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
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]])
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
