Überblick

Machine Learning Projekte mit Fokus auf Predictive Analytics und Modellentwicklung.

Machine Learning Visualization

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

  1. Problem Definition
  2. Data Collection & Cleaning
  3. Exploratory Data Analysis
  4. Feature Engineering
  5. Model Training & Evaluation
  6. Deployment & Monitoring

GitHub

Code und Notebooks auf GitHub