The goal of this research is to accurately classify Android applications as either malware or legitimate software using machine learning techniques. This accuracy is attained through a well-organized approach for dimensionality reduction utilizing an Autoencoder to transform a high-dimensional feature space to a compact representative feature space. This approach is crucial for reducing dimensionality, especially since the novel NATICUSdroid dataset used for Android malware classification contains a large number of features, including both native and custom permissions. The primary contribution of the research is the design of a Machine Learning Pipeline that prioritizes the most relevant features, ensuring high accuracy with a minimal set of features. Classification is performed using various ensemble classifiers. Predictive ability of the proposed MLP is assessed through various evaluation metrics using a confusion matrix.