Data Analytics and Data Mining are two significant pillars mainly used in Data Science. The main objective of the data science domain is to provide an accurate output to the customer's requirements. It is an extensive process of analyzing the raw data and mining the user-required pattern from a large data set. Recent data mining applications need improvement, so the data mining industries are motivated to enhance the quality of data and the efficiency of data analysis methods. Several earlier research works have been proposed for improving the mining accuracy in various real-time applications. But their outputs are comparatively less due to inefficiency in clustering, classification, and mining. Thus, this paper makes the user choose a better hybrid algorithm, improving the mining efficiency by preprocessing, clustering, and type. It also mainly focused on reducing time and computation complexity by applying dimensionality reduction. The hybrid algorithm is designed by integrating MCA PCA with FCM, RBFN, and MLP. The best method is to compare error, accuracy, and time complexity

Data Mining, Hybrid Algorithm, FCM, MCA, PCA, RBFN, MLP