DATA SCIENCE, python

DATA SCIENCE 1)Download 5 clustering datasets from UCI machine learning repository ([login to view URL] mIns=&type=&sort=nameUp&view=table). a) Run k-means algorithm with different numbers of clusters, including the correct of the crowd. Use Euclidean similarity function. Make diagrams of convergence of the algorithm, and distribution of data in clusters. Compare the latter with the proper distribution of data in clusters, as given by the creators of datasets («groundtruths»). What do you notice as it changes the number of clusters compared with the value of the objective function? How do you explain? b) Repeat the above using Gaussian mixture models and answers to all respectively ruling the question (a). Consider in your analysis as full as and diagonal covariance matrices. What differences in performance between the two setups; 2. Based on the relative demo of lesson1, use the corpus 20newsgroups dataset to train a model of learning topics using nonnegative matrix factorization. As in the demo, recreate every text through tf-idf representation, wherein each document is represented by a combination of relative frequency of each token in a text, and reverse the relative frequency in all the texts of the corpus. a. Try algorithm with at least 5 different number of options latent features. What do you observe regarding the convergence and the computational costs? That you give? b. Try algorithm with at least 5 different number of options inferred topics. What do you observe regarding the convergence and the computational costs? That you give? c. What happens if we increase the number of samples for convergence and computational costs? That you give? 3. Build a system that provides 4. Download the datasets from: https://archive.ics.uci.edu/ml/datasets/UJI+Pen+Characters. Use HMM models which will be trained to these data to make a system that can recognize handwriting. Try HMM models with different many states. What do you notice on the convergence and recognition accuracy, and computational costs? How do you interpret the phenomena based on the theory that you have taught? 1 http://scikitlearn. org/stable/auto_examples/applications/topics_extraction_with_nmf.html#exampleapplications- topics-extraction-with-nmf-py