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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)
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