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.
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.
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.
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.
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 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: Predicting housing prices utilizing USA_Housing.csv. Project uses Python, Scikitlearn, Numpy, Pandas, Matplotlib, and Seaborn.
Data Capstone project utilizing Kaggle 911 call information.
Project uses Python, NumPy, Pandas, Matplotlib, and Seaborn.
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.
Experienced in machine learning, data analysis and visualization, business analysis, and website design and development.
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Comfortable with Python, SQL, Tableau, PyTorch, NumPY, Matplotlib, Seaborn, Scikit-Learn, and more, I can help you analyze and understand your data in a way that makes sense to you.
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