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The aim is to build a predictive model that can accurately classify whether the employee is likely to leave or the employee is likely to stay in the company. This allows companies to take proactive measures, such as improving working conditions, offering promotions, or addressing dissatisfaction, to retain valuable employees.
The credit card fraud detection model employs a Random Forest Classifier, a robust ensemble learning technique. It analyzes various transaction features to accurately identify fraudulent activities, leveraging the collective decision-making of multiple decision trees to enhance detection accuracy and resilience against data imbalances.
LyriGenesis is an Introduction to Artificial Intelligence (AI) Final Project. This is an AI model that takes a word or phrase from a user and uses it to generate song lyrics whose length is also based on another input by the user.
Splitting the advertising data (advertising.csv) into training and testing data sets, then choosing and training a classification machine learning algorithm; Getting the accuracy of the ML model; Using feature engineering skills to create new features and improve my ML model;
An Image Classification Model trained on 6000+ images of various plants. When an image is uploaded, it returns whether the plant is poisonous or not. Tri-Valley Hacks 2023 project.