Überblick Machine Learning Projekte mit Fokus auf Predictive Analytics und Modellentwicklung. Technologien ML Libraries: Scikit-learn, TensorFlow Python: NumPy, Pandas Evaluation: Cross-validation, Metrics Deployment: Flask, Docker Projekt-Bereiche Supervised Learning Regression: Preisvorhersagen, Zeitreihen Classification: Kunden-Churn, Sentiment Analysis Ensemble Methods: Random Forest, Gradient Boosting Unsupervised Learning Clustering: K-Means, DBSCAN Dimensionality Reduction: PCA, t-SNE Anomaly Detection: Outlier-Erkennung Model Evaluation Train/Test Splits Cross-Validation Hyperparameter Tuning Model Comparison Key Skills 🤖 Model Development 📊 Feature Engineering 🔧 Hyperparameter Optimization 📈 Performance Evaluation 🚀 Model Deployment Methodologie Problem Definition Data Collection & Cleaning Exploratory Data Analysis Feature Engineering Model Training & Evaluation Deployment & Monitoring GitHub Code und Notebooks auf GitHub