jf20541

BERT-NLP-Deployment-FastAPI-Docker

FastAPI BERT deployment, Logistic Regression, and Multinomial NaiveBayes to predict NLP sentiment analysis with an Evaluation Accuracy of 94.15%, 89.31%, 84.77, respectively.

BGRU-BLSTM-GloVe-fastText-NLP

Compared model's performance using Bidirectional GRU and LSTM with GloVe and fastText pre-trained word embeddings on sentiment dataset. BGRU-GloVe Embeddings outperformed with an accuracy of 90.99%.

CointegratedPairsTrading

Verify if BTC and ETH pair of crypto are cointegrated. Calculating the hedge ratio by fitting a linear regression and check if the spread is stationary using Augmented Dickey-Fuller Test

DNNHyperparameterTuning

Optimize DNN hyperparameters using TPE-Bayesian optimization based on kernel fitting with 0.9625 ROC-AUC. The model uses metric values through search process probabilistic to converge to the optimal combination of hyperparameters.

EDA-FeatureEngineering-FeatureSelection

Different ways of Data Collection, Exploratory Data Analysis (EDA), Feature Engineering, and Feature Importance on 4 datasets. Seek patterns by visualization, test hypothesis, check assumptions, etc

LogisticRegressionPyTorch

Predict binary values using a Log-Reg with PyTorch and from scratch. Used Optuna to find the optimal model (SVM, Decision-Tree, Log-Reg) and seek optimal hyper-parameters.

MonteCarloPortfolioOptimization

Optimal Portfolio by assigning random weights to each SPDR Sector ETF with Returns 44.52%, Risk: 0.20, Sharpe Ratio 2.84. Iterating n-times to increase the accuracy for portfolio optimization and evaluating portfolio's Sharpe-Ratio.

Optimal-RegressionModel-HyperParameters-Flask-Azure-Docker

Seek the optimal regression model while optimizing model's hyperparameters using Tree-Structured Parzen Estimator Approach (TPE) that applies the Bayes Rule and evaluating performance with RMSE.

Pruning-DeepNeuralNetwork

Eliminated parameters with two pruning methods (weight and units) sparsity (k%) with minimal loss in performance.

RandomForest-Optimal-HyperParameter

RandomForest Model to classify binary target values (pos&neg) return with calculated features (RSI, MA, MACD, etc). Tuning hyperparameters with Bayesian Optimization, GridSearchCV, and RandomSearchCV.

jf20541/CointegratedPairsTrading

Verify if BTC and ETH pair of crypto are cointegrated. Calculating the hedge ratio by fitting a linear regression and check if the spread is stationary using Augmented Dickey-Fuller Test

jf20541/BGRU-BLSTM-GloVe-fastText-NLP

Compared model's performance using Bidirectional GRU and LSTM with GloVe and fastText pre-trained word embeddings on sentiment dataset. BGRU-GloVe Embeddings outperformed with an accuracy of 90.99%.

jf20541/LogisticRegressionPyTorch

Predict binary values using a Log-Reg with PyTorch and from scratch. Used Optuna to find the optimal model (SVM, Decision-Tree, Log-Reg) and seek optimal hyper-parameters.

jf20541/BERT-NLP-Deployment-FastAPI-Docker

FastAPI BERT deployment, Logistic Regression, and Multinomial NaiveBayes to predict NLP sentiment analysis with an Evaluation Accuracy of 94.15%, 89.31%, 84.77, respectively.

jf20541/DNNHyperparameterTuning

Optimize DNN hyperparameters using TPE-Bayesian optimization based on kernel fitting with 0.9625 ROC-AUC. The model uses metric values through search process probabilistic to converge to the optimal combination of hyperparameters.

jf20541/MonteCarloPortfolioOptimization

Optimal Portfolio by assigning random weights to each SPDR Sector ETF with Returns 44.52%, Risk: 0.20, Sharpe Ratio 2.84. Iterating n-times to increase the accuracy for portfolio optimization and evaluating portfolio's Sharpe-Ratio.

jf20541/Optimal-RegressionModel-HyperParameters-Flask-Azure-Docker

Seek the optimal regression model while optimizing model's hyperparameters using Tree-Structured Parzen Estimator Approach (TPE) that applies the Bayes Rule and evaluating performance with RMSE.

jf20541/RandomForest-Optimal-HyperParameter

RandomForest Model to classify binary target values (pos&neg) return with calculated features (RSI, MA, MACD, etc). Tuning hyperparameters with Bayesian Optimization, GridSearchCV, and RandomSearchCV.

jf20541/EDA-FeatureEngineering-FeatureSelection

Different ways of Data Collection, Exploratory Data Analysis (EDA), Feature Engineering, and Feature Importance on 4 datasets. Seek patterns by visualization, test hypothesis, check assumptions, etc

jf20541/Pruning-DeepNeuralNetwork

Eliminated parameters with two pruning methods (weight and units) sparsity (k%) with minimal loss in performance.

jf20541/DeepNeuralNetworks

Predicted a binary classification problem with an Artificial Neural Network and outputted 0.8733 ROC-AUC. Implemented scheduler to reduce learning rate, used criterion as a Binary Cross-Entropy and Adam Optimizer

jf20541/jf20541

jf20541/KMeansDbscanPCA

Two Unsupervised Learning clustering models (KMeans & DBSCAN) and PCA for reduction dimensionality. Applied techniques to find the optimal hyperparameters and visualized the outputs.

jf20541/KNearestNeighbors

K-Nearest Neighbors from scratch with different Distance Metrics and an Accuracy: 96.67%. Distance Metrics [Euclidean, Minkowski, Manhattan, Hamming] to find the optimal accuracy.

jf20541/Multi-LinearRegressionModel

Find the relationship between multiple independent variables to a dependent variable (GDP Growth %). Validated assumptions and tested hypothesis Variance Inflation Factor, Breusch–Pagan, Ljung-Box & Anderson-Darling Test

jf20541/PCARiskModel-SectorETF

jf20541/PortfolioVarianceBW

Collected Bridgewater Associate's 13F SEC Filing for 2021Q1 top 25 holdings based on percentage weight. Calculated factors covariance, factor exposures, idiosyncratic variances, rebalanced weights for Portfolio Variance.

jf20541/XGBoost-OptimalHyperParameters-AWS-SageMaker

XGBoost Classifier to predict binary target for vehicle insurance with a 0.9085 ROC-AUC by optimizing hyperparameters with Bayesian Optimization. Reduced overfitting by implementing Stratified K-Fold.