My solution to our ungraded assignment using scikit-learn for the "Preclinical and Clinical Data Analysis in Predictive Drug Discovery/Development" course.

The task was building a model and predicting the outcome of an AMES test for at least one of the provided test sets, while evaluating the accuracy of the model.

Features:
- Option to choose dataset for training/testing
- Option to use Gridsearch for models or use fixed parameters
- Confusion matrices and accuracy scores for all algorithms
- Export results for test set for Random Forest as .TXT file

Classifiers:
- Support Vectors Machine
- K Nearest Neighbors
- Random Forest
- Gradient Boosted Decision Trees
- Simple Neural Network (Multilayer Perceptron)