• Deepfake Image Detection App

    Deepfake Image Detection App

    Deployed app that is associated with the Deepfake Image Detection Notebook. This app was constructed using Flask along with manually built html pages. The app allows a user to input an image and uses a finely tuned CNN to predict whether the image is real or a deepfake.

    Note: As of this moment, this app is designed to work with high quality deepfake images. Screengrabs or poorly photoshopped images don't play well with the model.
  • Deepfake Image Detection Notebook

    Deepfake Image Detection Notebook

    Built a system that can predict whether an image was real or a deepfake with 97% accuracy and deployed the model to an associated app.

    - Obtained 140k+ images, both real and deepfakes, by combining multiple datasets of human face images
    - Processed the images into standard size and converted to arrays for modeling using ImageDataGenerator
    - Developed several high accuracy CNN models to predict between real and deepfake images achieving a 97% accuracy
    - Deployed a working app where users can test the model with a single image
  • Detecting Disaster Tweets

    Detecting Disaster Tweets

    Used NLP & Deep Learning to analyze disaster related tweets to make a social media monitor that determines whether a tweet is in reference to a disaster.

    - Utilized NLP & visualizations to analyze a dataset containing over 11,000 tweets associated with disaster keywords
    - Gained insight into the context and sentiment of tweets in the dataset by using NLP to find trends of disaster-related tweets
    - Developed several types of machine learning models resulting in an 89% accurate Stacking Classifier
    - Implemented several LSTM Networks for classification resulting in 90% accurate predictions
  • Global Terrorism Analysis

    Global Terrorism Analysis

    Analyzed terrorist attack data and used various machine learning models to determine the factors that make an attack successful in order to help evaluate current security protocols.

    - Obtained, scrubbed, and analyzed data on 181,000 terrorist attacks between 1997 and 2017
    - Used various advanced feature engineering methods to prepare the data for modeling including KNNImputer & condensing columns
    - Created several exploratory visuals to understand trends of successful attacks for the purpose of determining prevention methods
    - Developed a series of machine learning models for classification resulting in a 96% accurate Stacking Classifier