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**finalen Mock-Interviews bei Masterschool**.  \nZiel war es, mithilfe von Machine Learning vorherzusagen, **ob ein Kreditantrag genehmigt oder abgelehnt wird**.\n\nDabei lag mein Fokus nicht nur auf der Modellgenauigkeit, sondern darauf,  \n**einen klaren, strukturierten und nachvollziehbaren Analyseprozess** zu zeigen.\n\n---\n\n## 🧭 Ziele des Projekts\n\n- 🧹 Daten aufbereiten und bereinigen  \n- 🔍 Explorative Datenanalyse durchführen  \n- 🔢 Kategorische Variablen encodieren  \n- 🌳 Klassifikationsmodell (Decision Tree) trainieren  \n- 📈 Modellleistung evaluieren \u0026 interpretieren  \n- 🧠 Wichtigste Einflussfaktoren identifizieren  \n\n---\n\n## 💾 Datensatz\n\n**Quelle:** Masterschool Mock Interview Dataset  \n**Größe:** 563 Zeilen · 13 Spalten  \n\n| Spalte | Beschreibung |\n|--------|---------------|\n| `loan_id` | Eindeutige Kredit-ID |\n| `gender` | Geschlecht |\n| `married` | Familienstand |\n| `dependents` | Anzahl der unterhaltsberechtigten Personen |\n| `education` | Bildungsstatus |\n| `self_employed` | Selbstständig (Ja/Nein) |\n| `applicant_income` | Einkommen des Antragstellers |\n| `coapplicant_income` | Einkommen des Mit-Antragstellers |\n| `loan_amount` | Kreditsumme (in Tausend) |\n| `loan_amount_term` | Laufzeit des Kredits (Monate) |\n| `credit_history` | Kredit-Historie (1 = gut, 0 = schlecht) |\n| `property_area` | Gebiet (Urban / Semi Urban / Rural) |\n| `loan_status` | Zielvariable (1 = bewilligt, 0 = abgelehnt) |\n\n---\n\n## ⚙️ Vorgehensweise\n\n1. **Datenexploration** – Überblick über Struktur, Datentypen \u0026 fehlende Werte  \n2. **Data Cleaning** – Fehlende Werte mit Median/Modus ersetzt  \n3. **Encoding** – Kategorische Variablen per *One-Hot-Encoding* umgewandelt  \n4. **Train/Test Split** – 80/20-Aufteilung für Training \u0026 Evaluation  \n5. **Modelltraining** – Decision Tree Classifier verwendet  \n6. **Evaluation** – Accuracy, Confusion Matrix, Feature Importance analysiert  \n\n---\n\n## 🌳 Modell \u0026 Ergebnisse\n\n- **Algorithmus:** Decision Tree Classifier  \n- **Accuracy:** ~80 %  \n- **Confusion Matrix:** Zeigt, dass bewilligte Kredite sehr gut erkannt werden  \n- **Top Features:** Kredit-Historie, Kreditsumme, Einkommen  \n\n### 🔍 Insights\n- Eine gute Kredit-Historie ist der stärkste Indikator für Kreditbewilligung.  \n- Einkommen \u0026 Kreditsumme spielen ebenfalls eine zentrale Rolle.  \n- Weitere Merkmale wie Familienstand oder Se\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcinnaavox%2Floan-prediction","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcinnaavox%2Floan-prediction","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcinnaavox%2Floan-prediction/lists"}