Category | Technologies |
---|---|
Programming Languages & Software | Python, SQL, Stata, EViews, C++, R, Latex, Office} |
Tools & frameworks | Numpy, Pandas, Scikit-learn, Tensorflow, Surprise, Keras, Scalar, PyTorch, SciPy, Spark, NLTK, Open AI, Matplotlib, Seaborn, Plotly, Statsmodels |
AI/Machine Learning | Supervised Learning (Linear, Lasso, Ridge and Polynomial Regression, Logistic Regression, SVM, Decision Tree, Naive Bayes, KNN); Unsupervised Learning (K-means, DBSCAN, PCA); Neural Networks & Deep Learning (RNN, CNN, MLP, LSTM, GANs,Transformers, Autoencoders, and Diffusion), image processing, computer vision; Ensemble Learning (Bootstrapping, Random Forest, Voting classifier, bagging, XGBoost, GradientBoost, LightGBM, AdaBoost, Stacking); Natural Language Processing (NLP), text classification, LLM, Model tuning and evaluation |
Preprocessing, visualization and Data Analysis | Data Extraction, Visualization, Transformation, Imputation, Centering, Scaling, Labeling, Feature Engineering, Feature Permutation, SHAP |
Causal Inference, counterfactual and policy analysis | Difference-in-Difference, Synthetic Control Methods (SCM), Instrumental Variables, A/B test |
Time Series | Dynamic Linear Regression Models (ARDL, AR, MA, ARMA, ARIMA), Dynamic Skedastic & Correlation Models (ARCH, GARCH, EGARCH, Variance Targeting), Multivariate Time Series (VAR, ECM, VECM, Granger Causality, Regime Switching Models), Panel data |
Data science , machine learning,
Ph.D. in economics,
bachelor in engineering,
with over 15 years of experience
- United States
- in/reza-zamani-099b73195
Pinned Loading
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AIML-UC-Berkeley-feature-engineering-and-exploratory-Data-Analysis-EDA-to-driver-s-decision-to-ac
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AIML--UC-Berkeley--What-drives-the-price-of-a-car
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AIML-UC-Berkeley--campaign-marketing-analysis-in-banking-system
AIML-UC-Berkeley--campaign-marketing-analysis-in-banking-system PublicJupyter Notebook
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