martinsnathalia/machine-learning-exercises

Exercises to train machine learning techniques and algorithms.

Machine Learning Exercises

This repo is about activities for implementing techniques and practicing Machine Learning algorithms.

These activities were given in machine learning classes, and tasks that required the implementation of ML's algorithms on the Information Security Residency program at Federal University of Ceara.

There are 2 folders relative to code and data:

• In the data folder, there are some ".csv" files were used on the code. Some datasets are too large and therefore could not be made available on github.
• In the code folder, there are scripts with proposed tasks and the execution of the code.

Summarization of the files

File	Description	Libraries	Algorithms	Dataset	Metrics
linear-regression.ipynb	Linear regression, polynomial and regularization	Numpy, Matplotlib	Ordinary Least Squares (OLS), Gradient Descent (GD), Stochastic Gradient Descent (SGD)	artificial.csv, california.csv	MSE, RMSE
ensemble-random-forest-gs.ipynb	Use Random Forest and grid search for parameters optimization	Numpy, Scikit-learn, Matplotlib, Warnings	Random Forest	enron_spam_data_prep.csv	Accuracy, recall, precision, f1-score, ROC curve, precision-recall curve
svm-gridserach.ipynb	Use Support Vector Machine (SVM) and grid search for parameters optimization	Numpy, Scikit-learn, Matplotlib, Warnings	Support Vector Machine (SVM)	enron_spam_data_prep.csv	Accuracy, recall, precision, f1-score, ROC curve, precision-recall curve
artificial-neural-network.ipynb	Use validation set to adjust hyperparameters	Numpy, Scikit-learn, Matplotlib, Seaborn, Warnings	Multilayer Perceptron Classifier (MLP)	edge_iiot.csv	Cost function curve, Accuracy, Confusion Matrix
kfold-models-metrics.ipynb	KFold, statistical methods and algorithms	Numpy, Scikit-learn, Warnings	Logistic Regression, Gaussian Discriminant Analysis, Gaussian Naive Bayes, KNN, Decision Tree	jsvulnerability_balanced.csv	Mean value and standard deviation of accuracy, recall, precision and F1-score
kfold-feature-selection.ipynb	Test at least 5 algorithms and feature selection methods (Variance Threshold, SelectKBest, SelectPercentile, RFE) with k-fold	Pandas, Numpy, Warnings, Scikit-learn, Time	Decision Tree, XGBoost, Random Forest, Logistic Regression, Gaussian Naive Bayes, MLP Classifier	iot23_combined.csv	Accuracy, precision, recall, f1-score
deep-learning.ipynb	Classify the presence of malware	Pandas, Sciki-learn, Warnings, Keras	SVM, Logistic Regression, Random Forest, MLP Classifier, Neural Network: Sequential Model	kaggle: Android Malware Dataset for Machine Learning	Accuracy, Precision, Recall, F1-score, Confusion Matrix