Modeling for Cancer Risk Assessment Using Machine Learning

This project develops a machine learning model to predict cancer risk levels (High, Medium, Low) based on demographic, behavioral, and health data. It addresses class imbalance using techniques like SMOTE and optimizes model performance with hyperparameter tuning, providing crucial insights for early detection and intervention.

22 lectures

What You Will Learn

Mastering data preprocessing and exploratory data analysis (EDA) techniques for health-related datasets.
Implementing various machine learning models for classification tasks, including Logistic Regression, Random Forests, and XGBoost.
Applying SMOTE to address class imbalance problems in datasets.
Optimizing model performance using hyperparameter tuning with Optuna.
Building interactive data visualizations using Streamlit for improved data insights.
Deploying machine learning models as Streamlit applications on AWS EC2.
Evaluating and interpreting model performance using metrics such as recall and F1-score, particularly for minority classes.

System Architecture

Predictive Modeling for Cancer Risk Assessment Using Machine Learning Architecture Diagram

High-level architecture overview of the Predictive Modeling for Cancer Risk Assessment Using Machine Learning .

What You'll Build

  • A machine learning model for predicting cancer risk levels.
  • Interactive EDA visualizations to understand risk factors.
  • A Streamlit application for visualizing model predictions.
  • A deployed model on AWS EC2 accessible to healthcare professionals.
Predictive Modeling for Cancer Risk Assessment Using Machine Learning
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