Predicting different types of skin disorders using machine learning models trained on dermatological datasets.
Skin disorders are common health issues and can be caused by genetic, environmental, or lifestyle factors.
Accurate prediction and classification of skin conditions can help in early diagnosis and treatment planning.
This project applies data science and machine learning to:
- Analyze dermatology datasets
- Identify key features influencing diagnosis
- Build predictive models for classifying skin disorders
Goal: Assist healthcare professionals with decision support systems for improved dermatological care.
- Programming: Python (Jupyter Notebook)
- Libraries:
- Data Analysis → pandas, numpy
- Visualization → matplotlib, seaborn
- Machine Learning → scikit-learn, xgboost, lightgbm
- Model Evaluation → accuracy, precision, recall, F1-score, ROC-AUC
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Exploratory Data Analysis (EDA)
- Distribution of skin disorder cases
- Correlation between clinical features
- Visualizations of feature importance
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Data Preprocessing
- Handling missing values
- Encoding categorical variables
- Feature scaling and selection
-
Modeling
- Logistic Regression
- Random Forest
- XGBoost & LightGBM
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Model Evaluation
- Train-test split & cross-validation
- Metrics: Accuracy, Precision, Recall, F1-score, ROC-AUC
- Confusion matrix & ROC curves
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Insights & Business Value
- Key clinical indicators of specific skin disorders
- Supporting dermatologists with predictive analytics
- Distribution of skin disorder classes
- Correlation heatmaps
- ROC & Precision-Recall curves
- Confusion matrices
- Feature importance plots
- Deploy as a Flask/Django web app
- Integrate with real clinical datasets
- Use CNN models with image data for diagnosis
- Hyperparameter optimization with Optuna
Ari R.
Data Scientist
🔗 GitHub | 📧 [email protected]
✨ Developed with passion for Data Science & Machine Learning. "# Skin Disorder Prediction" "# SkinDisorder_Prediction"