Portfolio
Here are a few things I've been working on:
These are some of the projects that I've completed using Python, NumPy, Pandas, Matplotlib, Seaborn, SciKit-Learn, and more. Projects feature data visualization and analysis as well as machine learning concepts including Natural Language Processing, KNN, SVM, linear regression, logistic regression, cluster analysis, support vector machines, and more.
Looking for more? Head over to my GitHub repository.
Style Transfer: PyTorch
Image Style Transfer using PyTorch This project utilizes PyTorch, python, jupyter notebooks, matplotlib, and more to transfer the style of an image to the content of another image. This code can be applied to nearly any two images.
PyTorch Image Classifier
Image Classification with PyTorch. This deep learning project uses PyTorch to classify images into 102 different species of flowers.
Project utilizes Python, PyTorch, matplotlib, json, jupyter notebooks, and is modeled on densenet161 with cross entropy loss, an Adam optimizer, and stepLR scheduler and achieves greater than 95% accuracy in 20 epochs.
Natural Language Processing Project: Yelp Reviews
This NLP project attempts to classify Yelp reviews into 1 star or 5 star categories based off of the text content in the reviews.
Project uses numpy, pandas, scikitlearn, matplotlib, seaborn, vectorization, text processing with pipeline, tf-idf (term frequency-inverse document frequency), Naive Bayes, train test split, and a random forest classifier.
Logistic Regression: Advertising
This logistic regression project attempts to predict whether an internet user will click on an ad based on the features of the user.
Project uses Pandas, Numpy, Matplotlib, Seaborn, and Scikitlearn.
Support Vector Machine Project
Support Vector Machines Project: Analyzing the Iris flower data set (Fisher's Iris Data Set) which contains 50 samples of each of three species of Iris.
This project utilizes matplotlib, seaborn, pandas, numpy, and scikit-learn and uses train test split as well as grid search to classify iris specimens.
Linear Regression Project: Housing Prices
Linear Regression: Predicting housing prices utilizing USA_Housing.csv. Project uses Python, Scikitlearn, Numpy, Pandas, Matplotlib, and Seaborn.
Data Capstone Project: 911
Data Capstone project utilizing Kaggle 911 call information.
Project uses Python, NumPy, Pandas, Matplotlib, and Seaborn.
Support Vector Machine Project: Cancer Detection
Using SVM with Python to predict whether a breast cancer tumor is malignant or benign.
Exercise uses numpy, pandas, and scikitlearn and utilizes train test split, SVC, SVM, and GridSearch to identify the best parameters for prediction.