Summary#

Training machine learning models to classify and predict previously unknown data, often used in health care, finance, and image recognition.

Key Strengths#

• Comprehensive exploratory data analysis on datasets including data cleaning, statistical examination, and feature engineering
• Training machine learning models including understanding and calculating loss, interpreting accuracy metrics, and hyperparameter tuning
• Understanding various machine learning models including logistic regression, decision trees, random forest classifiers, naive bayes nets, support vector machines, and neural networks
• Interpreting results given by machine learning models and leveraging libraries like pyplot and seaborn to create visual representations

Related Projects#

• Exploratory Data Analysis on Beers & Breweries Datasets
• Titanic Survival Prediction
• Analyzing Covid-19 Data to Predict Long Covid-19 Cases