Tools: Jupyter / JupyterLab, GitHub, R Studio, and Watson Studio
Libraries: Pandas, NumPy, Matplotlib, Seaborn, Folium, ipython-sql, Scikit-learn, ScipPy, etc.
Projects: random album generator, predict housing prices, best classifier model, Predicting successful rocket landing, dashboa rd and interactive map
Utilizes statistical and machine learning techniques and high-performance data architectures and technologies such as SAS, R, Python, Spark, and SPSS, to address business problems and analyze large, unstructured data sets.
Technical Skills:
- Artificial Intelligence
- Bokeh
- Classification
- Clustering
- Data Analysis
- Database
- Data Science
- Data Visualization
- Db2
- Folium
- Foursquare
- IBM Cloud
- Jupyter
- Location
- Machine Learning
- Matplotlib
- Methodology
- ML
- Notebook
- Numpy
- Pandas
- Python
- Recommender Systems
- Regression
- RStudio
- Scikit-learn
- SCIPy
- Seaborn
- SQL
- Studio
- Watson
- Zeppelin