Comparison of two feature extraction techniques, Principal Component Analysis (PCA) and Sequential Backward Search Algorithm.
These two different techniques are used to select different number of features from the dataset, the selected features are then used to build a classifier and the classifier performance is compared with an ROC Curve. The classifier used here is the Logistic Regression classifier from Sklearn.
The dataset used in this project is an Occupancy Detection Data set obtained from the UCI Machine Learning Repository (https://archive.ics.uci.edu/ml/datasets/Occupancy+Detection+).