| --- |
| license: mit |
| language: |
| - en |
| tags: |
| - sklearn |
| - tensorflow |
| - keras |
| - random-forest |
| - cnn |
| - clustering |
| - nlp |
| - computer-vision |
| - recommendation-system |
| - time-series |
| - streamlit |
| pipeline_tag: tabular-classification |
| --- |
| |
| # 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/ -- 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) |
| ``` |
|
|
| ## 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 |
| |
| # Download from a category folder |
| model_path = hf_hub_download( |
| repo_id="OKTAYBBS/DataScientst-models", |
| filename="regression/gold_model.pkl" |
| ) |
| |
| model = joblib.load(model_path) |
| prediction = model.predict([[1500, 70, 20, 1.1]]) |
| ``` |
|
|
| ```python |
| # For Keras models |
| import tensorflow as tf |
| |
| model_path = hf_hub_download( |
| repo_id="OKTAYBBS/DataScientst-models", |
| filename="deep_learning/pneumonia_model.keras" |
| ) |
| model = tf.keras.models.load_model(model_path) |
| ``` |
|
|
| ## Links |
|
|
| - **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) |
|
|
