***Python in google colab*** Using a decision tree model in SKLearn library Please find/create a CSV file that has at least three columns (features) and 30 rows that can decide if a person has COVID-19 or not. Please use 30% of the dataset as your testing data and show the achieved аccuracy.

Traversal of Discrete Models Instructions 1. Implement the Viterbi algorithm in Python. Your task is to write code for the following prototype: def Viterbi(A,B,Pi,O) Returning Viterbi score and best path 2. Calculate the Viterbi score and the Best path of the first two vectors described in part 1 of this assignment by hand. Show your work. 3. Create an observation vector of at least 7 elements. 4. Use the observation vectors and results calculated in 2. and 3. above to test your implementation of the Viterbi algorithm.

A basic wooden railway set contains the pieces shown in figure below. It identifies the quantityof each type of piece denoted by ‘x’. Each piece is not uniquely identified by a primary keyvalue only labeled by type. i.e., Input information only states quantity of types such as twelvestraight pieces, two branch pieces, two merge pieces, and sixteen curve pieces. Pieces connect the knob to opening, with subtend curves extending the arc from start to end by45 degrees, and the pieces can be flipped over for track direction. The task is to connect these pieces into a railway that has no overlapping tracks and no looseends where a train could run off onto the floor. Question: Suppose that the pieces fit together exactly with no slack. Give a precise formulation of thetask as a search problem. Write a Java algorithm to assemble optimally solving the problem.

unique please Your task for this assignment is to identify a spanning tree in one connected undirected weighted graph using C++. Implement a spanning tree algorithm using C++. A spanning tree is a subset of the edges of a connected undirected weighted graph that connects all the vertices together, without any cycles. The program is interactive. Graph edges with respective weights (i.e., v1 v2 w) are entered at the command line and results are displayed on the console. Each input transaction represents an undirected edge of a connected weighted graph. The edge consists of two unequal non-negative integers in the range 0 to 9 representing graph vertices that the edge connects. Each edge has an assigned weight. The edge weight is a positive integer in the range 1 to 99. The three integers on each input transaction are separated by space. An input transaction containing the string “end-of-file” signals the end of the graph edge input. After the edge information is read, the process…