chineseballer06
Data scientist with a passion for solving problems through analytics. Academic background in mathematics along with a career in professional basketball.
Denver, Colorado
Pinned Repositories
applying-gradient-descent-data-science-intro-000
applying-gradient-descent-lab-data-science-intro-000
applying-nearest-neighbors-data-science-intro-000
calculating-distance-data-science-intro-000
King-County-Housing
Predicted the sales prices of homes in King County using multivariate linear regression models.
Lending-Club-Loan-Defaults
Applied deep learning models to classify which types of loans would be more likely to default. Cleaned and analyzed loan data using Pandas and NumPy to prepare data for classification models. Explored different features of a loan with Matplotlib and Seaborn to gain further insight for feature selection. Built different MLP models from Keras in order to determine which loans would default.
NBA-Shot-Analysis
Analyzed NBA data to gain insight on which shooting features indicated a made field goal attempt. Mined data for important trends and features using Pandas, NumPy, and Matplotlib to be plugged into machine learning models. Ran various machine learning models such as XGBoost, Random Forest, and Logistic Regression to predict whether a shot would be successful or not. Optimized different machine learning models using Scikit-Learn’s GridSearch in order to tune hyperparameters.
Statistical-Analysis-of-Northwind-Database
Performed various hypothesis testing to answer questions regarding statistical significance using the Northwind database. Used SQLAlchemy and Python to query and examine the data from the Northwind database in order to perform statistical analysis. Goal was to answer if discounts have a statistically significant effect on the number of products customers order by conducting hypothesis testing.
Stock-Analysis
Predicted and analyzed future stock prices using Monte Carlo simulations. Used pandas-datareader package to scrape historical stock prices of various companies. Calculated CAGR and Annualized Volatility to be fed into a Monte Carlo simulation.
Traffic-Accidents
Predicted where accidents in the UK are more likely to occur using satellite imagery. Extracted important traffic data features using machine learning algorithms to be used in deep learning models. Scraped satellite images throughout the UK using Google Maps Static API to train different CNN models. Constructed a mixed-input deep learning model using Keras functional API to predict where dangerous accidents are likely to occur.
chineseballer06's Repositories
chineseballer06/Stock-Analysis
Predicted and analyzed future stock prices using Monte Carlo simulations. Used pandas-datareader package to scrape historical stock prices of various companies. Calculated CAGR and Annualized Volatility to be fed into a Monte Carlo simulation.
chineseballer06/Traffic-Accidents
Predicted where accidents in the UK are more likely to occur using satellite imagery. Extracted important traffic data features using machine learning algorithms to be used in deep learning models. Scraped satellite images throughout the UK using Google Maps Static API to train different CNN models. Constructed a mixed-input deep learning model using Keras functional API to predict where dangerous accidents are likely to occur.
chineseballer06/NBA-Shot-Analysis
Analyzed NBA data to gain insight on which shooting features indicated a made field goal attempt. Mined data for important trends and features using Pandas, NumPy, and Matplotlib to be plugged into machine learning models. Ran various machine learning models such as XGBoost, Random Forest, and Logistic Regression to predict whether a shot would be successful or not. Optimized different machine learning models using Scikit-Learn’s GridSearch in order to tune hyperparameters.
chineseballer06/Lending-Club-Loan-Defaults
Applied deep learning models to classify which types of loans would be more likely to default. Cleaned and analyzed loan data using Pandas and NumPy to prepare data for classification models. Explored different features of a loan with Matplotlib and Seaborn to gain further insight for feature selection. Built different MLP models from Keras in order to determine which loans would default.
chineseballer06/Statistical-Analysis-of-Northwind-Database
Performed various hypothesis testing to answer questions regarding statistical significance using the Northwind database. Used SQLAlchemy and Python to query and examine the data from the Northwind database in order to perform statistical analysis. Goal was to answer if discounts have a statistically significant effect on the number of products customers order by conducting hypothesis testing.
chineseballer06/dsc-04-45-02-introduction-to-autoencoders-online-ds-ft-011419
chineseballer06/dsc-04-45-04-generating-word-embeddings-lab-online-ds-ft-011419
chineseballer06/dsc-04-46-02-sequence-model-use-cases-online-ds-ft-011419
chineseballer06/dsc-04-46-03-understanding-recurrent-neural-networks-online-ds-ft-011419
chineseballer06/dsc-04-46-04-LSTMs-and-GRUs-online-ds-ft-011419
chineseballer06/dsc-04-46-05-LSTMs-and-GRUs-lab-online-ds-ft-011419
chineseballer06/dsc-04-46-06-bidirectional-sequence-models-online-ds-ft-011419
chineseballer06/dsc-04-46-07-bidirectional-sequence-models-lab-online-ds-ft-011419
chineseballer06/dsc-4-45-03-building-simple-autoencoder-lab-online-ds-ft-011419
chineseballer06/dsc-4-45-04-building-deep-autoencoder-lab-online-ds-ft-011419
chineseballer06/dsc-4-45-05-denoising-autoencoders-online-ds-ft-011419
chineseballer06/dsc-4-45-06-denoising-autoencoders-lab-online-ds-ft-011419
chineseballer06/dsc-4-45-07-convolutional-autoencoders-online-ds-ft-011419
chineseballer06/dsc-4-45-08-convolutional-autoencoders-lab-online-ds-ft-011419
chineseballer06/dsc-4-45-09-neural-language-translation-seq2seq-codelaong-online-ds-ft-011419
chineseballer06/dsc-4-46-08-section-recap-online-ds-ft-011419
chineseballer06/dsc-4-47-10-section-recap-online-ds-ft-011419
chineseballer06/dsc-4-48-02-deep-generative-models-online-ds-ft-011419
chineseballer06/dsc-4-48-03-introduction-to-variational-auto-encoders-online-ds-ft-011419
chineseballer06/dsc-4-48-04-deep-convolutional-variational-autoencoders-codealong-online-ds-ft-011419
chineseballer06/dsc-4-48-05-introduction-to-generative-adversarial-networks-online-ds-ft-011419
chineseballer06/dsc-4-48-06-building-simple-GAN-in-keras-codealong-online-ds-ft-011419
chineseballer06/dsc-4-48-07-section-recap-online-ds-ft-011419
chineseballer06/dsc-implementing-recommender-systems-online-ds-ft-011419
chineseballer06/Pricing-Diamonds
Predicted the prices of "Round" cut diamonds using multivariate linear regression models.