This repository is for DataMining(DM) cource in university of Amirkabir under supervision of Dr.Ehsan Nazerfard
In HW1, Firstly some basic definition was asked, then Aprioriti, FP-growth, K-Means and associate rules were calculated for writing part. In implementation, main idea was using Pandas, Numpy and Matplotlib libraries for 'COVID-19' data set in South Korea.
In first part, K-Means algorithm was used for compressing a picture. Then with elbow method, number of clusters (K) was choosen. In second part of implementation, DBSCAN method was used to cluster 'COVID-19' patients on map using Folium library.
Writing part of the homework 3 contained some questions about Gradient Tree Boosting, Decision Tree and ensemble classifier. In the implemetation part, Decison Tree method was used on a heart dataset to classify and predict heart diseases. In the next part, Weka was used for the mentioned implementation homework part. In the last part, text classification with Naïve Bayes was implemented on a review dataset from amazon.
Final project was selected from kaggle: hotel-booking-demand. Two Methods, Random Forest and Neural Network, were implemented for this project.