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README.md

@@ -1,7 +1,6 @@
 ---
 license: mit
 language:
-  - tr
   - en
 tags:
   - sklearn
@@ -18,104 +17,79 @@ tags:
 pipeline_tag: tabular-classification
 ---
 
-# DataScientst -- 30 Proje, 34 Egitilmis Model
-
-10 farkli kategoride toplam 30 ML/AI projesinin egitilmis modelleri. Tum modeller Kaggle veri setleriyle egitilmistir.
-
-## Kategoriler ve Modeller
-
-### Regresyon (3 model)
-| Model | Dosya | Metrik |
-|-------|-------|--------|
-| Altin Fiyati Tahmini | `gold_model.pkl` | R² = 0.990 |
-| Ogrenci Sinav Puani | `student_model.pkl` | R² = 0.849 |
-| Uber/Taksi Ucret | `uber_model.pkl` | R² = 0.778 |
-
-### Siniflandirma (3 model)
-| Model | Dosya | Metrik |
-|-------|-------|--------|
-| Mobil Fiyat Segmenti | `mobile_model.pkl` | Accuracy = 81.2% |
-| Sarap Kalitesi | `wine_model.pkl` | Accuracy = 67.5% |
-| Musteri Terki (Churn) | `churn_model.pkl` | Accuracy = 78.9% |
-
-### Kumeleme (3 model)
-| Model | Dosya | Metrik |
-|-------|-------|--------|
-| NBA Oyuncu Gruplama | `nba_model.pkl` + `nba_scaler.pkl` | Silhouette = 0.452 |
-| Kredi Karti Segmentasyon | `cc_model.pkl` + `cc_scaler.pkl` | Silhouette = 0.531 |
-| Spotify Sarki Kumeleme | `spotify_model.pkl` + `spotify_scaler.pkl` | Silhouette = 0.327 |
-
-### Bilgisayarli Goru (1 model)
-| Model | Dosya | Metrik |
-|-------|-------|--------|
-| Maske Tespiti | `mask_model.pkl` | Accuracy = 82.5% |
-
-### Dogal Dil Isleme (3 model)
-| Model | Dosya | Metrik |
-|-------|-------|--------|
-| SMS Spam Tespiti | `spam_model.pkl` + `spam_vectorizer.pkl` | Accuracy = 98.0% |
-| IMDb Duygu Analizi | `imdb_model.pkl` + `imdb_vectorizer.pkl` | Accuracy = 87.3% |
-| Sahte Haber Tespiti | `news_model.pkl` + `news_vectorizer.pkl` | Accuracy = 97.6% |
-
-### Oneri Sistemleri (3 set)
-| Model | Dosyalar |
-|-------|----------|
-| Film Onerisi | `movie_data.pkl` + `movie_similarity.pkl` |
-| Kitap Onerisi | `book_data.pkl` + `book_similarity.pkl` |
-| Sarki Onerisi | `song_data.pkl` + `song_similarity.pkl` |
-
-### Zaman Serileri (3 model)
-| Model | Dosya | Metrik |
-|-------|-------|--------|
-| Hisse Senedi (AAPL) | `stock_model.pkl` | R² = 0.975 |
-| Hava Durumu | `weather_model.pkl` | R² = 0.912 |
-| Magaza Satisi | `walmart_model.pkl` | R² = 0.767 |
-
-### Derin Ogrenme (2 model)
-| Model | Dosya | Metrik |
-|-------|-------|--------|
-| Zaturre Teshisi (CNN) | `pneumonia_model.keras` | Val Acc = 92.5% |
-| Yuz Duygu Tanima (CNN) | `fer_model.keras` | Val Acc = 65.4% |
-
-### Diger
-| Model | Dosya |
-|-------|-------|
-| Metin Uretim (Markov) | `text_robot_model.pkl` |
-
-## Repo Yapisi
+# DataScientst -- 30 Projects, 34 Trained Models
+
+Trained models from a comprehensive ML/AI portfolio spanning 10 categories. All models were trained on real Kaggle datasets.
+
+## Repository Structure
 
 ```
-regression/          -- Altin, Ogrenci, Uber tahmin modelleri
-classification/      -- Mobil, Sarap, Churn siniflandirma modelleri
-clustering/          -- NBA, Kredi Karti, Spotify kumeleme modelleri + scaler'lar
-computer_vision/     -- Maske tespiti modeli
-nlp/                 -- Spam, IMDb, Sahte Haber modelleri + TF-IDF vectorizer'lar
-recommendation/      -- Film, Kitap, Sarki oneri verileri + similarity matrisleri
-time_series/         -- Hisse, Hava Durumu, Magaza tahmin modelleri
-data_viz/            -- Gorsellestirme CSV verileri
-deep_learning/       -- CNN modelleri (Keras) + Markov metin uretici
-metrics/             -- Tum modellerin performans metrikleri (JSON, CSV, PNG)
+regression/          -- Gold, Student, Uber prediction models
+classification/      -- Mobile, Wine, Churn classification models
+clustering/          -- NBA, Credit Card, Spotify clustering models + scalers
+computer_vision/     -- Face mask detection model
+nlp/                 -- Spam, IMDb, Fake News models + TF-IDF vectorizers
+recommendation/      -- Movie, Book, Music recommendation data + similarity matrices
+time_series/         -- Stock, Weather, Store prediction models
+data_viz/            -- Visualization CSV datasets
+deep_learning/       -- CNN models (Keras) + Markov text generator
+metrics/             -- Performance metrics for all models (JSON, CSV, PNG)
 ```
 
-## Kullanim
+## Models & Metrics
+
+### Regression
+| Model | File | Metric |
+|-------|------|--------|
+| Gold Price Prediction | `regression/gold_model.pkl` | R² = 0.990 |
+| Student Exam Score | `regression/student_model.pkl` | R² = 0.849 |
+| Uber/Taxi Fare | `regression/uber_model.pkl` | R² = 0.778 |
+
+### Classification
+| Model | File | Metric |
+|-------|------|--------|
+| Mobile Price Segment | `classification/mobile_model.pkl` | Accuracy = 81.2% |
+| Wine Quality | `classification/wine_model.pkl` | Accuracy = 67.5% |
+| Customer Churn | `classification/churn_model.pkl` | Accuracy = 78.9% |
+
+### Clustering
+| Model | File | Metric |
+|-------|------|--------|
+| NBA Player Clustering | `clustering/nba_model.pkl` | Silhouette = 0.452 |
+| Credit Card Segmentation | `clustering/cc_model.pkl` | Silhouette = 0.531 |
+| Spotify Song Clustering | `clustering/spotify_model.pkl` | Silhouette = 0.327 |
+
+### NLP
+| Model | File | Metric |
+|-------|------|--------|
+| SMS Spam Detection | `nlp/spam_model.pkl` | Accuracy = 98.0% |
+| IMDb Sentiment Analysis | `nlp/imdb_model.pkl` | Accuracy = 87.3% |
+| Fake News Detection | `nlp/news_model.pkl` | Accuracy = 97.6% |
+
+### Deep Learning
+| Model | File | Metric |
+|-------|------|--------|
+| Pneumonia Detection (CNN) | `deep_learning/pneumonia_model.keras` | Val Acc = 92.5% |
+| Facial Emotion Recognition (CNN) | `deep_learning/fer_model.keras` | Val Acc = 65.4% |
+
+## Usage
 
 ```python
 from huggingface_hub import hf_hub_download
 import joblib
 
-# Kategori klasorundan modeli indir
+# Download from a category folder
 model_path = hf_hub_download(
     repo_id="OKTAYBBS/DataScientst-models",
     filename="regression/gold_model.pkl"
 )
 
-# Yukle ve kullan
 model = joblib.load(model_path)
 prediction = model.predict([[1500, 70, 20, 1.1]])
 ```
 
 ```python
-# Keras modeli icin
+# For Keras models
 import tensorflow as tf
 
 model_path = hf_hub_download(
@@ -125,14 +99,8 @@ model_path = hf_hub_download(
 model = tf.keras.models.load_model(model_path)
 ```
 
-## Teknolojiler
-
-- **ML Framework:** scikit-learn 1.6.1
-- **DL Framework:** TensorFlow / Keras
-- **Veri Kaynaklari:** Kaggle Hub API
-- **Arayuz:** Streamlit
-
-## Linkler
+## Links
 
-- **Canli Demo:** [HF Space](https://huggingface.co/spaces/OKTAYBBS/DataScientst)
-- **Kaynak Kod:** [GitHub](https://github.com/oktaybobus/DataScientst)
+- **Live Demo:** [Streamlit App](https://oktaybobus-datascientst.streamlit.app)
+- **Portfolio:** [HF Space](https://huggingface.co/spaces/OKTAYBBS/DataScientst)
+- **Source Code:** [GitHub](https://github.com/oktaybobus/DataScientst)