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
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]])
# 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