SPSS to build decision tree, KNN and classification models
Data analysis using SPSS
Data_Analysis - Analyzing Fisher’s iris data to visualize and interpret the relationship between the two sepal variables, sepal length and sepal width.
Data_Analysis_2 - Hospital dataset - Data distribution and normalization.
Data_Analysis_3 - Spotify Dataset.
Decision_tree_Lupus - Classification approach by using decision trees and the Lupus data
Decision_tree_Wine_Data - Effect of the class imbalance of the accuracy of the decision trees.
Decision_tree_Wheat_dataset - Decision tree classification model for the three different varieties of wheat: Kama, Rosa and Canadian. 10-fold cross validation and at least five different configurations to produce a decision tree classifier.
Classification_model_Letter_recognition - Classification model for letter recognition using decision trees as a classification method with a holdout partitioning technique for splitting the data into training versus testing
KNN - K-nearest neighbor classifier for letter recognition to classify the data
Clustering - Perform k-means clustering using all the attributes with the exception of the class label, vary the number of clusters from 3 to 4 to 5 to 6. Hierarchical clustering using all attributes except the class label