Liver Disease Prediction Using ML Models. This is a Machine Learning project predicting Liver Disease i also provide model deployment , Pramod kusmude

This project focuses on predicting liver patient outcomes using machine learning.

Task 1 Data Exploration and Analysis :- Understanding the Data Identifying Data Quality Issues Descriptive Statistics Visualizing Data Distributions Feature Engineering Correlation Analysis Handling Categorical Variables Detecting Patterns and Trends Model Assumptions and Constraints Decision-Making and Business Insights Communication and Reporting

Task 2 Data preprocessing :- Handling Missing Values Encoding Categorical Variables Scaling Features Handling Outliers Feature Engineering Splitting Data

Task 3 Model Selection :- Logistic Regression Random Forest Decision Tree Support Vector Machine (SVM) Gradient Boosting

Task 4 HyperParameter Tuning :- Model Performance Improvement Avoiding Overfitting and Underfitting Model Robustness Resource Utilization Generalization to Unseen Data Model Interpretability Customization for Specific Tasks

Task 5 Model Evaluation :- Model evaluation ROC Curve and AUC-ROC Score Cross-Validation Feature Importance Hyperparameter Tuning Evaluation Model Comparison

Task 6 Next Steps:- Fine-tuning: Further tune hyperparameters of Random Forest for potential performance improvement. Validation: Use cross-validation to assess the model's generalization performance. Ensemble Methods: Explore ensemble methods like model stacking or boosting for potential improvements. Interpretation: If model interpretability is crucial, consider Logistic Regression, but be aware that it may sacrifice some predictive performance.