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)

OKTAYBBS/DataScientst-models

DataScientst -- 30 Projects, 34 Trained Models

Repository Structure

Models & Metrics

Regression

Classification

Clustering

NLP

Deep Learning

Usage

Links

OKTAYBBS/
DataScientst-models