🚀 Passionate about building real-world Machine Learning solutions and deploying them as interactive applications.
💡 Skilled in:
- Machine Learning & Deep Learning
- Time Series Forecasting (LSTM)
- Computer Vision (CNN)
- Data Analysis & Clustering
- Streamlit ML Apps
This project uses LSTM (Long Short-Term Memory) to predict time series values.
- Data generation (sine wave)
- Sliding window technique
- Data preprocessing
- LSTM model building
- Training & validation
- Prediction visualization
- LSTM layer
- Dropout layer
- Dense layers
- MAE: ~0.012
- MSE: ~0.00020
- High accuracy in capturing patterns
LSTM_Time_Series_Forecasting.ipynb
Built a deep learning model to classify geometric shapes from images.
- TensorFlow / Keras
- NumPy
- Matplotlib
CNN_Image_Classification.ipynb
Applied clustering to group countries based on demographic features.
- Data preprocessing
- Elbow method
- KMeans clustering
Predicted student performance using supervised ML models.
- Linear Regression
- Random Forest
- Gradient Boosting
Linear Regression performed best.
Analyzed student behavior patterns using clustering.
- DBSCAN
- Feature scaling
- Data visualization
Predicted housing prices using regression techniques.
- Linear regression
- Feature selection
- Correlation analysis
- R² ≈ 0.74
Boston_Housing_Prediction.R
✨ I am currently open for:
- Freelance projects
- Entry-level Data Science roles
- 💻 GitHub: https://github.com/sabrynkhatr696-design
- 🔗 LinkedIn: ( https://www.linkedin.com/in/sabrin-kater-4a5050385/ )
⭐ Always learning, building, and improving!