Goal: The goal was to learn to use neural networks to make an expert system to support in diagnostic decision-making. The objective was to read and pre-process the 'Heart Disease Health Indicator Dataset' from Kaggle and create and train a dense neural network to predict to classify the presence of heart disease.
Packages:
- pandas
- matplotlib
- sklearn (scikit-learn)
- seaborn
Goal: The goal was to use convolutional neural network to create a binary classifier for x-ray chest images. The aim was to achieve a minimum of 90% of sensitivity and 90% of specificity in classification results.
Packages:
- tensorflow
- sklearn (scikit learn)
- numpy
- matplotlib
Goal: The goal was to use the methods of text processing and to experiment with convolutional neural networks to create a classifier for a collection of patient drug reviews extracted from Drugs.com.
Packages
- pandas
- numpy
- matplotlib
- seaborn
- tensorflow
- sklearn (scikit learn)