Pinned Repositories
Deep-learning-project
Project in which I used convolutional neural network and recurrent neural network to classify images of flowers into 4 classes. Used different types of layers and adjustments to the architectures to try and obtain better and better scores, parsed the images through an image generator and plotted the train loss/accuracy vs test loss/accuracy. Made in python.
F1Stats
InformaationVisualization
IPark
small template for a parking app(no backend added)
Licenta
Python_dash_task
Python dash task
R-team-project
Team project, where we applied different algorithms on a data set to predict credit cards frauds. The dataset was already computed through a PCA algorithm so most of the features where unknown to us (probably as a security measure) except the timestamp and the amount of the transaction. We applied and compared multiple algorithms such as: Naive Bayes, SVM, Boosting, Random forest then Decisional tree, Linear model and a neural network and concluded that the neural network obtained the best results. My contribution as a member was on making and tunning random forest and decisional tree algorithms. Another thing to mention is that the data set was highly unbalanced (number of frauds very small compared to too non-frauds) so we also plotted the ROC-AUC curve too check the results of our algorithms. This project was made in R.
soilprofile_app
StormTheFront
Supervised-Unsupervised-Algorithms
Project in which I used supervised: k nearest neighbor and decisional tree and unsupervised: k-means and DBSCAN. For the supervised part I tried to predict player roles in football based on their stats and achieved around 90% accuracy. For the unsupervised I tried to form clusters of players, again based on their stats, and each cluster to define a main role in a football team. More details of the code on GitHub. Made in python.
AlexLazar1's Repositories
AlexLazar1/Deep-learning-project
Project in which I used convolutional neural network and recurrent neural network to classify images of flowers into 4 classes. Used different types of layers and adjustments to the architectures to try and obtain better and better scores, parsed the images through an image generator and plotted the train loss/accuracy vs test loss/accuracy. Made in python.
AlexLazar1/InformaationVisualization
AlexLazar1/R-team-project
Team project, where we applied different algorithms on a data set to predict credit cards frauds. The dataset was already computed through a PCA algorithm so most of the features where unknown to us (probably as a security measure) except the timestamp and the amount of the transaction. We applied and compared multiple algorithms such as: Naive Bayes, SVM, Boosting, Random forest then Decisional tree, Linear model and a neural network and concluded that the neural network obtained the best results. My contribution as a member was on making and tunning random forest and decisional tree algorithms. Another thing to mention is that the data set was highly unbalanced (number of frauds very small compared to too non-frauds) so we also plotted the ROC-AUC curve too check the results of our algorithms. This project was made in R.
AlexLazar1/StormTheFront
AlexLazar1/Supervised-Unsupervised-Algorithms
Project in which I used supervised: k nearest neighbor and decisional tree and unsupervised: k-means and DBSCAN. For the supervised part I tried to predict player roles in football based on their stats and achieved around 90% accuracy. For the unsupervised I tried to form clusters of players, again based on their stats, and each cluster to define a main role in a football team. More details of the code on GitHub. Made in python.
AlexLazar1/F1Stats
AlexLazar1/IPark
small template for a parking app(no backend added)
AlexLazar1/Licenta
AlexLazar1/Python_dash_task
Python dash task
AlexLazar1/soilprofile_app